/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index 1.22.4 1.4.1 1.22.4 1.4.1 Gene: pncA Drug: pyrazinamide aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 424 PASS: my_features_df and aa_df successfully combined nrows: 424 ncols: 267 count of NULL values before imputation or_mychisq 102 log10_or_mychisq 102 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 Genomic features being used EXCLUDING odds ratio (n): 5 These are: ['maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'] dst column exists and this is identical to drug column: pyrazinamide All feature names: ['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss', 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss', 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss', 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss', 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss', 'volumetric_rr', 'volumetric_mm', 'volumetric_ss', 'ligand_distance', 'ligand_affinity_change', 'mmcsm_lig', 'ALTS910101', 'AZAE970101', 'AZAE970102', 'BASU010101', 'BENS940101', 'BENS940102', 'BENS940103', 'BENS940104', 'BETM990101', 'BLAJ010101', 'BONM030101', 'BONM030102', 'BONM030103', 'BONM030104', 'BONM030105', 'BONM030106', 'BRYS930101', 'CROG050101', 'CSEM940101', 'DAYM780301', 'DAYM780302', 'DOSZ010101', 'DOSZ010102', 'DOSZ010103', 'DOSZ010104', 'FEND850101', 'FITW660101', 'GEOD900101', 'GIAG010101', 'GONG920101', 'GRAR740104', 'HENS920101', 'HENS920102', 'HENS920103', 'HENS920104', 'JOHM930101', 'JOND920103', 'JOND940101', 'KANM000101', 'KAPO950101', 'KESO980101', 'KESO980102', 'KOLA920101', 'KOLA930101', 'KOSJ950100_RSA_SST', 'KOSJ950100_SST', 'KOSJ950110_RSA', 'KOSJ950115', 'LEVJ860101', 'LINK010101', 'LIWA970101', 'LUTR910101', 'LUTR910102', 'LUTR910103', 'LUTR910104', 'LUTR910105', 'LUTR910106', 'LUTR910107', 'LUTR910108', 'LUTR910109', 'MCLA710101', 'MCLA720101', 'MEHP950102', 'MICC010101', 'MIRL960101', 'MIYS850102', 'MIYS850103', 'MIYS930101', 'MIYS960101', 'MIYS960102', 'MIYS960103', 'MIYS990106', 'MIYS990107', 'MIYT790101', 'MOHR870101', 'MOOG990101', 'MUET010101', 'MUET020101', 'MUET020102', 'NAOD960101', 'NGPC000101', 'NIEK910101', 'NIEK910102', 'OGAK980101', 'OVEJ920100_RSA', 'OVEJ920101', 'OVEJ920102', 'OVEJ920103', 'PRLA000101', 'PRLA000102', 'QUIB020101', 'QU_C930101', 'QU_C930102', 'QU_C930103', 'RIER950101', 'RISJ880101', 'RUSR970101', 'RUSR970102', 'RUSR970103', 'SIMK990101', 'SIMK990102', 'SIMK990103', 'SIMK990104', 'SIMK990105', 'SKOJ000101', 'SKOJ000102', 'SKOJ970101', 'TANS760101', 'TANS760102', 'THOP960101', 'TOBD000101', 'TOBD000102', 'TUDE900101', 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique', 'dst', 'dst_mode'] PASS: but NOT writing mask file PASS: But NOT writing processed file ################################################################# SUCCESS: Extacted training data for gene: pnca Dim of training_df: (424, 173) This EXCLUDES Odds Ratio ############################################################ Input params: Dim of input df: (424, 173) Data type to split: actual Split type: 70_30 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 173 No.of cols dropped: 2 No. of columns for x_features: 171 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: 70_30 Total no. of input features: 171 --------No. of numerical features: 165 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 69 Train data size: (46, 171) y_train numbers: Counter({0: 23, 1: 23}) Test data size: (23, 171) y_test_numbers: Counter({0: 12, 1: 11}) y_train ratio: 1.0 y_test ratio: 1.0909090909090908 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 23, 1: 23}) (46, 171) Simple Random UnderSampling Counter({0: 23, 1: 23}) (46, 171) Simple Combined Over and UnderSampling Counter({0: 23, 1: 23}) (46, 171) SMOTE_NC OverSampling Counter({0: 23, 1: 23}) (46, 171) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (46, 171) y_train numbers: 46 y_train ratio: 1.0 y_test ratio: 1.0909090909090908 ================================ Resampling: Random underampling ================================ Train data size: (46, 171) y_train numbers: 46 y_train ratio: 1.0 y_test ratio: 1.0909090909090908 ================================ Resampling:Combined (over+under) ================================ Train data size: (46, 171) y_train numbers: 46 y_train ratio: 1.0 y_test ratio: 1.0909090909090908 ============================== Resampling: Smote NC ============================== Train data size: (46, 171) y_train numbers: 46 y_train ratio: 1.0 y_test ratio: 1.0909090909090908 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.06983209 0.06812596 0.07007098 0.07098293 0.07116628 0.06777048 0.06843758 0.07148743 0.0691824 0.06956434] mean value: 0.06966204643249511 key: score_time value: [0.01473045 0.01459455 0.01476622 0.0151453 0.01529551 0.01501822 0.01511931 0.01507354 0.01471853 0.01428246] mean value: 0.014874410629272462 key: test_mcc value: [-0.16666667 -0.16666667 0. 0.16666667 0.16666667 0. 0.57735027 1. 0. 0.57735027] mean value: 0.21547005383792514 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.4 0.57142857 0.66666667 0.66666667 0. 0.66666667 1. 0.66666667 0.8 ] mean value: 0.5838095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.33333333 0.4 0.66666667 0.66666667 0. 1. 1. 0.5 0.66666667] mean value: 0.5566666666666668 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.4 0.6 0.6 0.4 0.75 1. 0.5 0.75] mean value: 0.58 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.41666667 0.41666667 0.5 0.58333333 0.58333333 0.5 0.75 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.25 0.4 0.5 0.5 0. 0.5 1. 0.5 0.66666667] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... 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Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished MCC on Blind test: 0.03 MCC on Training: 0.22 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.32639337 0.10014367 0.11328292 0.10580373 0.12144279 0.12823105 0.10124493 0.11474466 0.11551142 0.12595916] mean value: 0.1352757692337036 key: score_time value: [0.04496288 0.06633735 0.07299137 0.04744363 0.07813525 0.05086946 0.07549739 0.06788731 0.05281425 0.06873488] mean value: 0.0625673770904541 key: test_mcc value: [ 0.16666667 -0.16666667 0. -0.66666667 1. 0.40824829 1. 0.57735027 0.57735027 0.57735027] mean value: 0.3473632431366074 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.4 0.57142857 0.33333333 1. 0.5 1. 0.66666667 0.8 0.8 ] mean value: 0.6571428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.6900000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 1. 0.33333333 1. 0.5 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.4 0.2 1. 0.6 1. 0.75 0.75 0.75] mean value: 0.645 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.41666667 0.5 0.16666667 1. 0.66666667 1. 0.75 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.25 0.4 0.2 1. 0.33333333 1. 0.5 0.66666667 0.66666667] mean value: 0.535 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.35 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0088768 0.00883436 0.00876451 0.00884509 0.00882053 0.00887585 0.00983834 0.00893235 0.00874305 0.00888968] mean value: 0.008942055702209472 key: score_time value: [0.00824308 0.00837541 0.00823259 0.0084095 0.00818419 0.00830317 0.00822592 0.00840378 0.00874448 0.0081768 ] mean value: 0.0083298921585083 key: test_mcc value: [ 0.61237244 0.61237244 0. -0.66666667 -0.40824829 0.40824829 0. 0. 0.57735027 0.57735027] mean value: 0.17127787431041744 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.33333333 0.57142857 0.5 0.5 0.5 0.8 0.8 ] mean value: 0.590952380952381 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.33333333 0.5 1. 0.5 0.5 0.66666667 0.66666667] mean value: 0.6566666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.2 0.4 0.6 0.5 0.5 0.75 0.75] mean value: 0.5700000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.16666667 0.33333333 0.66666667 0.5 0.5 0.75 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.2 0.4 0.33333333 0.33333333 0.33333333 0.66666667 0.66666667] mean value: 0.4333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.17 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01081538 0.00830555 0.00815225 0.00809002 0.0081706 0.00806022 0.00830436 0.00815725 0.00827122 0.00802994] mean value: 0.008435678482055665 key: score_time value: [0.01007223 0.00833297 0.00843024 0.00829339 0.00832415 0.00842452 0.00829792 0.00854874 0.00856853 0.00827622] mean value: 0.008556890487670898 key: test_mcc value: [ 0.61237244 -0.40824829 0.40824829 -0.40824829 0.61237244 0.66666667 1. 0.57735027 0. 0. ] mean value: 0.30605135167840186 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0. 0.66666667 0.57142857 0.85714286 0.8 1. 0.8 0.5 0.5 ] mean value: 0.6361904761904762 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0. 0.5 0.5 0.75 1. 1. 0.66666667 0.5 0.5 ] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.66666667 1. 1. 0.5 0.5 ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.6 0.4 0.8 0.8 1. 0.75 0.5 0.5 ] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.33333333 0.66666667 0.33333333 0.75 0.83333333 1. 0.75 0.5 0.5 ] mean value: 0.6416666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0. 0.5 0.4 0.75 0.66666667 1. 0.66666667 0.33333333 0.33333333] mean value: 0.5149999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.31 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.0793376 0.08150387 0.08112669 0.07989144 0.07789445 0.0793705 0.08446932 0.08300233 0.08086729 0.08104253] mean value: 0.08085060119628906 key: score_time value: [0.01849914 0.01847696 0.0182848 0.0166471 0.01796627 0.01817036 0.01809716 0.01710749 0.01743054 0.01830173] mean value: 0.017898154258728028 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 0.61237244 0.40824829 0.57735027 0.57735027 0.57735027 -0.57735027] mean value: 0.19337396407417132 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 0.85714286 0.5 0.66666667 0.66666667 0.8 0.4 ] mean value: 0.5761904761904761 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 0.75 1. 1. 1. 0.66666667 0.33333333] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 0.5 1. 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 0.8 0.6 0.75 0.75 0.75 0.25] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 0.75 0.66666667 0.75 0.75 0.75 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 0.75 0.33333333 0.5 0.5 0.66666667 0.25 ] mean value: 0.43999999999999995 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.15 MCC on Training: 0.19 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.12388134 0.11589622 0.10810065 0.12355804 0.11949849 0.10448313 0.11970115 0.12029409 0.1248076 0.11048841] mean value: 0.11707091331481934 key: score_time value: [0.0086503 0.00907326 0.00914669 0.0087409 0.00889707 0.0087142 0.00969577 0.00923276 0.00898218 0.008883 ] mean value: 0.009001612663269043 key: test_mcc value: [0.61237244 0.61237244 0. 0.16666667 0.16666667 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.36983773027576633 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.66666667 0.66666667 0.5 0.66666667 0.5 0.8 0.8 ] mean value: 0.6504761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.66666667 0.66666667 1. 1. 0.5 0.66666667 0.66666667] mean value: 0.7566666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.6 0.6 0.6 0.75 0.5 0.75 0.75] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.58333333 0.58333333 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.5 0.5 0.33333333 0.5 0.33333333 0.66666667 0.66666667] mean value: 0.49000000000000005 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.48 MCC on Training: 0.37 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00929666 0.00943708 0.00833559 0.0092361 0.00886321 0.0086658 0.00868893 0.00899005 0.00896668 0.00865817] mean value: 0.008913826942443848 key: score_time value: [0.00976324 0.00939989 0.00918174 0.00877643 0.00878811 0.00839972 0.00925398 0.00932312 0.00906777 0.00832415] mean value: 0.009027814865112305 key: test_mcc value: [-0.16666667 0. 0. 0.61237244 0. 0.40824829 1. 1. 0. 0.57735027] mean value: 0.34313043286826167 key: train_mcc value: [0.57570364 0.698212 0.63496528 0.49692935 0.59982886 0.63994524 0.56652882 0.52704628 0.54659439 0.60609153] mean value: 0.5891845382894122 key: test_fscore value: [0.4 0.57142857 0.57142857 0.85714286 0.75 0.5 1. 1. 0.66666667 0.8 ] mean value: 0.7116666666666667 key: train_fscore value: [0.80851064 0.85714286 0.83333333 0.76595745 0.80851064 0.82608696 0.8 0.78431373 0.79166667 0.81632653] mean value: 0.8091848793171292 key: test_precision value: [0.33333333 0.4 0.4 0.75 0.6 1. 1. 1. 0.5 0.66666667] mean value: 0.665 key: train_precision value: [0.73076923 0.75 0.74074074 0.66666667 0.7037037 0.73076923 0.68965517 0.66666667 0.7037037 0.71428571] mean value: 0.7096960829719451 key: test_recall value: [0.5 1. 1. 1. 1. 0.33333333 1. 1. 1. 1. ] mean value: 0.8833333333333332 key: train_recall value: [0.9047619 1. 0.95238095 0.9 0.95 0.95 0.95238095 0.95238095 0.9047619 0.95238095] mean value: 0.9419047619047619 key: test_accuracy value: [0.4 0.4 0.4 0.8 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.645 key: train_accuracy value: [0.7804878 0.82926829 0.80487805 0.73170732 0.7804878 0.80487805 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779326364692218 key: test_roc_auc value: [0.41666667 0.5 0.5 0.75 0.5 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [0.77738095 0.825 0.80119048 0.73571429 0.78452381 0.80833333 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779761904761904 key: test_jcc value: [0.25 0.4 0.4 0.75 0.6 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.5900000000000001 key: train_jcc value: [0.67857143 0.75 0.71428571 0.62068966 0.67857143 0.7037037 0.66666667 0.64516129 0.65517241 0.68965517] mean value: 0.6802477473500833 MCC on Blind test: -0.03 MCC on Training: 0.34 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01152015 0.01112819 0.0104053 0.01001024 0.00959754 0.00985265 0.00978637 0.01080632 0.01076031 0.00988483] mean value: 0.010375189781188964 key: score_time value: [0.0099802 0.00937295 0.00921988 0.00906038 0.00855017 0.00891304 0.00887537 0.00913906 0.00896144 0.00853586] mean value: 0.009060835838317871 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 1. 0.40824829 0.57735027 1. -0.57735027 -0.57735027] mean value: 0.15893163974770408 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 1. 0.5 0.66666667 1. 0.4 0.4 ] mean value: 0.5838095238095239 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 1. 1. 1. 1. 0.33333333 0.33333333] mean value: 0.6333333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 1. 0.6 0.75 1. 0.25 0.25] mean value: 0.585 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 1. 0.66666667 0.75 1. 0.25 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 1. 0.33333333 0.5 1. 0.25 0.25 ] mean value: 0.4733333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.16 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00849676 0.0109396 0.00833678 0.0086844 0.00781727 0.00782561 0.00776601 0.00888157 0.00875449 0.00902677] mean value: 0.008652925491333008 key: score_time value: [0.04690957 0.03601575 0.00927186 0.00889158 0.00916505 0.00916147 0.00889063 0.00961494 0.00972152 0.009655 ] mean value: 0.015729737281799317 key: test_mcc value: [ 0.16666667 -0.16666667 0.40824829 -0.40824829 0.16666667 -0.61237244 0. 0.57735027 0. 0.57735027] mean value: 0.07089947693501238 key: train_mcc value: [0.36718832 0.56527676 0.56527676 0.56086079 0.56086079 0.52420964 0.43656413 0.43052839 0.4472136 0.47673129] mean value: 0.49347104597079217 key: test_fscore value: [0.5 0.4 0.66666667 0.57142857 0.66666667 0. 0.5 0.8 0.66666667 0.8 ] mean value: 0.5571428571428572 key: train_fscore value: [0.71111111 0.8 0.8 0.76923077 0.76923077 0.77272727 0.73913043 0.72727273 0.75 0.74418605] mean value: 0.7582889130866886 key: test_precision value: [0.5 0.33333333 0.5 0.5 0.66666667 0. 0.5 0.66666667 0.5 0.66666667] mean value: 0.4833333333333333 key: train_precision value: [0.66666667 0.75 0.75 0.78947368 0.78947368 0.70833333 0.68 0.69565217 0.66666667 0.72727273] mean value: 0.722353893627349 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [0.76190476 0.85714286 0.85714286 0.75 0.75 0.85 0.80952381 0.76190476 0.85714286 0.76190476] mean value: 0.8016666666666665 key: test_accuracy value: [0.6 0.4 0.6 0.4 0.6 0.2 0.5 0.75 0.5 0.75] mean value: 0.53 key: train_accuracy value: [0.68292683 0.7804878 0.7804878 0.7804878 0.7804878 0.75609756 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7441927990708479 key: test_roc_auc value: [0.58333333 0.41666667 0.66666667 0.33333333 0.58333333 0.25 0.5 0.75 0.5 0.75 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.68095238 0.77857143 0.77857143 0.7797619 0.7797619 0.75833333 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7436904761904762 key: test_jcc value: [0.33333333 0.25 0.5 0.4 0.5 0. 0.33333333 0.66666667 0.5 0.66666667] mean value: 0.41500000000000004 key: train_jcc value: [0.55172414 0.66666667 0.66666667 0.625 0.625 0.62962963 0.5862069 0.57142857 0.6 0.59259259] mean value: 0.6114915161466885 MCC on Blind test: 0.21 MCC on Training: 0.07 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01105928 0.01321888 0.01351905 0.01347065 0.01348019 0.01346397 0.01366043 0.01355052 0.01382542 0.01353216] mean value: 0.013278055191040038 key: score_time value: [0.01112556 0.01103234 0.01141381 0.01149201 0.01138902 0.01138401 0.01141477 0.01133609 0.01137686 0.0114007 ] mean value: 0.011336517333984376 key: test_mcc value: [ 0.16666667 -0.16666667 -0.61237244 -0.40824829 0.40824829 -0.16666667 -0.57735027 1. 1. 0.57735027] mean value: 0.12209608976375388 key: train_mcc value: [0.8547619 0.75714286 0.95227002 0.90238095 0.90238095 0.85441771 0.9047619 0.9047619 0.76277007 0.9047619 ] mean value: 0.8700410175391831 key: test_fscore value: [0.5 0.4 0.33333333 0.57142857 0.5 0.4 0.4 1. 1. 0.66666667] mean value: 0.5771428571428572 key: train_fscore value: [0.92682927 0.87804878 0.97674419 0.95 0.95 0.92307692 0.95238095 0.95238095 0.87804878 0.95238095] mean value: 0.9339890795534584 key: test_precision value: [0.5 0.33333333 0.25 0.5 1. 0.5 0.33333333 1. 1. 1. ] mean value: 0.6416666666666666 key: train_precision value: [0.95 0.9 0.95454545 0.95 0.95 0.94736842 0.95238095 0.95238095 0.9 0.95238095] mean value: 0.9409056732740944 key: test_recall value: [0.5 0.5 0.5 0.66666667 0.33333333 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.5833333333333333 key: train_recall value: [0.9047619 0.85714286 1. 0.95 0.95 0.9 0.95238095 0.95238095 0.85714286 0.95238095] mean value: 0.9276190476190477 key: test_accuracy value: [0.6 0.4 0.2 0.4 0.6 0.4 0.25 1. 1. 0.75] mean value: 0.5599999999999999 key: train_accuracy value: [0.92682927 0.87804878 0.97560976 0.95121951 0.95121951 0.92682927 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347851335656214 key: test_roc_auc value: [0.58333333 0.41666667 0.25 0.33333333 0.66666667 0.41666667 0.25 1. 1. 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92738095 0.87857143 0.975 0.95119048 0.95119048 0.92619048 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347619047619047 key: test_jcc value: [0.33333333 0.25 0.2 0.4 0.33333333 0.25 0.25 1. 1. 0.5 ] mean value: 0.45166666666666666 key: train_jcc value: [0.86363636 0.7826087 0.95454545 0.9047619 0.9047619 0.85714286 0.90909091 0.90909091 0.7826087 0.90909091] mean value: 0.8777338603425558 MCC on Blind test: -0.3 MCC on Training: 0.12 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02238131 0.01721191 0.01313329 0.01515412 0.01499963 0.01554823 0.01335239 0.01455402 0.01436663 0.01606369] mean value: 0.01567652225494385 key: score_time value: [0.01137257 0.00866985 0.00838876 0.00821638 0.00824046 0.00816345 0.00818801 0.00819993 0.00820422 0.00858164] mean value: 0.008622527122497559 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.61237244 -0.61237244 1. 1. 0. 1. ] mean value: 0.09587585476806848 key: train_mcc value: [0.85441771 0.95238095 1. 0.90238095 0.90238095 0.90649828 0.85811633 0.85811633 0.85811633 0.9047619 ] mean value: 0.8997169740060625 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5595238095238095 key: train_fscore value: [0.93023256 0.97560976 1. 0.95 0.95 0.94736842 0.92682927 0.92682927 0.92682927 0.95238095] mean value: 0.9486079492548729 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) key: test_precision value: [0. 0.25 0.25 0.5 0.75 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.90909091 1. 1. 0.95 0.95 1. 0.95 0.95 0.95 0.95238095] mean value: 0.9611471861471861 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [0.95238095 0.95238095 1. 0.95 0.95 0.9 0.9047619 0.9047619 0.9047619 0.95238095] mean value: 0.9371428571428572 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.8 0.2 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.92682927 0.97560976 1. 0.95121951 0.95121951 0.95121951 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9494192799070849 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.75 0.25 1. 1. 0.5 1. ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92619048 0.97619048 1. 0.95119048 0.95119048 0.95 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9492857142857142 key: test_jcc value: [0. 0.2 0.2 0.4 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.4883333333333333 key: train_jcc value: [0.86956522 0.95238095 1. 0.9047619 0.9047619 0.9 0.86363636 0.86363636 0.86363636 0.90909091] mean value: 0.9031469979296066 MCC on Blind test: 0.03 MCC on Training: 0.1 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.1546545 0.16696286 0.14823389 0.16742659 0.17512679 0.15866518 0.17716575 0.17654037 0.17484379 0.18263817] mean value: 0.1682257890701294 key: score_time value: [0.00973511 0.00954175 0.00969124 0.00897574 0.00950289 0.00987792 0.00941896 0.00863886 0.00958753 0.00968432] mean value: 0.009465432167053223 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 0. ] mean value: 0.012542521434735132 key: train_mcc value: [0.41487884 1. 0.7098505 1. 1. 0.95227002 0.85811633 1. 1. 1. ] mean value: 0.8935115692085429 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4904761904761905 key: train_fscore value: [0.72727273 1. 0.86363636 1. 1. 0.97435897 0.92682927 1. 1. 1. ] mean value: 0.9492097333560748 key: test_precision value: [0. 0.25 0.25 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4666666666666666 key: train_precision value: [0.69565217 1. 0.82608696 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.9471739130434782 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.5333333333333333 key: train_recall value: [0.76190476 1. 0.9047619 1. 1. 0.95 0.9047619 1. 1. 1. ] mean value: 0.9521428571428571 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.6 0.4 1. 1. 0.5 0.5] mean value: 0.52 key: train_accuracy value: [0.70731707 1. 0.85365854 1. 1. 0.97560976 0.92857143 1. 1. 1. ] mean value: 0.9465156794425088 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 0.5 ] mean value: 0.525 key: train_roc_auc value: [0.70595238 1. 0.85238095 1. 1. 0.975 0.92857143 1. 1. 1. ] mean value: 0.9461904761904762 key: test_jcc value: [0. 0.2 0.2 0.4 0.5 0. 1. 1. 0.33333333 0.33333333] mean value: 0.39666666666666667 key: train_jcc value: [0.57142857 1. 0.76 1. 1. 0.95 0.86363636 1. 1. 1. ] mean value: 0.9145064935064935 MCC on Blind test: -0.05 MCC on Training: 0.01 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.23822618 0.23336077 0.21709681 0.24177051 0.24478054 0.24459743 0.28664613 0.40778923 0.24076605 0.26968765] mean value: 0.262472128868103 key: score_time value: [0.01223922 0.01181555 0.01198864 0.01188159 0.01193166 0.01195812 0.01216125 0.01203704 0.01191735 0.01255322] mean value: 0.01204836368560791 key: test_mcc value: [ 0. -0.61237244 -0.16666667 -0.40824829 0.16666667 0.40824829 1. 1. 0. 0.57735027] mean value: 0.19649778334938311 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.4 0.57142857 0.66666667 0.5 1. 1. 0.5 0.8 ] mean value: 0.5771428571428572 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.25 0.33333333 0.5 0.66666667 1. 1. 1. 0.5 0.66666667] mean value: 0.5916666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0.33333333 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.4 0.4 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.6050000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.25 0.41666667 0.33333333 0.58333333 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0. 0.2 0.25 0.4 0.5 0.33333333 1. 1. 0.33333333 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.2 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01147342 0.01119113 0.00825357 0.00846148 0.00789928 0.00796032 0.00934005 0.00869226 0.00836444 0.00792694] mean value: 0.008956289291381836 key: score_time value: [0.01143026 0.01177001 0.0087657 0.00846839 0.00825453 0.00837731 0.00894117 0.00849533 0.00824475 0.00833869] mean value: 0.009108614921569825 key: test_mcc value: [-0.40824829 0. -0.61237244 -0.40824829 0.16666667 0. 1. 0.57735027 0.57735027 0. ] mean value: 0.08924981884223976 key: train_mcc value: [0.36515617 0.31666667 0.46623254 0.46300848 0.46300848 0.41428571 0.43052839 0.38138504 0.42857143 0.43052839] mean value: 0.415937128657245 key: test_fscore value: [0. 0.57142857 0.33333333 0.57142857 0.66666667 0. 1. 0.8 0.8 0.5 ] mean value: 0.5242857142857142 key: train_fscore value: [0.69767442 0.66666667 0.75555556 0.71794872 0.71794872 0.7 0.72727273 0.69767442 0.71428571 0.72727273] mean value: 0.712229966416013 key: test_precision value: [0. 0.4 0.25 0.5 0.66666667 0. 1. 0.66666667 0.66666667 0.5 ] mean value: 0.46499999999999997 key: train_precision value: [0.68181818 0.66666667 0.70833333 0.73684211 0.73684211 0.7 0.69565217 0.68181818 0.71428571 0.69565217] mean value: 0.701791063627448 key: test_recall value: [0. 1. 0.5 0.66666667 0.66666667 0. 1. 1. 1. 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [0.71428571 0.66666667 0.80952381 0.7 0.7 0.7 0.76190476 0.71428571 0.71428571 0.76190476] mean value: 0.7242857142857143 key: test_accuracy value: [0.4 0.4 0.2 0.4 0.6 0.4 1. 0.75 0.75 0.5 ] mean value: 0.54 key: train_accuracy value: [0.68292683 0.65853659 0.73170732 0.73170732 0.73170732 0.70731707 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7077235772357724 key: test_roc_auc value: [0.33333333 0.5 0.25 0.33333333 0.58333333 0.5 1. 0.75 0.75 0.5 ] mean value: 0.55 key: train_roc_auc value: [0.68214286 0.65833333 0.7297619 0.73095238 0.73095238 0.70714286 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7072619047619048 key: test_jcc value: [0. 0.4 0.2 0.4 0.5 0. 1. 0.66666667 0.66666667 0.33333333] mean value: 0.41666666666666663 key: train_jcc value: [0.53571429 0.5 0.60714286 0.56 0.56 0.53846154 0.57142857 0.53571429 0.55555556 0.57142857] mean value: 0.5535445665445665 MCC on Blind test: 0.23 MCC on Training: 0.09 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00830674 0.00859237 0.008219 0.00823474 0.00812411 0.00882483 0.00810313 0.00837708 0.00851035 0.0091424 ] mean value: 0.008443474769592285 key: score_time value: [0.0082345 0.00846815 0.00852323 0.00832844 0.00868106 0.00884366 0.00846958 0.00844622 0.00902724 0.00921249] mean value: 0.008623456954956055 key: test_mcc value: [ 0.61237244 -0.16666667 -0.61237244 1. -0.61237244 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.2361259995670279 key: train_mcc value: [0.65952381 0.78072006 0.78072006 0.698212 0.65915306 0.81975606 0.67357531 0.78446454 0.81322028 0.78446454] mean value: 0.7453809730969173 key: test_fscore value: [0.66666667 0.4 0.33333333 1. 0. 0.5 0.66666667 0. 0.8 0.66666667] mean value: 0.5033333333333333 key: train_fscore value: [0.82926829 0.86486486 0.86486486 0.78787879 0.75 0.88888889 0.82051282 0.86486486 0.9 0.86486486] mean value: 0.8436008249422884 key: test_precision value: [1. 0.33333333 0.25 1. 0. 1. 1. 0. 0.66666667 1. ] mean value: 0.625 key: train_precision value: [0.85 1. 1. 1. 1. 1. 0.88888889 1. 0.94736842 1. ] mean value: 0.9686257309941521 key: test_recall value: [0.5 0.5 0.5 1. 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.76190476 0.76190476 0.65 0.6 0.8 0.76190476 0.76190476 0.85714286 0.76190476] mean value: 0.7526190476190475 key: test_accuracy value: [0.8 0.4 0.2 1. 0.2 0.6 0.75 0.5 0.75 0.75] mean value: 0.595 key: train_accuracy value: [0.82926829 0.87804878 0.87804878 0.82926829 0.80487805 0.90243902 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8621951219512196 key: test_roc_auc value: [0.75 0.41666667 0.25 1. 0.25 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6083333333333333 key: train_roc_auc value: [0.8297619 0.88095238 0.88095238 0.825 0.8 0.9 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8616666666666667 key: test_jcc value: [0.5 0.25 0.2 1. 0. 0.33333333 0.5 0. 0.66666667 0.5 ] mean value: 0.39499999999999996 key: train_jcc value: [0.70833333 0.76190476 0.76190476 0.65 0.6 0.8 0.69565217 0.76190476 0.81818182 0.76190476] mean value: 0.7319786373047242 MCC on Blind test: -0.07 MCC on Training: 0.24 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00886369 0.00908661 0.00913072 0.00901675 0.0099175 0.00954652 0.0095377 0.00934553 0.00916696 0.00928688] mean value: 0.009289884567260742 key: score_time value: [0.00935364 0.00876117 0.00870132 0.00849605 0.00885177 0.00891066 0.00928736 0.00865459 0.00920367 0.0093751 ] mean value: 0.008959531784057617 key: test_mcc value: [-0.66666667 -0.16666667 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 1. ] mean value: 0.13127126071736755 key: train_mcc value: [0.77831178 0.90692382 1. 0.74124932 0.95238095 0.95227002 0.80952381 0.81322028 0.8660254 0.78446454] mean value: 0.860436992906499 key: test_fscore value: [0. 0.4 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5471428571428572 key: train_fscore value: [0.89361702 0.95 1. 0.86956522 0.97560976 0.97435897 0.9047619 0.90909091 0.92307692 0.86486486] mean value: 0.9264945570919038 key: test_precision value: [0. 0.33333333 0.25 0.5 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.80769231 1. 1. 0.76923077 0.95238095 1. 0.9047619 0.86956522 1. 1. ] mean value: 0.9303631151457239 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 0.9047619 1. 1. 1. 0.95 0.9047619 0.95238095 0.85714286 0.76190476] mean value: 0.9330952380952382 key: test_accuracy value: [0.2 0.4 0.2 0.4 0.6 0.4 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.87804878 0.95121951 1. 0.85365854 0.97560976 0.97560976 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9253193960511034 key: test_roc_auc value: [0.16666667 0.41666667 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 1. ] mean value: 0.575 key: train_roc_auc value: [0.875 0.95238095 1. 0.85714286 0.97619048 0.975 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9254761904761905 key: test_jcc value: [0. 0.25 0.2 0.4 0.5 0. 1. 1. 0.33333333 1. ] mean value: 0.4683333333333334 key: train_jcc value: [0.80769231 0.9047619 1. 0.76923077 0.95238095 0.95 0.82608696 0.83333333 0.85714286 0.76190476] mean value: 0.8662533842968625 MCC on Blind test: 0.05 MCC on Training: 0.13 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01168633 0.00837159 0.00850081 0.00848937 0.00842285 0.00870395 0.00865269 0.00870728 0.00851512 0.00836968] mean value: 0.008841967582702637 key: score_time value: [0.00938773 0.00887704 0.00931931 0.0093143 0.0092597 0.00886726 0.00860405 0.00841451 0.00835061 0.00839901] mean value: 0.008879351615905761 key: test_mcc value: [-0.40824829 -0.40824829 0. 0.16666667 0.66666667 0.40824829 -0.57735027 1. -0.57735027 0. ] mean value: 0.027038450449021856 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0.66666667 0.8 0.5 0.4 1. 0.4 0.5 ] mean value: 0.42666666666666664 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0.66666667 1. 1. 0.33333333 1. 0.33333333 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0.66666667 0.66666667 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.41666666666666663 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.6 0.6 0.8 0.6 0.25 1. 0.25 0.5 ] mean value: 0.54 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.33333333 0.5 0.58333333 0.83333333 0.66666667 0.25 1. 0.25 0.5 ] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0.5 0.66666667 0.33333333 0.25 1. 0.25 0.33333333] mean value: 0.33333333333333337 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.03 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.53580642 0.54012418 0.60651898 0.56697035 0.57534242 0.54496598 0.5803318 0.51704288 0.56650019 0.60939026] mean value: 0.5642993450164795 key: score_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.19202542 0.17487597 0.19566822 0.13752317 0.14859271 0.129076 0.16214061 0.15445709 0.12103462 0.14640665] mean value: 0.1561800479888916 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 1. 0.40824829 0.57735027 1. 0.57735027 -0.57735027] mean value: 0.21522652263201553 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 1. 0.5 0.66666667 1. 0.8 0.4 ] mean value: 0.6104761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.5 0.5 1. 1. 1. 1. 0.66666667 0.33333333] mean value: 0.65 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0. 0.6 0.4 1. 0.6 0.75 1. 0.75 0.25] mean value: 0.595 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 1. 0.66666667 0.75 1. 0.75 0.25 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.5 0.4 1. 0.33333333 0.5 1. 0.66666667 0.25 ] mean value: 0.49833333333333335 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.22 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.88683033 0.87128091 0.87492061 0.83152342 0.83610821 0.80956531 0.83383894 0.83388352 0.84564376 0.88258576] mean value: 0.8506180763244628 key: score_time value: [0.14966321 0.21105886 0.13897252 0.21106958 0.20700097 0.31694913 0.18808746 0.20293784 0.2083509 0.14931774] mean value: 0.1983408212661743 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 0.61237244 0.40824829 1. 1. 0.57735027 0. ] mean value: 0.276463766201595 key: train_mcc value: [0.7565654 0.90692382 0.8047619 0.90238095 0.8547619 0.85441771 0.9047619 0.80952381 0.81322028 0.80952381] mean value: 0.841684150259194 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 0.85714286 0.5 1. 1. 0.8 0.5 ] mean value: 0.6395238095238095 key: train_fscore value: [0.88372093 0.95 0.9047619 0.95 0.92682927 0.92307692 0.95238095 0.9047619 0.9 0.9047619 ] mean value: 0.9200293788268832 key: test_precision value: [0.5 0. 0.5 0.5 0.75 1. 1. 1. 0.66666667 0.5 ] mean value: 0.6416666666666667 key: train_precision value: [0.86363636 1. 0.9047619 0.95 0.9047619 0.94736842 0.95238095 0.9047619 0.94736842 0.9047619 ] mean value: 0.9279801777170199 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 1. 1. 1. 0.5 ] mean value: 0.7 key: train_recall value: [0.9047619 0.9047619 0.9047619 0.95 0.95 0.9 0.95238095 0.9047619 0.85714286 0.9047619 ] mean value: 0.9133333333333334 key: test_accuracy value: [0.6 0. 0.6 0.4 0.8 0.6 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_accuracy value: [0.87804878 0.95121951 0.90243902 0.95121951 0.92682927 0.92682927 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203252032520325 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 0.75 0.66666667 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_roc_auc value: [0.87738095 0.95238095 0.90238095 0.95119048 0.92738095 0.92619048 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203571428571428 key: test_jcc value: [0.33333333 0. 0.5 0.4 0.75 0.33333333 1. 1. 0.66666667 0.33333333] mean value: 0.5316666666666666 key: train_jcc value: [0.79166667 0.9047619 0.82608696 0.9047619 0.86363636 0.85714286 0.90909091 0.82608696 0.81818182 0.82608696] mean value: 0.8527503293807641 MCC on Blind test: -0.13 MCC on Training: 0.28 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01023483 0.00901461 0.00901508 0.00974703 0.00945687 0.00973272 0.00944853 0.00979948 0.0098896 0.00956798] mean value: 0.009590673446655273 key: score_time value: [0.00928092 0.00862145 0.00870895 0.00905776 0.00836253 0.00883198 0.00930738 0.0089705 0.00901508 0.00875497] mean value: 0.008891153335571288 key: test_mcc value: [-0.40824829 -0.16666667 -0.61237244 -0.66666667 0.61237244 0. 1. 1. 0. 1. ] mean value: 0.17584183762028038 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5423809523809524 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 0. 1. 1. 0.5 1. ] mean value: 0.5166666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.2 0.2 0.8 0.4 1. 1. 0.5 1. ] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.41666667 0.25 0.16666667 0.75 0.5 1. 1. 0.5 1. ] mean value: 0.5916666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.47333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.31 MCC on Training: 0.18 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.02553225 0.02714777 0.02722263 0.02663302 0.0281353 0.02692461 0.0268929 0.02496362 0.02815104 0.02551985] mean value: 0.02671229839324951 key: score_time value: [0.00938988 0.00863791 0.00905633 0.0095377 0.00930858 0.0093832 0.00873446 0.00859666 0.00925875 0.00935149] mean value: 0.009125494956970214 key: test_mcc value: [ 0. -0.16666667 -0.61237244 -0.66666667 0.61237244 0.40824829 1. 1. 0. 0.57735027] mean value: 0.21522652263201558 key: train_mcc value: [1. 1. 0.8547619 1. 1. 1. 0.85811633 1. 1. 1. ] mean value: 0.9712878235082938 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0.5 1. 1. 0.5 0.66666667] mean value: 0.5590476190476191 key: train_fscore value: [1. 1. 0.92682927 1. 1. 1. 0.92682927 1. 1. 1. ] mean value: 0.9853658536585366 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 1. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_precision value: [1. 1. 0.95 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.99 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0.33333333 1. 1. 0.5 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 0.9047619 1. 1. 1. 0.9047619 1. 1. 1. ] mean value: 0.980952380952381 key: test_accuracy value: [0.6 0.4 0.2 0.2 0.8 0.6 1. 1. 0.5 0.75] mean value: 0.605 key: train_accuracy value: [1. 1. 0.92682927 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855400696864113 key: test_roc_auc value: [0.5 0.41666667 0.25 0.16666667 0.75 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 0.92738095 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855952380952381 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0.33333333 1. 1. 0.33333333 0.5 ] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 0.86363636 1. 1. 1. 0.86363636 1. 1. 1. ] mean value: 0.9727272727272727 MCC on Blind test: -0.31 MCC on Training: 0.22 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01111126 0.00935316 0.009269 0.00922489 0.00926185 0.00934291 0.01016712 0.00949955 0.00930762 0.00868011] mean value: 0.009521746635437011 key: score_time value: [0.00926232 0.00929666 0.0089283 0.00931835 0.00908446 0.00929403 0.00975561 0.00940108 0.00917196 0.00874758] mean value: 0.009226036071777344 key: test_mcc value: [-0.40824829 -0.16666667 -0.16666667 -0.40824829 0.16666667 0. 1. 0.57735027 0. 0. ] mean value: 0.059418702159523294 key: train_mcc value: [0.65952381 0.8547619 0.75714286 0.7633652 0.65871309 0.85441771 0.76980036 0.62187434 0.71428571 0.71754731] mean value: 0.7371432287703027 key: test_fscore value: [0. 0.4 0.4 0.57142857 0.66666667 0. 1. 0.66666667 0.5 0.5 ] mean value: 0.4704761904761904 key: train_fscore value: [0.82926829 0.92682927 0.87804878 0.86486486 0.82051282 0.92307692 0.87179487 0.8 0.85714286 0.86363636] mean value: 0.8635175042492115 key: test_precision value: [0. 0.33333333 0.33333333 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [0.85 0.95 0.9 0.94117647 0.84210526 0.94736842 0.94444444 0.84210526 0.85714286 0.82608696] mean value: 0.8900429676065696 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 0.5 0.5 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.9047619 0.85714286 0.8 0.8 0.9 0.80952381 0.76190476 0.85714286 0.9047619 ] mean value: 0.8404761904761904 key: test_accuracy value: [0.4 0.4 0.4 0.4 0.6 0.4 1. 0.75 0.5 0.5 ] mean value: 0.5349999999999999 key: train_accuracy value: [0.82926829 0.92682927 0.87804878 0.87804878 0.82926829 0.92682927 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8673054587688733 key: test_roc_auc value: [0.33333333 0.41666667 0.41666667 0.33333333 0.58333333 0.5 1. 0.75 0.5 0.5 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.8297619 0.92738095 0.87857143 0.87619048 0.82857143 0.92619048 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8671428571428572 key: test_jcc value: [0. 0.25 0.25 0.4 0.5 0. 1. 0.5 0.33333333 0.33333333] mean value: 0.3566666666666667 key: train_jcc value: [0.70833333 0.86363636 0.7826087 0.76190476 0.69565217 0.85714286 0.77272727 0.66666667 0.75 0.76 ] mean value: 0.7618672124976473 MCC on Blind test: 0.12 MCC on Training: 0.06 Running classifier: 23 Model_name: Stochastic GDescent Model func: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00862122 0.00922823 0.00891519 0.00913572 0.00806808 0.00820661 0.00832367 0.00818133 0.00808668 0.00812387] mean value: 0.008489060401916503 key: score_time value: [0.00857043 0.00888681 0.00888491 0.00887418 0.00816822 0.00827622 0.00822997 0.00819159 0.00825238 0.00827312] mean value: 0.008460783958435058 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 0.16666667 0. 0. 1. 0.57735027 0. 0.57735027] mean value: 0.0688374043190466 key: train_mcc value: [0.90649828 1. 1. 0.95227002 0.70272837 1. 0.55901699 0.40824829 0.36760731 0.8660254 ] mean value: 0.7762394663113877 key: test_fscore value: [0. 0.33333333 0.33333333 0.66666667 0.75 0. 1. 0.66666667 0.66666667 0.66666667] mean value: 0.5083333333333334 key: train_fscore value: [0.95454545 1. 1. 0.97435897 0.85106383 1. 0.64516129 0.44444444 0.72413793 0.92307692] mean value: 0.8516788847570094 key: test_precision value: [0. 0.25 0.25 0.66666667 0.6 0. 1. 1. 0.5 1. ] mean value: 0.5266666666666666 key: train_precision value: [0.91304348 1. 1. 1. 0.74074074 1. 1. 1. 0.56756757 1. ] mean value: 0.9221351786569176 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 0.5 1. 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 0.95 1. 1. 0.47619048 0.28571429 1. 0.85714286] mean value: 0.856904761904762 key: test_accuracy value: [0.4 0.2 0.2 0.6 0.6 0.4 1. 0.75 0.5 0.75] mean value: 0.54 key: train_accuracy value: [0.95121951 1. 1. 0.97560976 0.82926829 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8684668989547039 key: test_roc_auc value: [0.33333333 0.25 0.25 0.58333333 0.5 0.5 1. 0.75 0.5 0.75 ] mean value: 0.5416666666666666 key: train_roc_auc value: [0.95 1. 1. 0.975 0.83333333 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8686904761904761 key: test_jcc value: [0. 0.2 0.2 0.5 0.6 0. 1. 0.5 0.5 0.5] mean value: 0.4 key: train_jcc value: [0.91304348 1. 1. 0.95 0.74074074 1. 0.47619048 0.28571429 0.56756757 0.85714286] mean value: 0.7790399405616796 MCC on Blind test: 0.0 MCC on Training: 0.07 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.05810428 0.03964543 0.03329706 0.03207445 0.03326654 0.03801894 0.03421211 0.0343132 0.03303766 0.03327084] mean value: 0.03692405223846436 key: score_time value: [0.01037598 0.01003814 0.00997329 0.01020169 0.01002002 0.00987911 0.00995755 0.00995421 0.00986147 0.01017356] mean value: 0.010043501853942871 key: test_mcc value: [ 0.61237244 -0.16666667 0. -0.66666667 0.66666667 0.40824829 0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.31633551362514944 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.4 0.57142857 0.33333333 0.8 0.5 0.66666667 0.66666667 0.8 0.8 ] mean value: 0.6204761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.74 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.4 0.2 0.8 0.6 0.75 0.75 0.75 0.75] mean value: 0.62 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.41666667 0.5 0.16666667 0.83333333 0.66666667 0.75 0.75 0.75 0.75 ] mean value: 0.6333333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.25 0.4 0.2 0.66666667 0.33333333 0.5 0.5 0.66666667 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.32 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... ?9&?{:?H7?L?iE?i?#0?]6?:?Ֆ+??yh?5i?TP?;7?Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... ?`ff?`ff???`?Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... qjV@[Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.07147145 0.06671619 0.06645942 0.06676054 0.068331 0.06672955 0.06703568 0.06725049 0.06718302 0.06788754] mean value: 0.06758248805999756 key: score_time value: [0.0143826 0.01437283 0.01442099 0.01425743 0.01563931 0.01469231 0.01427484 0.01426411 0.01429868 0.0143373 ] mean value: 0.014494037628173828 key: test_mcc value: [-0.16666667 -0.16666667 0. 0.16666667 0.16666667 0. 0.57735027 1. 0. 0.57735027] mean value: 0.21547005383792514 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.4 0.57142857 0.66666667 0.66666667 0. 0.66666667 1. 0.66666667 0.8 ] mean value: 0.5838095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.33333333 0.4 0.66666667 0.66666667 0. 1. 1. 0.5 0.66666667] mean value: 0.5566666666666668 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.4 0.6 0.6 0.4 0.75 1. 0.5 0.75] mean value: 0.58 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.41666667 0.41666667 0.5 0.58333333 0.58333333 0.5 0.75 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.25 0.4 0.5 0.5 0. 0.5 1. 0.5 0.66666667] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.22 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.08163309 0.11858511 0.11845922 0.11047935 0.10642529 0.09915996 0.11996365 0.15040755 0.11813974 0.12285495] mean value: 0.11461079120635986 key: score_time value: [0.06332254 0.04014683 0.07738638 0.05280519 0.04362941 0.03615856 0.03845716 0.05396342 0.06287646 0.04782629] mean value: 0.05165722370147705 key: test_mcc value: [ 0.16666667 -0.16666667 0. -0.66666667 1. 0.40824829 1. 0.57735027 0.57735027 0.57735027] mean value: 0.3473632431366074 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.4 0.57142857 0.33333333 1. 0.5 1. 0.66666667 0.8 0.8 ] mean value: 0.6571428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.6900000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.5 0.5 1. 0.33333333 1. 0.33333333 1. 0.5 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.4 0.2 1. 0.6 1. 0.75 0.75 0.75] mean value: 0.645 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.41666667 0.5 0.16666667 1. 0.66666667 1. 0.75 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.25 0.4 0.2 1. 0.33333333 1. 0.5 0.66666667 0.66666667] mean value: 0.535 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.35 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01383662 0.00976181 0.00846291 0.00878906 0.00854349 0.00851846 0.00881314 0.00859356 0.00868225 0.00870061] mean value: 0.009270191192626953 key: score_time value: [0.01244545 0.00819087 0.00814748 0.00815272 0.00818849 0.00828934 0.00816822 0.00814724 0.00818372 0.00817466] mean value: 0.008608818054199219 key: test_mcc value: [ 0.61237244 0.61237244 0. -0.66666667 -0.40824829 0.40824829 0. 0. 0.57735027 0.57735027] mean value: 0.17127787431041744 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.33333333 0.57142857 0.5 0.5 0.5 0.8 0.8 ] mean value: 0.590952380952381 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.33333333 0.5 1. 0.5 0.5 0.66666667 0.66666667] mean value: 0.6566666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.2 0.4 0.6 0.5 0.5 0.75 0.75] mean value: 0.5700000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.16666667 0.33333333 0.66666667 0.5 0.5 0.75 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.2 0.4 0.33333333 0.33333333 0.33333333 0.66666667 0.66666667] mean value: 0.4333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.17 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00991845 0.00776005 0.00782776 0.00775552 0.00846386 0.00771499 0.0078125 0.00782704 0.00776744 0.00778747] mean value: 0.008063507080078126 key: score_time value: [0.00917935 0.00832558 0.00809622 0.00811052 0.00812507 0.00854683 0.00807476 0.00812912 0.00812459 0.00849462] mean value: 0.00832066535949707 key: test_mcc value: [ 0.61237244 -0.40824829 0.40824829 -0.40824829 0.61237244 0.66666667 1. 0.57735027 0. 0. ] mean value: 0.30605135167840186 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0. 0.66666667 0.57142857 0.85714286 0.8 1. 0.8 0.5 0.5 ] mean value: 0.6361904761904762 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0. 0.5 0.5 0.75 1. 1. 0.66666667 0.5 0.5 ] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.66666667 1. 1. 0.5 0.5 ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.6 0.4 0.8 0.8 1. 0.75 0.5 0.5 ] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.33333333 0.66666667 0.33333333 0.75 0.83333333 1. 0.75 0.5 0.5 ] mean value: 0.6416666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0. 0.5 0.4 0.75 0.66666667 1. 0.66666667 0.33333333 0.33333333] mean value: 0.5149999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.31 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08452272 0.08124566 0.0868783 0.08290911 0.08450985 0.08158779 0.08731771 0.08250403 0.08408141 0.08428979] mean value: 0.083984637260437 key: score_time value: [0.01821256 0.01827836 0.01890159 0.01829147 0.01904392 0.0188849 0.01866412 0.01823401 0.01864219 0.01820183] mean value: 0.018535494804382324 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 0.61237244 0.40824829 0.57735027 0.57735027 0.57735027 -0.57735027] mean value: 0.19337396407417132 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 0.85714286 0.5 0.66666667 0.66666667 0.8 0.4 ] mean value: 0.5761904761904761 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 0.75 1. 1. 1. 0.66666667 0.33333333] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 0.5 1. 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 0.8 0.6 0.75 0.75 0.75 0.25] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 0.75 0.66666667 0.75 0.75 0.75 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 0.75 0.33333333 0.5 0.5 0.66666667 0.25 ] mean value: 0.43999999999999995 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.15 MCC on Training: 0.19 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.12011313 0.11419797 0.10635972 0.12131834 0.1168859 0.1044004 0.11868238 0.11927056 0.12323308 0.10795641] mean value: 0.11524178981781005 key: score_time value: [0.00861526 0.0085752 0.00875354 0.00858045 0.0085125 0.00891137 0.00879335 0.00856757 0.00877142 0.00879788] mean value: 0.008687853813171387 key: test_mcc value: [0.61237244 0.61237244 0. 0.16666667 0.16666667 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.36983773027576633 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.66666667 0.66666667 0.5 0.66666667 0.5 0.8 0.8 ] mean value: 0.6504761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.66666667 0.66666667 1. 1. 0.5 0.66666667 0.66666667] mean value: 0.7566666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.6 0.6 0.6 0.75 0.5 0.75 0.75] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.58333333 0.58333333 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.5 0.5 0.33333333 0.5 0.33333333 0.66666667 0.66666667] mean value: 0.49000000000000005 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.48 MCC on Training: 0.37 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00801754 0.00788379 0.00803995 0.00794744 0.0079186 0.00786209 0.00811148 0.00784183 0.00799656 0.00807548] mean value: 0.007969474792480469 key: score_time value: [0.00836015 0.00829911 0.00836468 0.0083406 0.00827551 0.0082891 0.00820875 0.00825548 0.00828886 0.00827885] mean value: 0.00829610824584961 key: test_mcc value: [-0.16666667 0. 0. 0.61237244 0. 0.40824829 1. 1. 0. 0.57735027] mean value: 0.34313043286826167 key: train_mcc value: [0.57570364 0.698212 0.63496528 0.49692935 0.59982886 0.63994524 0.56652882 0.52704628 0.54659439 0.60609153] mean value: 0.5891845382894122 key: test_fscore value: [0.4 0.57142857 0.57142857 0.85714286 0.75 0.5 1. 1. 0.66666667 0.8 ] mean value: 0.7116666666666667 key: train_fscore value: [0.80851064 0.85714286 0.83333333 0.76595745 0.80851064 0.82608696 0.8 0.78431373 0.79166667 0.81632653] mean value: 0.8091848793171292 key: test_precision value: [0.33333333 0.4 0.4 0.75 0.6 1. 1. 1. 0.5 0.66666667] mean value: 0.665 key: train_precision value: [0.73076923 0.75 0.74074074 0.66666667 0.7037037 0.73076923 0.68965517 0.66666667 0.7037037 0.71428571] mean value: 0.7096960829719451 key: test_recall value: [0.5 1. 1. 1. 1. 0.33333333 1. 1. 1. 1. ] mean value: 0.8833333333333332 key: train_recall value: [0.9047619 1. 0.95238095 0.9 0.95 0.95 0.95238095 0.95238095 0.9047619 0.95238095] mean value: 0.9419047619047619 key: test_accuracy value: [0.4 0.4 0.4 0.8 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.645 key: train_accuracy value: [0.7804878 0.82926829 0.80487805 0.73170732 0.7804878 0.80487805 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779326364692218 key: test_roc_auc value: [0.41666667 0.5 0.5 0.75 0.5 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [0.77738095 0.825 0.80119048 0.73571429 0.78452381 0.80833333 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779761904761904 key: test_jcc value: [0.25 0.4 0.4 0.75 0.6 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.5900000000000001 key: train_jcc value: [0.67857143 0.75 0.71428571 0.62068966 0.67857143 0.7037037 0.66666667 0.64516129 0.65517241 0.68965517] mean value: 0.6802477473500833 MCC on Blind test: -0.03 MCC on Training: 0.34 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.00958753 0.00953865 0.00945377 0.00958991 0.00966001 0.00957441 0.00958896 0.00952601 0.00952077 0.01068854] mean value: 0.009672856330871582 key: score_time value: [0.0084002 0.00827026 0.00833797 0.00846314 0.00835466 0.00877142 0.00828671 0.00822973 0.00890112 0.00831556] mean value: 0.008433079719543457 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 1. 0.40824829 0.57735027 1. -0.57735027 -0.57735027] mean value: 0.15893163974770408 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 1. 0.5 0.66666667 1. 0.4 0.4 ] mean value: 0.5838095238095239 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 1. 1. 1. 1. 0.33333333 0.33333333] mean value: 0.6333333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 1. 0.6 0.75 1. 0.25 0.25] mean value: 0.585 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 1. 0.66666667 0.75 1. 0.25 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 1. 0.33333333 0.5 1. 0.25 0.25 ] mean value: 0.4733333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.16 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00887346 0.00884914 0.0087254 0.00889206 0.00897646 0.009027 0.00814652 0.00933361 0.00784755 0.00863242] mean value: 0.008730363845825196 key: score_time value: [0.00973225 0.00965524 0.0097816 0.01492572 0.00945878 0.01028347 0.00957394 0.01003051 0.00966191 0.00960255] mean value: 0.01027059555053711 key: test_mcc value: [ 0.16666667 -0.16666667 0.40824829 -0.40824829 0.16666667 -0.61237244 0. 0.57735027 0. 0.57735027] mean value: 0.07089947693501238 key: train_mcc value: [0.36718832 0.56527676 0.56527676 0.56086079 0.56086079 0.52420964 0.43656413 0.43052839 0.4472136 0.47673129] mean value: 0.49347104597079217 key: test_fscore value: [0.5 0.4 0.66666667 0.57142857 0.66666667 0. 0.5 0.8 0.66666667 0.8 ] mean value: 0.5571428571428572 key: train_fscore value: [0.71111111 0.8 0.8 0.76923077 0.76923077 0.77272727 0.73913043 0.72727273 0.75 0.74418605] mean value: 0.7582889130866886 key: test_precision value: [0.5 0.33333333 0.5 0.5 0.66666667 0. 0.5 0.66666667 0.5 0.66666667] mean value: 0.4833333333333333 key: train_precision value: [0.66666667 0.75 0.75 0.78947368 0.78947368 0.70833333 0.68 0.69565217 0.66666667 0.72727273] mean value: 0.722353893627349 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [0.76190476 0.85714286 0.85714286 0.75 0.75 0.85 0.80952381 0.76190476 0.85714286 0.76190476] mean value: 0.8016666666666665 key: test_accuracy value: [0.6 0.4 0.6 0.4 0.6 0.2 0.5 0.75 0.5 0.75] mean value: 0.53 key: train_accuracy value: [0.68292683 0.7804878 0.7804878 0.7804878 0.7804878 0.75609756 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7441927990708479 key: test_roc_auc value: [0.58333333 0.41666667 0.66666667 0.33333333 0.58333333 0.25 0.5 0.75 0.5 0.75 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.68095238 0.77857143 0.77857143 0.7797619 0.7797619 0.75833333 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7436904761904762 key: test_jcc value: [0.33333333 0.25 0.5 0.4 0.5 0. 0.33333333 0.66666667 0.5 0.66666667] mean value: 0.41500000000000004 key: train_jcc value: [0.55172414 0.66666667 0.66666667 0.625 0.625 0.62962963 0.5862069 0.57142857 0.6 0.59259259] mean value: 0.6114915161466885 MCC on Blind test: 0.21 MCC on Training: 0.07 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01089215 0.01359582 0.01349401 0.01353574 0.01374888 0.0136168 0.01361299 0.01387882 0.01385021 0.01362491] mean value: 0.01338503360748291 key: score_time value: [0.01122189 0.01152539 0.01142907 0.01145124 0.01145363 0.01145649 0.01146746 0.01146293 0.01143336 0.01160645] mean value: 0.011450791358947754 key: test_mcc value: [ 0.16666667 -0.16666667 -0.61237244 -0.40824829 0.40824829 -0.16666667 -0.57735027 1. 1. 0.57735027] mean value: 0.12209608976375388 key: train_mcc value: [0.8547619 0.75714286 0.95227002 0.90238095 0.90238095 0.85441771 0.9047619 0.9047619 0.76277007 0.9047619 ] mean value: 0.8700410175391831 key: test_fscore value: [0.5 0.4 0.33333333 0.57142857 0.5 0.4 0.4 1. 1. 0.66666667] mean value: 0.5771428571428572 key: train_fscore value: [0.92682927 0.87804878 0.97674419 0.95 0.95 0.92307692 0.95238095 0.95238095 0.87804878 0.95238095] mean value: 0.9339890795534584 key: test_precision value: [0.5 0.33333333 0.25 0.5 1. 0.5 0.33333333 1. 1. 1. ] mean value: 0.6416666666666666 key: train_precision value: [0.95 0.9 0.95454545 0.95 0.95 0.94736842 0.95238095 0.95238095 0.9 0.95238095] mean value: 0.9409056732740944 key: test_recall value: [0.5 0.5 0.5 0.66666667 0.33333333 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.5833333333333333 key: train_recall value: [0.9047619 0.85714286 1. 0.95 0.95 0.9 0.95238095 0.95238095 0.85714286 0.95238095] mean value: 0.9276190476190477 key: test_accuracy value: [0.6 0.4 0.2 0.4 0.6 0.4 0.25 1. 1. 0.75] mean value: 0.5599999999999999 key: train_accuracy value: [0.92682927 0.87804878 0.97560976 0.95121951 0.95121951 0.92682927 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347851335656214 key: test_roc_auc value: [0.58333333 0.41666667 0.25 0.33333333 0.66666667 0.41666667 0.25 1. 1. 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92738095 0.87857143 0.975 0.95119048 0.95119048 0.92619048 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347619047619047 key: test_jcc value: [0.33333333 0.25 0.2 0.4 0.33333333 0.25 0.25 1. 1. 0.5 ] mean value: 0.45166666666666666 key: train_jcc value: [0.86363636 0.7826087 0.95454545 0.9047619 0.9047619 0.85714286 0.90909091 0.90909091 0.7826087 0.90909091] mean value: 0.8777338603425558 MCC on Blind test: -0.3 MCC on Training: 0.12 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02257204 0.01846004 0.01457477 0.01547074 0.01657891 0.01571465 0.01362658 0.01604843 0.01542592 0.01508784] mean value: 0.01635599136352539 key: score_time value: [0.01160479 0.0097611 0.00901341 0.0083828 0.0087471 0.00856948 0.00840473 0.00912809 0.00890493 0.00857544] mean value: 0.009109187126159667 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.61237244 -0.61237244 1. 1. 0. 1. ] mean value: 0.09587585476806848 key: train_mcc value: [0.85441771 0.95238095 1. 0.90238095 0.90238095 0.90649828 0.85811633 0.85811633 0.85811633 0.9047619 ] mean value: 0.8997169740060625 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5595238095238095 key: train_fscore value: [0.93023256 0.97560976 1. 0.95 0.95 0.94736842 0.92682927 0.92682927 0.92682927 0.95238095] mean value: 0.9486079492548729 key: test_precision value: [0. 0.25 0.25 0.5 0.75 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.90909091 1. 1. 0.95 0.95 1. 0.95 0.95 0.95 0.95238095] mean value: 0.9611471861471861 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [0.95238095 0.95238095 1. 0.95 0.95 0.9 0.9047619 0.9047619 0.9047619 0.95238095] mean value: 0.9371428571428572 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.8 0.2 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.92682927 0.97560976 1. 0.95121951 0.95121951 0.95121951 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9494192799070849 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.75 0.25 1. 1. 0.5 1. ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92619048 0.97619048 1. 0.95119048 0.95119048 0.95 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9492857142857142 key: test_jcc value: [0. 0.2 0.2 0.4 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.4883333333333333 key: train_jcc value: [0.86956522 0.95238095 1. 0.9047619 0.9047619 0.9 0.86363636 0.86363636 0.86363636 0.90909091] mean value: 0.9031469979296066 MCC on Blind test: 0.03 MCC on Training: 0.1 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.15620804 0.16348553 0.15360594 0.17080092 0.1831665 0.16175008 0.17978621 0.17742205 0.17361164 0.18059707] mean value: 0.17004339694976806 key: score_time value: [0.00939107 0.00886726 0.0096879 0.00971437 0.00965619 0.00970888 0.00925088 0.00879502 0.0094924 0.0095489 ] mean value: 0.009411287307739259 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 0. ] mean value: 0.012542521434735132 key: train_mcc value: [0.41487884 1. 0.7098505 1. 1. 0.95227002 0.85811633 1. 1. 1. ] mean value: 0.8935115692085429 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4904761904761905 key: train_fscore value: [0.72727273 1. 0.86363636 1. 1. 0.97435897 0.92682927 1. 1. 1. ] mean value: 0.9492097333560748 key: test_precision value: [0. 0.25 0.25 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4666666666666666 key: train_precision value: [0.69565217 1. 0.82608696 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.9471739130434782 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.5333333333333333 key: train_recall value: [0.76190476 1. 0.9047619 1. 1. 0.95 0.9047619 1. 1. 1. ] mean value: 0.9521428571428571 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.6 0.4 1. 1. 0.5 0.5] mean value: 0.52 key: train_accuracy value: [0.70731707 1. 0.85365854 1. 1. 0.97560976 0.92857143 1. 1. 1. ] mean value: 0.9465156794425088 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 0.5 ] mean value: 0.525 key: train_roc_auc value: [0.70595238 1. 0.85238095 1. 1. 0.975 0.92857143 1. 1. 1. ] mean value: 0.9461904761904762 key: test_jcc value: [0. 0.2 0.2 0.4 0.5 0. 1. 1. 0.33333333 0.33333333] mean value: 0.39666666666666667 key: train_jcc value: [0.57142857 1. 0.76 1. 1. 0.95 0.86363636 1. 1. 1. ] mean value: 0.9145064935064935 MCC on Blind test: -0.05 MCC on Training: 0.01 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.23483658 0.23520446 0.2153573 0.24292231 0.24237514 0.24720836 0.25254416 0.36358142 0.30904245 0.25887346] mean value: 0.26019456386566164 key: score_time value: [0.01219893 0.01181388 0.01180792 0.01184154 0.01180482 0.01187086 0.01180148 0.0118866 0.01189446 0.01175952] mean value: 0.011868000030517578 key: test_mcc value: [ 0. -0.61237244 -0.16666667 -0.40824829 0.16666667 0.40824829 1. 1. 0. 0.57735027] mean value: 0.19649778334938311 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.4 0.57142857 0.66666667 0.5 1. 1. 0.5 0.8 ] mean value: 0.5771428571428572 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.25 0.33333333 0.5 0.66666667 1. 1. 1. 0.5 0.66666667] mean value: 0.5916666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0.33333333 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.4 0.4 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.6050000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.25 0.41666667 0.33333333 0.58333333 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.2 0.25 0.4 0.5 0.33333333 1. 1. 0.33333333 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.2 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01118445 0.01103711 0.00822091 0.00794482 0.00777507 0.00779271 0.00791383 0.00842404 0.00823975 0.00813913] mean value: 0.008667182922363282 key: score_time value: [0.01109362 0.01109242 0.00850892 0.00833392 0.00816655 0.00823069 0.00806332 0.00877857 0.00854731 0.00810337] mean value: 0.008891868591308593 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [-0.40824829 0. -0.61237244 -0.40824829 0.16666667 0. 1. 0.57735027 0.57735027 0. ] mean value: 0.08924981884223976 key: train_mcc value: [0.36515617 0.31666667 0.46623254 0.46300848 0.46300848 0.41428571 0.43052839 0.38138504 0.42857143 0.43052839] mean value: 0.415937128657245 key: test_fscore value: [0. 0.57142857 0.33333333 0.57142857 0.66666667 0. 1. 0.8 0.8 0.5 ] mean value: 0.5242857142857142 key: train_fscore value: [0.69767442 0.66666667 0.75555556 0.71794872 0.71794872 0.7 0.72727273 0.69767442 0.71428571 0.72727273] mean value: 0.712229966416013 key: test_precision value: [0. 0.4 0.25 0.5 0.66666667 0. 1. 0.66666667 0.66666667 0.5 ] mean value: 0.46499999999999997 key: train_precision value: [0.68181818 0.66666667 0.70833333 0.73684211 0.73684211 0.7 0.69565217 0.68181818 0.71428571 0.69565217] mean value: 0.701791063627448 key: test_recall value: [0. 1. 0.5 0.66666667 0.66666667 0. 1. 1. 1. 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [0.71428571 0.66666667 0.80952381 0.7 0.7 0.7 0.76190476 0.71428571 0.71428571 0.76190476] mean value: 0.7242857142857143 key: test_accuracy value: [0.4 0.4 0.2 0.4 0.6 0.4 1. 0.75 0.75 0.5 ] mean value: 0.54 key: train_accuracy value: [0.68292683 0.65853659 0.73170732 0.73170732 0.73170732 0.70731707 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7077235772357724 key: test_roc_auc value: [0.33333333 0.5 0.25 0.33333333 0.58333333 0.5 1. 0.75 0.75 0.5 ] mean value: 0.55 key: train_roc_auc value: [0.68214286 0.65833333 0.7297619 0.73095238 0.73095238 0.70714286 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7072619047619048 key: test_jcc value: [0. 0.4 0.2 0.4 0.5 0. 1. 0.66666667 0.66666667 0.33333333] mean value: 0.41666666666666663 key: train_jcc value: [0.53571429 0.5 0.60714286 0.56 0.56 0.53846154 0.57142857 0.53571429 0.55555556 0.57142857] mean value: 0.5535445665445665 MCC on Blind test: 0.23 MCC on Training: 0.09 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0081799 0.00812912 0.00806451 0.00806212 0.00808954 0.00801945 0.0080657 0.00803256 0.00913548 0.008111 ] mean value: 0.008188939094543457 key: score_time value: [0.00832891 0.0082612 0.00831604 0.00825977 0.00833678 0.00831842 0.00828862 0.00840378 0.00862408 0.00831175] mean value: 0.008344936370849609 key: test_mcc value: [ 0.61237244 -0.16666667 -0.61237244 1. -0.61237244 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.2361259995670279 key: train_mcc value: [0.65952381 0.78072006 0.78072006 0.698212 0.65915306 0.81975606 0.67357531 0.78446454 0.81322028 0.78446454] mean value: 0.7453809730969173 key: test_fscore value: [0.66666667 0.4 0.33333333 1. 0. 0.5 0.66666667 0. 0.8 0.66666667] mean value: 0.5033333333333333 key: train_fscore value: [0.82926829 0.86486486 0.86486486 0.78787879 0.75 0.88888889 0.82051282 0.86486486 0.9 0.86486486] mean value: 0.8436008249422884 key: test_precision value: [1. 0.33333333 0.25 1. 0. 1. 1. 0. 0.66666667 1. ] mean value: 0.625 key: train_precision value: [0.85 1. 1. 1. 1. 1. 0.88888889 1. 0.94736842 1. ] mean value: 0.9686257309941521 key: test_recall value: [0.5 0.5 0.5 1. 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.76190476 0.76190476 0.65 0.6 0.8 0.76190476 0.76190476 0.85714286 0.76190476] mean value: 0.7526190476190475 key: test_accuracy value: [0.8 0.4 0.2 1. 0.2 0.6 0.75 0.5 0.75 0.75] mean value: 0.595 key: train_accuracy value: [0.82926829 0.87804878 0.87804878 0.82926829 0.80487805 0.90243902 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8621951219512196 key: test_roc_auc value: [0.75 0.41666667 0.25 1. 0.25 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6083333333333333 key: train_roc_auc value: [0.8297619 0.88095238 0.88095238 0.825 0.8 0.9 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8616666666666667 key: test_jcc value: [0.5 0.25 0.2 1. 0. 0.33333333 0.5 0. 0.66666667 0.5 ] mean value: 0.39499999999999996 key: train_jcc value: [0.70833333 0.76190476 0.76190476 0.65 0.6 0.8 0.69565217 0.76190476 0.81818182 0.76190476] mean value: 0.7319786373047242 MCC on Blind test: -0.07 MCC on Training: 0.24 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00810981 0.0085299 0.00833607 0.0084002 0.00886893 0.00882959 0.00940371 0.00805235 0.00858521 0.00805831] mean value: 0.00851740837097168 key: score_time value: [0.00830674 0.00835013 0.00854397 0.00817323 0.00825143 0.00889039 0.00817609 0.00830173 0.00810242 0.00857377] mean value: 0.008366990089416503 key: test_mcc value: [-0.66666667 -0.16666667 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 1. ] mean value: 0.13127126071736755 key: train_mcc value: [0.77831178 0.90692382 1. 0.74124932 0.95238095 0.95227002 0.80952381 0.81322028 0.8660254 0.78446454] mean value: 0.860436992906499 key: test_fscore value: [0. 0.4 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5471428571428572 key: train_fscore value: [0.89361702 0.95 1. 0.86956522 0.97560976 0.97435897 0.9047619 0.90909091 0.92307692 0.86486486] mean value: 0.9264945570919038 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0. 0.33333333 0.25 0.5 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.80769231 1. 1. 0.76923077 0.95238095 1. 0.9047619 0.86956522 1. 1. ] mean value: 0.9303631151457239 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 0.9047619 1. 1. 1. 0.95 0.9047619 0.95238095 0.85714286 0.76190476] mean value: 0.9330952380952382 key: test_accuracy value: [0.2 0.4 0.2 0.4 0.6 0.4 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.87804878 0.95121951 1. 0.85365854 0.97560976 0.97560976 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9253193960511034 key: test_roc_auc value: [0.16666667 0.41666667 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 1. ] mean value: 0.575 key: train_roc_auc value: [0.875 0.95238095 1. 0.85714286 0.97619048 0.975 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9254761904761905 key: test_jcc value: [0. 0.25 0.2 0.4 0.5 0. 1. 1. 0.33333333 1. ] mean value: 0.4683333333333334 key: train_jcc value: [0.80769231 0.9047619 1. 0.76923077 0.95238095 0.95 0.82608696 0.83333333 0.85714286 0.76190476] mean value: 0.8662533842968625 MCC on Blind test: 0.05 MCC on Training: 0.13 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0112586 0.00938201 0.00923204 0.00876093 0.00977373 0.00839782 0.0093205 0.00851107 0.00952101 0.0099349 ] mean value: 0.009409260749816895 key: score_time value: [0.0096581 0.00917578 0.00987244 0.00925231 0.00997996 0.00905943 0.00872159 0.00874567 0.00847888 0.00921369] mean value: 0.009215784072875977 key: test_mcc value: [-0.40824829 -0.40824829 0. 0.16666667 0.66666667 0.40824829 -0.57735027 1. -0.57735027 0. ] mean value: 0.027038450449021856 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0.66666667 0.8 0.5 0.4 1. 0.4 0.5 ] mean value: 0.42666666666666664 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0.66666667 1. 1. 0.33333333 1. 0.33333333 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0.66666667 0.66666667 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.41666666666666663 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.6 0.6 0.8 0.6 0.25 1. 0.25 0.5 ] mean value: 0.54 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.33333333 0.5 0.58333333 0.83333333 0.66666667 0.25 1. 0.25 0.5 ] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0.5 0.66666667 0.33333333 0.25 1. 0.25 0.33333333] mean value: 0.33333333333333337 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.03 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.55105233 0.53781676 0.53187943 0.56034636 0.51717305 0.57593083 0.58922815 0.57616568 0.57828498 0.53115702] mean value: 0.5549034595489502 key: score_time value: [0.17247725 0.13413644 0.17475033 0.10614705 0.17236185 0.15031648 0.14225435 0.16412115 0.19759965 0.1547451 ] mean value: 0.15689096450805665 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 1. 0.40824829 0.57735027 1. 0.57735027 -0.57735027] mean value: 0.21522652263201553 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 1. 0.5 0.66666667 1. 0.8 0.4 ] mean value: 0.6104761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.5 0.5 1. 1. 1. 1. 0.66666667 0.33333333] mean value: 0.65 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0. 0.6 0.4 1. 0.6 0.75 1. 0.75 0.25] mean value: 0.595 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 1. 0.66666667 0.75 1. 0.75 0.25 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.5 0.4 1. 0.33333333 0.5 1. 0.66666667 0.25 ] mean value: 0.49833333333333335 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.22 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.84234905 0.91010118 0.88582349 0.8455646 0.8680613 0.86011481 0.81437445 0.86640668 0.84942198 0.88474488] mean value: 0.8626962423324585 key: score_time value: [0.21817231 0.20926094 0.11821461 0.1948905 0.27175617 0.18559885 0.22147179 0.16411352 0.2225132 0.17579198] mean value: 0.19817838668823243 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 0.61237244 0.40824829 1. 1. 0.57735027 0. ] mean value: 0.276463766201595 key: train_mcc value: [0.7565654 0.90692382 0.8047619 0.90238095 0.8547619 0.85441771 0.9047619 0.80952381 0.81322028 0.80952381] mean value: 0.841684150259194 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 0.85714286 0.5 1. 1. 0.8 0.5 ] mean value: 0.6395238095238095 key: train_fscore value: [0.88372093 0.95 0.9047619 0.95 0.92682927 0.92307692 0.95238095 0.9047619 0.9 0.9047619 ] mean value: 0.9200293788268832 key: test_precision value: [0.5 0. 0.5 0.5 0.75 1. 1. 1. 0.66666667 0.5 ] mean value: 0.6416666666666667 key: train_precision value: [0.86363636 1. 0.9047619 0.95 0.9047619 0.94736842 0.95238095 0.9047619 0.94736842 0.9047619 ] mean value: 0.9279801777170199 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 1. 1. 1. 0.5 ] mean value: 0.7 key: train_recall value: [0.9047619 0.9047619 0.9047619 0.95 0.95 0.9 0.95238095 0.9047619 0.85714286 0.9047619 ] mean value: 0.9133333333333334 key: test_accuracy value: [0.6 0. 0.6 0.4 0.8 0.6 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_accuracy value: [0.87804878 0.95121951 0.90243902 0.95121951 0.92682927 0.92682927 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203252032520325 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 0.75 0.66666667 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_roc_auc value: [0.87738095 0.95238095 0.90238095 0.95119048 0.92738095 0.92619048 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203571428571428 key: test_jcc value: [0.33333333 0. 0.5 0.4 0.75 0.33333333 1. 1. 0.66666667 0.33333333] mean value: 0.5316666666666666 key: train_jcc value: [0.79166667 0.9047619 0.82608696 0.9047619 0.86363636 0.85714286 0.90909091 0.82608696 0.81818182 0.82608696] mean value: 0.8527503293807641 MCC on Blind test: -0.13 MCC on Training: 0.28 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.00949883 0.00883245 0.0098927 0.00988412 0.00924563 0.00968933 0.00867939 0.00862598 0.00880575 0.00921679] mean value: 0.009237098693847656 key: score_time value: [0.00891423 0.00907803 0.00937724 0.00920177 0.00865936 0.00903368 0.00833631 0.00812626 0.00827265 0.00852466] mean value: 0.00875241756439209 key: test_mcc value: [-0.40824829 -0.16666667 -0.61237244 -0.66666667 0.61237244 0. 1. 1. 0. 1. ] mean value: 0.17584183762028038 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5423809523809524 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 0. 1. 1. 0.5 1. ] mean value: 0.5166666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.2 0.2 0.8 0.4 1. 1. 0.5 1. ] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.41666667 0.25 0.16666667 0.75 0.5 1. 1. 0.5 1. ] mean value: 0.5916666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.47333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.31 MCC on Training: 0.18 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.02626038 0.02669883 0.02652597 0.02703953 0.02571082 0.02500319 0.02573419 0.02634645 0.02708006 0.02537894] mean value: 0.02617783546447754 key: score_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.00906801 0.00909162 0.00930405 0.00932908 0.00918078 0.00894046 0.00926852 0.00903821 0.00933886 0.00892043] mean value: 0.009148001670837402 key: test_mcc value: [ 0. -0.16666667 -0.61237244 -0.66666667 0.61237244 0.40824829 1. 1. 0. 0.57735027] mean value: 0.21522652263201558 key: train_mcc value: [1. 1. 0.8547619 1. 1. 1. 0.85811633 1. 1. 1. ] mean value: 0.9712878235082938 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0.5 1. 1. 0.5 0.66666667] mean value: 0.5590476190476191 key: train_fscore value: [1. 1. 0.92682927 1. 1. 1. 0.92682927 1. 1. 1. ] mean value: 0.9853658536585366 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 1. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_precision value: [1. 1. 0.95 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.99 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0.33333333 1. 1. 0.5 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 0.9047619 1. 1. 1. 0.9047619 1. 1. 1. ] mean value: 0.980952380952381 key: test_accuracy value: [0.6 0.4 0.2 0.2 0.8 0.6 1. 1. 0.5 0.75] mean value: 0.605 key: train_accuracy value: [1. 1. 0.92682927 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855400696864113 key: test_roc_auc value: [0.5 0.41666667 0.25 0.16666667 0.75 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 0.92738095 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855952380952381 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0.33333333 1. 1. 0.33333333 0.5 ] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 0.86363636 1. 1. 1. 0.86363636 1. 1. 1. ] mean value: 0.9727272727272727 MCC on Blind test: -0.31 MCC on Training: 0.22 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00938535 0.00972438 0.00911021 0.00823379 0.00916481 0.00917959 0.00969982 0.00937176 0.00918269 0.0087328 ] mean value: 0.009178519248962402 key: score_time value: [0.009197 0.00930834 0.0089221 0.00905299 0.00910592 0.00923848 0.00937533 0.00919485 0.00919533 0.0084734 ] mean value: 0.00910637378692627 key: test_mcc value: [-0.40824829 -0.16666667 -0.16666667 -0.40824829 0.16666667 0. 1. 0.57735027 0. 0. ] mean value: 0.059418702159523294 key: train_mcc value: [0.65952381 0.8547619 0.75714286 0.7633652 0.65871309 0.85441771 0.76980036 0.62187434 0.71428571 0.71754731] mean value: 0.7371432287703027 key: test_fscore value: [0. 0.4 0.4 0.57142857 0.66666667 0. 1. 0.66666667 0.5 0.5 ] mean value: 0.4704761904761904 key: train_fscore value: [0.82926829 0.92682927 0.87804878 0.86486486 0.82051282 0.92307692 0.87179487 0.8 0.85714286 0.86363636] mean value: 0.8635175042492115 key: test_precision value: [0. 0.33333333 0.33333333 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [0.85 0.95 0.9 0.94117647 0.84210526 0.94736842 0.94444444 0.84210526 0.85714286 0.82608696] mean value: 0.8900429676065696 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 0.5 0.5 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.9047619 0.85714286 0.8 0.8 0.9 0.80952381 0.76190476 0.85714286 0.9047619 ] mean value: 0.8404761904761904 key: test_accuracy value: [0.4 0.4 0.4 0.4 0.6 0.4 1. 0.75 0.5 0.5 ] mean value: 0.5349999999999999 key: train_accuracy value: [0.82926829 0.92682927 0.87804878 0.87804878 0.82926829 0.92682927 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8673054587688733 key: test_roc_auc value: [0.33333333 0.41666667 0.41666667 0.33333333 0.58333333 0.5 1. 0.75 0.5 0.5 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.8297619 0.92738095 0.87857143 0.87619048 0.82857143 0.92619048 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8671428571428572 key: test_jcc value: [0. 0.25 0.25 0.4 0.5 0. 1. 0.5 0.33333333 0.33333333] mean value: 0.3566666666666667 key: train_jcc value: [0.70833333 0.86363636 0.7826087 0.76190476 0.69565217 0.85714286 0.77272727 0.66666667 0.75 0.76 ] mean value: 0.7618672124976473 MCC on Blind test: 0.12 MCC on Training: 0.06 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00964975 0.00910807 0.00927353 0.00923562 0.00853014 0.00894547 0.00911546 0.00859642 0.00952506 0.00896096] mean value: 0.009094047546386718 key: score_time value: [0.0092504 0.00931263 0.00865817 0.00860786 0.00952506 0.00881886 0.0090363 0.00928473 0.0087204 0.00911427] mean value: 0.009032869338989257 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 0.16666667 0. 0. 1. 0.57735027 0. 0.57735027] mean value: 0.0688374043190466 key: train_mcc value: [0.90649828 1. 1. 0.95227002 0.70272837 1. 0.55901699 0.40824829 0.36760731 0.8660254 ] mean value: 0.7762394663113877 key: test_fscore value: [0. 0.33333333 0.33333333 0.66666667 0.75 0. 1. 0.66666667 0.66666667 0.66666667] mean value: 0.5083333333333334 key: train_fscore value: [0.95454545 1. 1. 0.97435897 0.85106383 1. 0.64516129 0.44444444 0.72413793 0.92307692] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' 0.8516788847570094 key: test_precision value: [0. 0.25 0.25 0.66666667 0.6 0. 1. 1. 0.5 1. ] mean value: 0.5266666666666666 key: train_precision value: [0.91304348 1. 1. 1. 0.74074074 1. 1. 1. 0.56756757 1. ] mean value: 0.9221351786569176 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 0.5 1. 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 0.95 1. 1. 0.47619048 0.28571429 1. 0.85714286] mean value: 0.856904761904762 key: test_accuracy value: [0.4 0.2 0.2 0.6 0.6 0.4 1. 0.75 0.5 0.75] mean value: 0.54 key: train_accuracy value: [0.95121951 1. 1. 0.97560976 0.82926829 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8684668989547039 key: test_roc_auc value: [0.33333333 0.25 0.25 0.58333333 0.5 0.5 1. 0.75 0.5 0.75 ] mean value: 0.5416666666666666 key: train_roc_auc value: [0.95 1. 1. 0.975 0.83333333 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8686904761904761 key: test_jcc value: [0. 0.2 0.2 0.5 0.6 0. 1. 0.5 0.5 0.5] mean value: 0.4 key: train_jcc value: [0.91304348 1. 1. 0.95 0.74074074 1. 0.47619048 0.28571429 0.56756757 0.85714286] mean value: 0.7790399405616796 MCC on Blind test: 0.0 MCC on Training: 0.07 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.03335929 0.03750134 0.03714919 0.03618264 0.0361259 0.03416443 0.04334474 0.03402209 0.0338695 0.03476334] mean value: 0.03604824542999267 key: score_time value: [0.01109123 0.01125193 0.01119828 0.01089144 0.01003575 0.01077485 0.00999618 0.01012254 0.00999403 0.01049829] mean value: 0.010585451126098632 key: test_mcc value: [ 0.61237244 -0.16666667 0. -0.66666667 0.66666667 0.40824829 0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.31633551362514944 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.4 0.57142857 0.33333333 0.8 0.5 0.66666667 0.66666667 0.8 0.8 ] mean value: 0.6204761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.74 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.4 0.2 0.8 0.6 0.75 0.75 0.75 0.75] mean value: 0.62 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.41666667 0.5 0.16666667 0.83333333 0.66666667 0.75 0.75 0.75 0.75 ] mean value: 0.6333333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.25 0.4 0.2 0.66666667 0.33333333 0.5 0.5 0.66666667 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.32 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... .@X?@@8ZnZ@Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.06901884 0.06626534 0.06610632 0.06622219 0.06662774 0.067173 0.06930685 0.06728816 0.0678072 0.06621051] mean value: 0.06720261573791504 key: score_time value: [0.01410079 0.01401258 0.01425099 0.01410389 0.01408815 0.01531243 0.01415563 0.01406837 0.01470828 0.01417041] mean value: 0.014297151565551757 key: test_mcc value: [-0.16666667 -0.16666667 0. 0.16666667 0.16666667 0. 0.57735027 1. 0. 0.57735027] mean value: 0.21547005383792514 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.4 0.57142857 0.66666667 0.66666667 0. 0.66666667 1. 0.66666667 0.8 ] mean value: 0.5838095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.33333333 0.4 0.66666667 0.66666667 0. 1. 1. 0.5 0.66666667] mean value: 0.5566666666666668 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.4 0.6 0.6 0.4 0.75 1. 0.5 0.75] mean value: 0.58 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.41666667 0.41666667 0.5 0.58333333 0.58333333 0.5 0.75 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.25 0.4 0.5 0.5 0. 0.5 1. 0.5 0.66666667] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.22 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.07935047 0.13531899 0.10189247 0.12564421 0.11338019 0.11033821 0.12098455 0.13838768 0.10319829 0.11569977] mean value: 0.11441948413848876 key: score_time value: [0.05062103 0.07207036 0.03837752 0.07185149 0.04288912 0.06338954 0.06348372 0.03653002 0.07189059 0.04760909] mean value: 0.05587124824523926 key: test_mcc value: [ 0.16666667 -0.16666667 0. -0.66666667 1. 0.40824829 1. 0.57735027 0.57735027 0.57735027] mean value: 0.3473632431366074 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.4 0.57142857 0.33333333 1. 0.5 1. 0.66666667 0.8 0.8 ] mean value: 0.6571428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.6900000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 1. 0.33333333 1. 0.5 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.4 0.2 1. 0.6 1. 0.75 0.75 0.75] mean value: 0.645 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.41666667 0.5 0.16666667 1. 0.66666667 1. 0.75 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.25 0.4 0.2 1. 0.33333333 1. 0.5 0.66666667 0.66666667] mean value: 0.535 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.35 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0106113 0.01021552 0.00998759 0.01035881 0.00972486 0.00995779 0.01029468 0.00904942 0.00902104 0.00940084] mean value: 0.009862184524536133 key: score_time value: [0.00979686 0.0092473 0.00866055 0.00901008 0.01036477 0.00936627 0.0083096 0.00928235 0.00915575 0.00888109] mean value: 0.009207463264465332 key: test_mcc value: [ 0.61237244 0.61237244 0. -0.66666667 -0.40824829 0.40824829 0. 0. 0.57735027 0.57735027] mean value: 0.17127787431041744 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.33333333 0.57142857 0.5 0.5 0.5 0.8 0.8 ] mean value: 0.590952380952381 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.33333333 0.5 1. 0.5 0.5 0.66666667 0.66666667] mean value: 0.6566666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.2 0.4 0.6 0.5 0.5 0.75 0.75] mean value: 0.5700000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.16666667 0.33333333 0.66666667 0.5 0.5 0.75 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.2 0.4 0.33333333 0.33333333 0.33333333 0.66666667 0.66666667] mean value: 0.4333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.17 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00891185 0.00789356 0.00875473 0.00853729 0.00886393 0.00852799 0.00934815 0.00828815 0.00798941 0.00848126] mean value: 0.008559632301330566 key: score_time value: [0.00863075 0.00866628 0.00843549 0.00854349 0.00826931 0.00819707 0.00855517 0.00816345 0.00838542 0.00813746] mean value: 0.00839838981628418 key: test_mcc value: [ 0.61237244 -0.40824829 0.40824829 -0.40824829 0.61237244 0.66666667 1. 0.57735027 0. 0. ] mean value: 0.30605135167840186 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0. 0.66666667 0.57142857 0.85714286 0.8 1. 0.8 0.5 0.5 ] mean value: 0.6361904761904762 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0. 0.5 0.5 0.75 1. 1. 0.66666667 0.5 0.5 ] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.66666667 1. 1. 0.5 0.5 ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.6 0.4 0.8 0.8 1. 0.75 0.5 0.5 ] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.33333333 0.66666667 0.33333333 0.75 0.83333333 1. 0.75 0.5 0.5 ] mean value: 0.6416666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0. 0.5 0.4 0.75 0.66666667 1. 0.66666667 0.33333333 0.33333333] mean value: 0.5149999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.31 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08233285 0.0812242 0.08000469 0.08140969 0.07679605 0.0758462 0.07588482 0.07593584 0.07602239 0.0815146 ] mean value: 0.07869713306427002 key: score_time value: [0.01752472 0.01677775 0.01716328 0.01761603 0.01675701 0.01663923 0.01664209 0.01688862 0.01691008 0.01788044] mean value: 0.017079925537109374 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 0.61237244 0.40824829 0.57735027 0.57735027 0.57735027 -0.57735027] mean value: 0.19337396407417132 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 0.85714286 0.5 0.66666667 0.66666667 0.8 0.4 ] mean value: 0.5761904761904761 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 0.75 1. 1. 1. 0.66666667 0.33333333] mean value: 0.6416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 0.5 1. 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 0.8 0.6 0.75 0.75 0.75 0.25] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 0.75 0.66666667 0.75 0.75 0.75 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 0.75 0.33333333 0.5 0.5 0.66666667 0.25 ] mean value: 0.43999999999999995 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.15 MCC on Training: 0.19 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.12078643 0.11477804 0.10680938 0.1219449 0.11855173 0.10424113 0.11969233 0.11945939 0.1235249 0.10827184] mean value: 0.1158060073852539 key: score_time value: [0.00873613 0.0086627 0.00918221 0.00889516 0.0086627 0.00863004 0.00891042 0.00873637 0.00871038 0.0086832 ] mean value: 0.008780932426452637 key: test_mcc value: [0.61237244 0.61237244 0. 0.16666667 0.16666667 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.36983773027576633 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 0.57142857 0.66666667 0.66666667 0.5 0.66666667 0.5 0.8 0.8 ] mean value: 0.6504761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.4 0.66666667 0.66666667 1. 1. 0.5 0.66666667 0.66666667] mean value: 0.7566666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.4 0.6 0.6 0.6 0.75 0.5 0.75 0.75] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.58333333 0.58333333 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 0.4 0.5 0.5 0.33333333 0.5 0.33333333 0.66666667 0.66666667] mean value: 0.49000000000000005 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.48 MCC on Training: 0.37 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00905442 0.0086906 0.00810862 0.00923395 0.00944638 0.00882316 0.00911808 0.0086298 0.00889826 0.00855994] mean value: 0.00885632038116455 key: score_time value: [0.00911403 0.00840688 0.00894094 0.00904536 0.00929165 0.0090878 0.00932407 0.00891185 0.00899148 0.00882363] mean value: 0.008993768692016601 key: test_mcc value: [-0.16666667 0. 0. 0.61237244 0. 0.40824829 1. 1. 0. 0.57735027] mean value: 0.34313043286826167 key: train_mcc value: [0.57570364 0.698212 0.63496528 0.49692935 0.59982886 0.63994524 0.56652882 0.52704628 0.54659439 0.60609153] mean value: 0.5891845382894122 key: test_fscore value: [0.4 0.57142857 0.57142857 0.85714286 0.75 0.5 1. 1. 0.66666667 0.8 ] mean value: 0.7116666666666667 key: train_fscore value: [0.80851064 0.85714286 0.83333333 0.76595745 0.80851064 0.82608696 0.8 0.78431373 0.79166667 0.81632653] mean value: 0.8091848793171292 key: test_precision value: [0.33333333 0.4 0.4 0.75 0.6 1. 1. 1. 0.5 0.66666667] mean value: 0.665 key: train_precision value: [0.73076923 0.75 0.74074074 0.66666667 0.7037037 0.73076923 0.68965517 0.66666667 0.7037037 0.71428571] mean value: 0.7096960829719451 key: test_recall value: [0.5 1. 1. 1. 1. 0.33333333 1. 1. 1. 1. ] mean value: 0.8833333333333332 key: train_recall value: [0.9047619 1. 0.95238095 0.9 0.95 0.95 0.95238095 0.95238095 0.9047619 0.95238095] mean value: 0.9419047619047619 key: test_accuracy value: [0.4 0.4 0.4 0.8 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.645 key: train_accuracy value: [0.7804878 0.82926829 0.80487805 0.73170732 0.7804878 0.80487805 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779326364692218 key: test_roc_auc value: [0.41666667 0.5 0.5 0.75 0.5 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [0.77738095 0.825 0.80119048 0.73571429 0.78452381 0.80833333 0.76190476 0.73809524 0.76190476 0.78571429] mean value: 0.7779761904761904 key: test_jcc value: [0.25 0.4 0.4 0.75 0.6 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.5900000000000001 key: train_jcc value: [0.67857143 0.75 0.71428571 0.62068966 0.67857143 0.7037037 0.66666667 0.64516129 0.65517241 0.68965517] mean value: 0.6802477473500833 MCC on Blind test: -0.03 MCC on Training: 0.34 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01041269 0.01092553 0.01067853 0.01062799 0.01066828 0.01066041 0.01015639 0.01018453 0.01082611 0.01012492] mean value: 0.010526537895202637 key: score_time value: [0.00973082 0.00912237 0.00909185 0.00925374 0.00846124 0.00906968 0.00829339 0.0100174 0.00925136 0.00900269] mean value: 0.0091294527053833 key: test_mcc value: [ 0.16666667 -0.66666667 0.66666667 -0.40824829 1. 0.40824829 0.57735027 1. -0.57735027 -0.57735027] mean value: 0.15893163974770408 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.57142857 1. 0.5 0.66666667 1. 0.4 0.4 ] mean value: 0.5838095238095239 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.5 1. 1. 1. 1. 0.33333333 0.33333333] mean value: 0.6333333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.8 0.4 1. 0.6 0.75 1. 0.25 0.25] mean value: 0.585 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.16666667 0.83333333 0.33333333 1. 0.66666667 0.75 1. 0.25 0.25 ] mean value: 0.5833333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.4 1. 0.33333333 0.5 1. 0.25 0.25 ] mean value: 0.4733333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.16 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00851798 0.00857496 0.00890756 0.00856853 0.00912952 0.00901175 0.00886893 0.00944304 0.00859928 0.00860143] mean value: 0.008822298049926758 key: score_time value: [0.01027918 0.01006818 0.01002216 0.00937033 0.00983095 0.0098989 0.00986934 0.01035786 0.00985146 0.0096581 ] mean value: 0.009920644760131835 key: test_mcc value: [ 0.16666667 -0.16666667 0.40824829 -0.40824829 0.16666667 -0.61237244 0. 0.57735027 0. 0.57735027] mean value: 0.07089947693501238 key: train_mcc value: [0.36718832 0.56527676 0.56527676 0.56086079 0.56086079 0.52420964 0.43656413 0.43052839 0.4472136 0.47673129] mean value: 0.49347104597079217 key: test_fscore value: [0.5 0.4 0.66666667 0.57142857 0.66666667 0. 0.5 0.8 0.66666667 0.8 ] mean value: 0.5571428571428572 key: train_fscore value: [0.71111111 0.8 0.8 0.76923077 0.76923077 0.77272727 0.73913043 0.72727273 0.75 0.74418605] mean value: 0.7582889130866886 key: test_precision value: [0.5 0.33333333 0.5 0.5 0.66666667 0. 0.5 0.66666667 0.5 0.66666667] mean value: 0.4833333333333333 key: train_precision value: [0.66666667 0.75 0.75 0.78947368 0.78947368 0.70833333 0.68 0.69565217 0.66666667 0.72727273] mean value: 0.722353893627349 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [0.76190476 0.85714286 0.85714286 0.75 0.75 0.85 0.80952381 0.76190476 0.85714286 0.76190476] mean value: 0.8016666666666665 key: test_accuracy value: [0.6 0.4 0.6 0.4 0.6 0.2 0.5 0.75 0.5 0.75] mean value: 0.53 key: train_accuracy value: [0.68292683 0.7804878 0.7804878 0.7804878 0.7804878 0.75609756 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7441927990708479 key: test_roc_auc value: [0.58333333 0.41666667 0.66666667 0.33333333 0.58333333 0.25 0.5 0.75 0.5 0.75 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.68095238 0.77857143 0.77857143 0.7797619 0.7797619 0.75833333 0.71428571 0.71428571 0.71428571 0.73809524] mean value: 0.7436904761904762 key: test_jcc value: [0.33333333 0.25 0.5 0.4 0.5 0. 0.33333333 0.66666667 0.5 0.66666667] mean value: 0.41500000000000004 key: train_jcc value: [0.55172414 0.66666667 0.66666667 0.625 0.625 0.62962963 0.5862069 0.57142857 0.6 0.59259259] mean value: 0.6114915161466885 MCC on Blind test: 0.21 MCC on Training: 0.07 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01083231 0.01477337 0.01424289 0.01483798 0.01470923 0.01433825 0.01439548 0.01455998 0.01430869 0.01439524] mean value: 0.014139342308044433 key: score_time value: [0.01216412 0.01256561 0.01264334 0.01278043 0.01200223 0.01212287 0.01209044 0.01213431 0.0121243 0.01209593] mean value: 0.012272357940673828 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [ 0.16666667 -0.16666667 -0.61237244 -0.40824829 0.40824829 -0.16666667 -0.57735027 1. 1. 0.57735027] mean value: 0.12209608976375388 key: train_mcc value: [0.8547619 0.75714286 0.95227002 0.90238095 0.90238095 0.85441771 0.9047619 0.9047619 0.76277007 0.9047619 ] mean value: 0.8700410175391831 key: test_fscore value: [0.5 0.4 0.33333333 0.57142857 0.5 0.4 0.4 1. 1. 0.66666667] mean value: 0.5771428571428572 key: train_fscore value: [0.92682927 0.87804878 0.97674419 0.95 0.95 0.92307692 0.95238095 0.95238095 0.87804878 0.95238095] mean value: 0.9339890795534584 key: test_precision value: [0.5 0.33333333 0.25 0.5 1. 0.5 0.33333333 1. 1. 1. ] mean value: 0.6416666666666666 key: train_precision value: [0.95 0.9 0.95454545 0.95 0.95 0.94736842 0.95238095 0.95238095 0.9 0.95238095] mean value: 0.9409056732740944 key: test_recall value: [0.5 0.5 0.5 0.66666667 0.33333333 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.5833333333333333 key: train_recall value: [0.9047619 0.85714286 1. 0.95 0.95 0.9 0.95238095 0.95238095 0.85714286 0.95238095] mean value: 0.9276190476190477 key: test_accuracy value: [0.6 0.4 0.2 0.4 0.6 0.4 0.25 1. 1. 0.75] mean value: 0.5599999999999999 key: train_accuracy value: [0.92682927 0.87804878 0.97560976 0.95121951 0.95121951 0.92682927 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347851335656214 key: test_roc_auc value: [0.58333333 0.41666667 0.25 0.33333333 0.66666667 0.41666667 0.25 1. 1. 0.75 ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92738095 0.87857143 0.975 0.95119048 0.95119048 0.92619048 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347619047619047 key: test_jcc value: [0.33333333 0.25 0.2 0.4 0.33333333 0.25 0.25 1. 1. 0.5 ] mean value: 0.45166666666666666 key: train_jcc value: [0.86363636 0.7826087 0.95454545 0.9047619 0.9047619 0.85714286 0.90909091 0.90909091 0.7826087 0.90909091] mean value: 0.8777338603425558 MCC on Blind test: -0.3 MCC on Training: 0.12 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02299666 0.01810503 0.01486659 0.01721692 0.01725698 0.01778316 0.01558995 0.01684546 0.01661897 0.01729798] mean value: 0.01745777130126953 key: score_time value: [0.01176691 0.0097754 0.00930905 0.00952411 0.00942755 0.00944138 0.00940466 0.00945425 0.00946403 0.00965834] mean value: 0.009722566604614258 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.61237244 -0.61237244 1. 1. 0. 1. ] mean value: 0.09587585476806848 key: train_mcc value: [0.85441771 0.95238095 1. 0.90238095 0.90238095 0.90649828 0.85811633 0.85811633 0.85811633 0.9047619 ] mean value: 0.8997169740060625 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5595238095238095 key: train_fscore value: [0.93023256 0.97560976 1. 0.95 0.95 0.94736842 0.92682927 0.92682927 0.92682927 0.95238095] mean value: 0.9486079492548729 key: test_precision value: [0. 0.25 0.25 0.5 0.75 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.90909091 1. 1. 0.95 0.95 1. 0.95 0.95 0.95 0.95238095] mean value: 0.9611471861471861 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [0.95238095 0.95238095 1. 0.95 0.95 0.9 0.9047619 0.9047619 0.9047619 0.95238095] mean value: 0.9371428571428572 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.8 0.2 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.92682927 0.97560976 1. 0.95121951 0.95121951 0.95121951 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9494192799070849 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.75 0.25 1. 1. 0.5 1. ] mean value: 0.5666666666666667 key: train_roc_auc value: [0.92619048 0.97619048 1. 0.95119048 0.95119048 0.95 0.92857143 0.92857143 0.92857143 0.95238095] mean value: 0.9492857142857142 key: test_jcc value: [0. 0.2 0.2 0.4 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.4883333333333333 key: train_jcc value: [0.86956522 0.95238095 1. 0.9047619 0.9047619 0.9 0.86363636 0.86363636 0.86363636 0.90909091] mean value: 0.9031469979296066 MCC on Blind test: 0.03 MCC on Training: 0.1 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.16378284 0.17526865 0.15863681 0.1655252 0.18480325 0.16098142 0.17436695 0.17567134 0.16711688 0.18096042] mean value: 0.17071137428283692 key: score_time value: [0.00912428 0.00996947 0.00977707 0.0086298 0.01003218 0.00945187 0.00936747 0.0089004 0.00927258 0.00871253] mean value: 0.009323763847351074 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 0. ] mean value: 0.012542521434735132 key: train_mcc value: [0.41487884 1. 0.7098505 1. 1. 0.95227002 0.85811633 1. 1. 1. ] mean value: 0.8935115692085429 key: test_fscore value: [0. 0.33333333 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4904761904761905 key: train_fscore value: [0.72727273 1. 0.86363636 1. 1. 0.97435897 0.92682927 1. 1. 1. ] mean value: 0.9492097333560748 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0. 0.25 0.25 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4666666666666666 key: train_precision value: [0.69565217 1. 0.82608696 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.9471739130434782 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.5333333333333333 key: train_recall value: [0.76190476 1. 0.9047619 1. 1. 0.95 0.9047619 1. 1. 1. ] mean value: 0.9521428571428571 key: test_accuracy value: [0.4 0.2 0.2 0.4 0.6 0.4 1. 1. 0.5 0.5] mean value: 0.52 key: train_accuracy value: [0.70731707 1. 0.85365854 1. 1. 0.97560976 0.92857143 1. 1. 1. ] mean value: 0.9465156794425088 key: test_roc_auc value: [0.33333333 0.25 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 0.5 ] mean value: 0.525 key: train_roc_auc value: [0.70595238 1. 0.85238095 1. 1. 0.975 0.92857143 1. 1. 1. ] mean value: 0.9461904761904762 key: test_jcc value: [0. 0.2 0.2 0.4 0.5 0. 1. 1. 0.33333333 0.33333333] mean value: 0.39666666666666667 key: train_jcc value: [0.57142857 1. 0.76 1. 1. 0.95 0.86363636 1. 1. 1. ] mean value: 0.9145064935064935 MCC on Blind test: -0.05 MCC on Training: 0.01 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.24625897 0.23865318 0.21961713 0.24214768 0.24594498 0.24879456 0.26082468 0.30763769 0.29634738 0.27621984] mean value: 0.25824460983276365 key: score_time value: [0.01220822 0.01179051 0.01182485 0.011868 0.01182795 0.01183939 0.01282811 0.01191735 0.01189685 0.01186538] mean value: 0.011986660957336425 key: test_mcc value: [ 0. -0.61237244 -0.16666667 -0.40824829 0.16666667 0.40824829 1. 1. 0. 0.57735027] mean value: 0.19649778334938311 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.4 0.57142857 0.66666667 0.5 1. 1. 0.5 0.8 ] mean value: 0.5771428571428572 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.25 0.33333333 0.5 0.66666667 1. 1. 1. 0.5 0.66666667] mean value: 0.5916666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0.33333333 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.2 0.4 0.4 0.6 0.6 1. 1. 0.5 0.75] mean value: 0.6050000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.25 0.41666667 0.33333333 0.58333333 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.2 0.25 0.4 0.5 0.33333333 1. 1. 0.33333333 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.2 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01279354 0.01213837 0.00982642 0.00959229 0.00902486 0.00909352 0.00987792 0.00921607 0.00927114 0.00922585] mean value: 0.010005998611450195 key: score_time value: [0.01215696 0.0111835 0.00986505 0.00925684 0.00937867 0.00914979 0.00969934 0.00920773 0.00940967 0.00933027] mean value: 0.009863781929016113 key: test_mcc value: [-0.40824829 0. -0.61237244 -0.40824829 0.16666667 0. 1. 0.57735027 0.57735027 0. ] mean value: 0.08924981884223976 key: train_mcc value: [0.36515617 0.31666667 0.46623254 0.46300848 0.46300848 0.41428571 0.43052839 0.38138504 0.42857143 0.43052839] mean value: 0.415937128657245 key: test_fscore value: [0. 0.57142857 0.33333333 0.57142857 0.66666667 0. 1. 0.8 0.8 0.5 ] mean value: 0.5242857142857142 key: train_fscore value: [0.69767442 0.66666667 0.75555556 0.71794872 0.71794872 0.7 0.72727273 0.69767442 0.71428571 0.72727273] mean value: 0.712229966416013 key: test_precision value: [0. 0.4 0.25 0.5 0.66666667 0. 1. 0.66666667 0.66666667 0.5 ] mean value: 0.46499999999999997 key: train_precision value: [0.68181818 0.66666667 0.70833333 0.73684211 0.73684211 0.7 0.69565217 0.68181818 0.71428571 0.69565217] mean value: 0.701791063627448 key: test_recall value: [0. 1. 0.5 0.66666667 0.66666667 0. 1. 1. 1. 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [0.71428571 0.66666667 0.80952381 0.7 0.7 0.7 0.76190476 0.71428571 0.71428571 0.76190476] mean value: 0.7242857142857143 key: test_accuracy value: [0.4 0.4 0.2 0.4 0.6 0.4 1. 0.75 0.75 0.5 ] mean value: 0.54 key: train_accuracy value: [0.68292683 0.65853659 0.73170732 0.73170732 0.73170732 0.70731707 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7077235772357724 key: test_roc_auc value: [0.33333333 0.5 0.25 0.33333333 0.58333333 0.5 1. 0.75 0.75 0.5 ] mean value: 0.55 key: train_roc_auc value: [0.68214286 0.65833333 0.7297619 0.73095238 0.73095238 0.70714286 0.71428571 0.69047619 0.71428571 0.71428571] mean value: 0.7072619047619048 key: test_jcc value: [0. 0.4 0.2 0.4 0.5 0. 1. 0.66666667 0.66666667 0.33333333] mean value: 0.41666666666666663 key: train_jcc value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.53571429 0.5 0.60714286 0.56 0.56 0.53846154 0.57142857 0.53571429 0.55555556 0.57142857] mean value: 0.5535445665445665 MCC on Blind test: 0.23 MCC on Training: 0.09 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00941753 0.0086658 0.00904059 0.00928974 0.00901437 0.00918198 0.00941277 0.00908089 0.00972533 0.00922704] mean value: 0.00920560359954834 key: score_time value: [0.00908136 0.00835204 0.00908518 0.00907612 0.00931621 0.00917792 0.00910568 0.00933957 0.00910759 0.00913668] mean value: 0.009077835083007812 key: test_mcc value: [ 0.61237244 -0.16666667 -0.61237244 1. -0.61237244 0.40824829 0.57735027 0. 0.57735027 0.57735027] mean value: 0.2361259995670279 key: train_mcc value: [0.65952381 0.78072006 0.78072006 0.698212 0.65915306 0.81975606 0.67357531 0.78446454 0.81322028 0.78446454] mean value: 0.7453809730969173 key: test_fscore value: [0.66666667 0.4 0.33333333 1. 0. 0.5 0.66666667 0. 0.8 0.66666667] mean value: 0.5033333333333333 key: train_fscore value: [0.82926829 0.86486486 0.86486486 0.78787879 0.75 0.88888889 0.82051282 0.86486486 0.9 0.86486486] mean value: 0.8436008249422884 key: test_precision value: [1. 0.33333333 0.25 1. 0. 1. 1. 0. 0.66666667 1. ] mean value: 0.625 key: train_precision value: [0.85 1. 1. 1. 1. 1. 0.88888889 1. 0.94736842 1. ] mean value: 0.9686257309941521 key: test_recall value: [0.5 0.5 0.5 1. 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.76190476 0.76190476 0.65 0.6 0.8 0.76190476 0.76190476 0.85714286 0.76190476] mean value: 0.7526190476190475 key: test_accuracy value: [0.8 0.4 0.2 1. 0.2 0.6 0.75 0.5 0.75 0.75] mean value: 0.595 key: train_accuracy value: [0.82926829 0.87804878 0.87804878 0.82926829 0.80487805 0.90243902 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8621951219512196 key: test_roc_auc value: [0.75 0.41666667 0.25 1. 0.25 0.66666667 0.75 0.5 0.75 0.75 ] mean value: 0.6083333333333333 key: train_roc_auc value: [0.8297619 0.88095238 0.88095238 0.825 0.8 0.9 0.83333333 0.88095238 0.9047619 0.88095238] mean value: 0.8616666666666667 key: test_jcc value: [0.5 0.25 0.2 1. 0. 0.33333333 0.5 0. 0.66666667 0.5 ] mean value: 0.39499999999999996 key: train_jcc value: [0.70833333 0.76190476 0.76190476 0.65 0.6 0.8 0.69565217 0.76190476 0.81818182 0.76190476] mean value: 0.7319786373047242 MCC on Blind test: -0.07 MCC on Training: 0.24 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00879359 0.00986171 0.0087254 0.01002908 0.00985909 0.00996017 0.01025343 0.00930762 0.00952411 0.00843883] mean value: 0.009475302696228028 key: score_time value: [0.00916529 0.00899577 0.00936365 0.00893188 0.00941706 0.00886536 0.00964165 0.00923872 0.00833917 0.00856662] mean value: 0.009052515029907227 key: test_mcc value: [-0.66666667 -0.16666667 -0.61237244 -0.40824829 0.16666667 0. 1. 1. 0. 1. ] mean value: 0.13127126071736755 key: train_mcc value: [0.77831178 0.90692382 1. 0.74124932 0.95238095 0.95227002 0.80952381 0.81322028 0.8660254 0.78446454] mean value: 0.860436992906499 key: test_fscore value: [0. 0.4 0.33333333 0.57142857 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5471428571428572 key: train_fscore value: [0.89361702 0.95 1. 0.86956522 0.97560976 0.97435897 0.9047619 0.90909091 0.92307692 0.86486486] mean value: 0.9264945570919038 key: test_precision value: [0. 0.33333333 0.25 0.5 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.525 key: train_precision value: [0.80769231 1. 1. 0.76923077 0.95238095 1. 0.9047619 0.86956522 1. 1. ] mean value: 0.9303631151457239 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 0.9047619 1. 1. 1. 0.95 0.9047619 0.95238095 0.85714286 0.76190476] mean value: 0.9330952380952382 key: test_accuracy value: [0.2 0.4 0.2 0.4 0.6 0.4 1. 1. 0.5 1. ] mean value: 0.5700000000000001 key: train_accuracy value: [0.87804878 0.95121951 1. 0.85365854 0.97560976 0.97560976 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9253193960511034 key: test_roc_auc value: [0.16666667 0.41666667 0.25 0.33333333 0.58333333 0.5 1. 1. 0.5 1. ] mean value: 0.575 key: train_roc_auc value: [0.875 0.95238095 1. 0.85714286 0.97619048 0.975 0.9047619 0.9047619 0.92857143 0.88095238] mean value: 0.9254761904761905 key: test_jcc value: [0. 0.25 0.2 0.4 0.5 0. 1. 1. 0.33333333 1. ] mean value: 0.4683333333333334 key: train_jcc value: [0.80769231 0.9047619 1. 0.76923077 0.95238095 0.95 0.82608696 0.83333333 0.85714286 0.76190476] mean value: 0.8662533842968625 MCC on Blind test: 0.05 MCC on Training: 0.13 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.00943923 0.00943756 0.00967765 0.00893188 0.00952053 0.00971556 0.00848389 0.00952244 0.0095067 0.00899053] mean value: 0.009322595596313477 key: score_time value: [0.00993276 0.00933909 0.00935459 0.00928378 0.00878644 0.00932765 0.0086596 0.00952387 0.00939035 0.0092051 ] mean value: 0.00928032398223877 key: test_mcc value: [-0.40824829 -0.40824829 0. 0.16666667 0.66666667 0.40824829 -0.57735027 1. -0.57735027 0. ] mean value: 0.027038450449021856 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0.66666667 0.8 0.5 0.4 1. 0.4 0.5 ] mean value: 0.42666666666666664 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0.66666667 1. 1. 0.33333333 1. 0.33333333 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0.66666667 0.66666667 0.33333333 0.5 1. 0.5 0.5 ] mean value: 0.41666666666666663 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.6 0.6 0.8 0.6 0.25 1. 0.25 0.5 ] mean value: 0.54 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.33333333 0.5 0.58333333 0.83333333 0.66666667 0.25 1. 0.25 0.5 ] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0.5 0.66666667 0.33333333 0.25 1. 0.25 0.33333333] mean value: 0.33333333333333337 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.03 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.57225037 0.57406354 0.52002716 0.53686929 0.56386375 0.50304246 0.62644362 0.59339714 0.54040647 0.52413034] mean value: 0.5554494142532349 key: score_time value: [0.14558578 0.10611153 0.17744875 0.19189763 0.15916181 0.13559365 0.14123869 0.17529607 0.19755816 0.1636405 ] mean value: 0.15935325622558594 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 1. 0.40824829 0.57735027 1. 0.57735027 -0.57735027] mean value: 0.21522652263201553 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 1. 0.5 0.66666667 1. 0.8 0.4 ] mean value: 0.6104761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.5 0.5 1. 1. 1. 1. 0.66666667 0.33333333] mean value: 0.65 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0. 0.6 0.4 1. 0.6 0.75 1. 0.75 0.25] mean value: 0.595 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 1. 0.66666667 0.75 1. 0.75 0.25 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.5 0.4 1. 0.33333333 0.5 1. 0.66666667 0.25 ] mean value: 0.49833333333333335 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.22 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.83490586 0.88071322 0.81933165 0.91471887 0.91377974 0.83173299 0.8566606 0.86901212 0.86864448 0.85167694] mean value: 0.8641176462173462 key: score_time value: [0.19354987 0.21571326 0.1897068 0.17770934 0.17067981 0.20904517 0.17669272 0.19137979 0.2022543 0.21376586] mean value: 0.19404969215393067 key: test_mcc value: [ 0.16666667 -1. 0.40824829 -0.40824829 0.61237244 0.40824829 1. 1. 0.57735027 0. ] mean value: 0.276463766201595 key: train_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.7565654 0.90692382 0.8047619 0.90238095 0.8547619 0.85441771 0.9047619 0.80952381 0.81322028 0.80952381] mean value: 0.841684150259194 key: test_fscore value: [0.5 0. 0.66666667 0.57142857 0.85714286 0.5 1. 1. 0.8 0.5 ] mean value: 0.6395238095238095 key: train_fscore value: [0.88372093 0.95 0.9047619 0.95 0.92682927 0.92307692 0.95238095 0.9047619 0.9 0.9047619 ] mean value: 0.9200293788268832 key: test_precision value: [0.5 0. 0.5 0.5 0.75 1. 1. 1. 0.66666667 0.5 ] mean value: 0.6416666666666667 key: train_precision value: [0.86363636 1. 0.9047619 0.95 0.9047619 0.94736842 0.95238095 0.9047619 0.94736842 0.9047619 ] mean value: 0.9279801777170199 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 1. 1. 1. 0.5 ] mean value: 0.7 key: train_recall value: [0.9047619 0.9047619 0.9047619 0.95 0.95 0.9 0.95238095 0.9047619 0.85714286 0.9047619 ] mean value: 0.9133333333333334 key: test_accuracy value: [0.6 0. 0.6 0.4 0.8 0.6 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_accuracy value: [0.87804878 0.95121951 0.90243902 0.95121951 0.92682927 0.92682927 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203252032520325 key: test_roc_auc value: [0.58333333 0. 0.66666667 0.33333333 0.75 0.66666667 1. 1. 0.75 0.5 ] mean value: 0.625 key: train_roc_auc value: [0.87738095 0.95238095 0.90238095 0.95119048 0.92738095 0.92619048 0.95238095 0.9047619 0.9047619 0.9047619 ] mean value: 0.9203571428571428 key: test_jcc value: [0.33333333 0. 0.5 0.4 0.75 0.33333333 1. 1. 0.66666667 0.33333333] mean value: 0.5316666666666666 key: train_jcc value: [0.79166667 0.9047619 0.82608696 0.9047619 0.86363636 0.85714286 0.90909091 0.82608696 0.81818182 0.82608696] mean value: 0.8527503293807641 MCC on Blind test: -0.13 MCC on Training: 0.28 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02301264 0.00958657 0.01026487 0.00936556 0.0102005 0.00937605 0.01041985 0.00954866 0.00914311 0.00929427] mean value: 0.01102120876312256 key: score_time value: [0.01910353 0.00909686 0.00941515 0.0089817 0.00914288 0.00945878 0.00953722 0.00891304 0.00830054 0.00915599] mean value: 0.010110569000244141 key: test_mcc value: [-0.40824829 -0.16666667 -0.61237244 -0.66666667 0.61237244 0. 1. 1. 0. 1. ] mean value: 0.17584183762028038 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0. 1. 1. 0.5 1. ] mean value: 0.5423809523809524 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 0. 1. 1. 0.5 1. ] mean value: 0.5166666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0. 1. 1. 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.2 0.2 0.8 0.4 1. 1. 0.5 1. ] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.41666667 0.25 0.16666667 0.75 0.5 1. 1. 0.5 1. ] mean value: 0.5916666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0. 1. 1. 0.33333333 1. ] mean value: 0.47333333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.31 MCC on Training: 0.18 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.02582765 0.02451968 0.02460074 0.02604413 0.02447891 0.02503943 0.02485681 0.02496171 0.02501249 0.02538705] mean value: 0.025072860717773437 key: score_time value: [0.00885057 0.00860453 0.00849152 0.00858569 0.0084908 0.00919199 0.00886202 0.00870347 0.00899339 0.00876284] mean value: 0.008753681182861328 key: test_mcc value: [ 0. -0.16666667 -0.61237244 -0.66666667 0.61237244 0.40824829 1. 1. 0. 0.57735027] mean value: 0.21522652263201558 key: train_mcc value: [1. 1. 0.8547619 1. 1. 1. 0.85811633 1. 1. 1. ] mean value: 0.9712878235082938 key: test_fscore value: [0. 0.4 0.33333333 0.33333333 0.85714286 0.5 1. 1. 0.5 0.66666667] mean value: 0.5590476190476191 key: train_fscore value: [1. 1. 0.92682927 1. 1. 1. 0.92682927 1. 1. 1. ] mean value: 0.9853658536585366 key: test_precision value: [0. 0.33333333 0.25 0.33333333 0.75 1. 1. 1. 0.5 1. ] mean value: 0.6166666666666666 key: train_precision value: [1. 1. 0.95 1. 1. 1. 0.95 1. 1. 1. ] mean value: 0.99 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0.33333333 1. 1. 0.5 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 0.9047619 1. 1. 1. 0.9047619 1. 1. 1. ] mean value: 0.980952380952381 key: test_accuracy value: [0.6 0.4 0.2 0.2 0.8 0.6 1. 1. 0.5 0.75] mean value: 0.605 key: train_accuracy value: [1. 1. 0.92682927 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855400696864113 key: test_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.5 0.41666667 0.25 0.16666667 0.75 0.66666667 1. 1. 0.5 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 0.92738095 1. 1. 1. 0.92857143 1. 1. 1. ] mean value: 0.9855952380952381 key: test_jcc value: [0. 0.25 0.2 0.2 0.75 0.33333333 1. 1. 0.33333333 0.5 ] mean value: 0.45666666666666667 key: train_jcc value: [1. 1. 0.86363636 1. 1. 1. 0.86363636 1. 1. 1. ] mean value: 0.9727272727272727 MCC on Blind test: -0.31 MCC on Training: 0.22 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.0140388 0.00950265 0.00924253 0.00973916 0.00953579 0.00926042 0.00978374 0.00953698 0.00996804 0.00947928] mean value: 0.010008740425109863 key: score_time value: [0.01185226 0.00954533 0.00921082 0.00965381 0.01047969 0.01009488 0.00931334 0.00929523 0.00941062 0.00950694] mean value: 0.009836292266845703 key: test_mcc value: [-0.40824829 -0.16666667 -0.16666667 -0.40824829 0.16666667 0. 1. 0.57735027 0. 0. ] mean value: 0.059418702159523294 key: train_mcc value: [0.65952381 0.8547619 0.75714286 0.7633652 0.65871309 0.85441771 0.76980036 0.62187434 0.71428571 0.71754731] mean value: 0.7371432287703027 key: test_fscore value: [0. 0.4 0.4 0.57142857 0.66666667 0. 1. 0.66666667 0.5 0.5 ] mean value: 0.4704761904761904 key: train_fscore value: [0.82926829 0.92682927 0.87804878 0.86486486 0.82051282 0.92307692 0.87179487 0.8 0.85714286 0.86363636] mean value: 0.8635175042492115 key: test_precision value: [0. 0.33333333 0.33333333 0.5 0.66666667 0. 1. 1. 0.5 0.5 ] mean value: 0.4833333333333333 key: train_precision value: [0.85 0.95 0.9 0.94117647 0.84210526 0.94736842 0.94444444 0.84210526 0.85714286 0.82608696] mean value: 0.8900429676065696 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 0.5 0.5 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.80952381 0.9047619 0.85714286 0.8 0.8 0.9 0.80952381 0.76190476 0.85714286 0.9047619 ] mean value: 0.8404761904761904 key: test_accuracy value: [0.4 0.4 0.4 0.4 0.6 0.4 1. 0.75 0.5 0.5 ] mean value: 0.5349999999999999 key: train_accuracy value: [0.82926829 0.92682927 0.87804878 0.87804878 0.82926829 0.92682927 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8673054587688733 key: test_roc_auc value: [0.33333333 0.41666667 0.41666667 0.33333333 0.58333333 0.5 1. 0.75 0.5 0.5 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.8297619 0.92738095 0.87857143 0.87619048 0.82857143 0.92619048 0.88095238 0.80952381 0.85714286 0.85714286] mean value: 0.8671428571428572 key: test_jcc value: [0. 0.25 0.25 0.4 0.5 0. 1. 0.5 0.33333333 0.33333333] mean value: 0.3566666666666667 key: train_jcc value: [0.70833333 0.86363636 0.7826087 0.76190476 0.69565217 0.85714286 0.77272727 0.66666667 0.75 0.76 ] mean value: 0.7618672124976473 MCC on Blind test: 0.12 MCC on Training: 0.06 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00907588 0.00938797 0.00921416 0.00959897 0.00976229 0.0095582 0.00840521 0.00849414 0.00907922 0.00890398] mean value: 0.009148001670837402 key: score_time value: [0.00901389 0.00934815 0.00934172 0.00924611 0.00913692 0.00936723 0.00833535 0.00881696 0.00879908 0.00887227] mean value: 0.009027767181396484 key: test_mcc value: [-0.40824829 -0.61237244 -0.61237244 0.16666667 0. 0. 1. 0.57735027 0. 0.57735027] mean value: 0.0688374043190466 key: train_mcc value: [0.90649828 1. 1. 0.95227002 0.70272837 1. 0.55901699 0.40824829 0.36760731 0.8660254 ] mean value: 0.7762394663113877 key: test_fscore value: [0. 0.33333333 0.33333333 0.66666667 0.75 0. 1. 0.66666667 0.66666667 0.66666667] mean value: 0.5083333333333334 key: train_fscore value: [0.95454545 1. 1. 0.97435897 0.85106383 1. 0.64516129 0.44444444 0.72413793 0.92307692] mean value: 0.8516788847570094 key: test_precision value: [0. 0.25 0.25 0.66666667 0.6 0. 1. 1. 0.5 1. ] mean value: 0.5266666666666666 key: train_precision value: [0.91304348 1. 1. 1. 0.74074074 1. 1. 1. 0.56756757 1. ] mean value: 0.9221351786569176 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 0.5 1. 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 0.95 1. 1. 0.47619048 0.28571429 1. 0.85714286] mean value: 0.856904761904762 key: test_accuracy value: [0.4 0.2 0.2 0.6 0.6 0.4 1. 0.75 0.5 0.75] mean value: 0.54 key: train_accuracy value: [0.95121951 1. 1. 0.97560976 0.82926829 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8684668989547039 key: test_roc_auc value: [0.33333333 0.25 0.25 0.58333333 0.5 0.5 1. 0.75 0.5 0.75 ] mean value: 0.5416666666666666 key: train_roc_auc value: [0.95 1. 1. 0.975 0.83333333 1. 0.73809524 0.64285714 0.61904762 0.92857143] mean value: 0.8686904761904761 key: test_jcc value: [0. 0.2 0.2 0.5 0.6 0. 1. 0.5 0.5 0.5] mean value: 0.4 key: train_jcc value: [0.91304348 1. 1. 0.95 0.74074074 1. 0.47619048 0.28571429 0.56756757 0.85714286] mean value: 0.7790399405616796 MCC on Blind test: 0.0 MCC on Training: 0.07 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.05605292 0.03590488 0.03413749 0.04259133 0.0376277 0.03950238 0.03527665 0.03697419 0.03608084 0.04768467] mean value: 0.040183305740356445 key: score_time value: [0.01017642 0.01047134 0.00995302 0.010849 0.01102209 0.01037669 0.01024103 0.0103929 0.01064944 0.01044631] mean value: 0.010457825660705567 key: test_mcc value: [ 0.61237244 -0.16666667 0. -0.66666667 0.66666667 0.40824829 0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.31633551362514944 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.4 0.57142857 0.33333333 0.8 0.5 0.66666667 0.66666667 0.8 0.8 ] mean value: 0.6204761904761904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.33333333 0.4 0.33333333 1. 1. 1. 1. 0.66666667 0.66666667] mean value: 0.74 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.33333333 0.66666667 0.33333333 0.5 0.5 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.4 0.2 0.8 0.6 0.75 0.75 0.75 0.75] mean value: 0.62 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.41666667 0.5 0.16666667 0.83333333 0.66666667 0.75 0.75 0.75 0.75 ] mean value: 0.6333333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.25 0.4 0.2 0.66666667 0.33333333 0.5 0.5 0.66666667 0.66666667] mean value: 0.4683333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.32 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... loky_pN8ZN8ZBuilding estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... <(??BF??.=? ?"?>? ?UU5?ى=?;?ى?K.>?] ???ä=U?>E>j+)?0??!? >j?>>\G?ㄔ=a?U=/?\?GX>= >>J@>?>??>>??=Z+?]x ?L(?a#?>D-?8p=1bD?9&?H7?[=a2?iE?#0?Ës=6!(?:??m2>;7?5i?;[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)...  `[`[Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... `ff?0VGzUPzU[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... 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Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.07245159 0.0669992 0.06665778 0.06658888 0.06635976 0.06793022 0.06691575 0.06897569 0.07255554 0.07085109] mean value: 0.06862854957580566 key: score_time value: [0.01423645 0.01433563 0.01421928 0.01416278 0.01423764 0.01449394 0.01425385 0.0149219 0.01485515 0.01411796] mean value: 0.014383459091186523 key: test_mcc value: [ 0.16666667 -0.40824829 1. -0.16666667 0.16666667 -0.16666667 0. 0.57735027 0.57735027 0.57735027] mean value: 0.23238025171050145 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 1. 0.4 0.66666667 0.4 0.5 0.8 0.8 0.8 ] mean value: 0.5866666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 1. 0.5 0.66666667 0.5 0.5 0.66666667 0.66666667 0.66666667] mean value: 0.5666666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.33333333 0.66666667 0.33333333 0.5 1. 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 1. 0.4 0.6 0.4 0.5 0.75 0.75 0.75] mean value: 0.615 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.33333333 1. 0.41666667 0.58333333 0.41666667 0.5 0.75 0.75 0.75 ] mean value: 0.6083333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 1. 0.25 0.5 0.25 0.33333333 0.66666667 0.66666667 0.66666667] mean value: 0.4666666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.23 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.07599735 0.12595415 0.09812045 0.10985875 0.11749601 0.13445973 0.14749622 0.1326468 0.11447096 0.10273337] mean value: 0.11592338085174561 key: score_time value: [0.07129121 0.04892468 0.05861712 0.042099 0.05203485 0.06753087 0.06974196 0.03904748 0.04094124 0.05680799] mean value: 0.05470364093780518 key: test_mcc value: [ 0.61237244 0.16666667 0.66666667 0.16666667 -0.40824829 -0.16666667 1. 0. 1. 0.57735027] mean value: 0.361480774775489 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.5 0.8 0.66666667 0.57142857 0.4 1. 0.5 1. 0.8 ] mean value: 0.6904761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.66666667 0.66666667 0.5 0.5 1. 0.5 1. 0.66666667] mean value: 0.7 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0.33333333 1. 0.5 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.6 0.8 0.6 0.4 0.4 1. 0.5 1. 0.75] mean value: 0.6849999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.58333333 0.83333333 0.58333333 0.33333333 0.41666667 1. 0.5 1. 0.75 ] mean value: 0.675 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.33333333 0.66666667 0.5 0.4 0.25 1. 0.33333333 1. 0.66666667] mean value: 0.5650000000000001 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.36 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0131309 0.00861692 0.00911331 0.00908542 0.00874877 0.00875521 0.0089221 0.00927711 0.0088098 0.0088284 ] mean value: 0.009328794479370118 key: score_time value: [0.01178646 0.00842476 0.008214 0.00878119 0.00845742 0.00824523 0.00835514 0.00882697 0.00840044 0.00872993] mean value: 0.008822154998779298 key: test_mcc value: [ 0.16666667 0.16666667 0.66666667 0.16666667 0.61237244 -0.16666667 0.57735027 -0.57735027 1. 0.57735027] mean value: 0.31897227048854204 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.5 0.8 0.66666667 0.85714286 0.4 0.66666667 0. 1. 0.8 ] mean value: 0.6190476190476191 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) test_precision value: [0.5 0.5 0.66666667 0.66666667 0.75 0.5 1. 0. 1. 0.66666667] mean value: 0.625 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 1. 0.33333333 0.5 0. 1. 1. ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.6 0.8 0.6 0.8 0.4 0.75 0.25 1. 0.75] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.58333333 0.83333333 0.58333333 0.75 0.41666667 0.75 0.25 1. 0.75 ] mean value: 0.65 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.33333333 0.66666667 0.5 0.75 0.25 0.5 0. 1. 0.66666667] mean value: 0.5 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.32 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00942349 0.00944328 0.00863814 0.00854158 0.00874162 0.00933552 0.00931382 0.00919151 0.00877237 0.00829053] mean value: 0.00896918773651123 key: score_time value: [0.0095613 0.00939584 0.00906587 0.00885415 0.00848174 0.00931859 0.00943851 0.00876904 0.00887609 0.00850272] mean value: 0.009026384353637696 key: test_mcc value: [ 0.61237244 -0.40824829 0. 0.16666667 0.61237244 0. 0.57735027 -0.57735027 0.57735027 -1. ] mean value: 0.05605135167840185 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0. 0.57142857 0.66666667 0.85714286 0. 0.8 0. 0.66666667 0. ] mean value: 0.4228571428571429 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0. 0.4 0.66666667 0.75 0. 0.66666667 0. 1. 0. ] mean value: 0.44833333333333325 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0. 1. 0. 0.5 0. ] mean value: 0.4666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.4 0.4 0.6 0.8 0.4 0.75 0.25 0.75 0. ] mean value: 0.515 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.33333333 0.5 0.58333333 0.75 0.5 0.75 0.25 0.75 0. ] mean value: 0.5166666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0. 0.4 0.5 0.75 0. 0.66666667 0. 0.5 0. ] mean value: 0.33166666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.06 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08723235 0.08638859 0.08226252 0.08310246 0.08300376 0.08438563 0.08477879 0.08323193 0.08433032 0.08245111] mean value: 0.08411674499511719 key: score_time value: [0.01899314 0.01917386 0.01922822 0.01843333 0.01810694 0.01819396 0.01848578 0.01838779 0.01798558 0.01785612] mean value: 0.01848447322845459 key: test_mcc value: [ 0.16666667 -0.40824829 0.40824829 0.16666667 0.61237244 -0.16666667 0.57735027 -0.57735027 1. 0. ] mean value: 0.17790391023624613 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.66666667 0.66666667 0.85714286 0.4 0.66666667 0.4 1. 0.5 ] mean value: 0.5657142857142856 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.5 0.66666667 0.75 0.5 1. 0.33333333 1. 0.5 ] mean value: 0.575 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.33333333 0.5 0.5 1. 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.6 0.6 0.8 0.4 0.75 0.25 1. 0.5 ] mean value: 0.5900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.33333333 0.66666667 0.58333333 0.75 0.41666667 0.75 0.25 1. 0.5 ] mean value: 0.5833333333333334 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.5 0.5 0.75 0.25 0.5 0.25 1. 0.33333333] mean value: 0.4416666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.15 MCC on Training: 0.18 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.11713099 0.10202193 0.1162889 0.11519527 0.11763167 0.1037972 0.11975336 0.12369323 0.11843276 0.10948038] mean value: 0.1143425703048706 key: score_time value: [0.00872087 0.00855494 0.00870824 0.00866985 0.00876188 0.00862122 0.00891376 0.00888658 0.00862932 0.0086391 ] mean value: 0.008710575103759766 key: test_mcc value: [ 0.61237244 0.16666667 0.66666667 0.16666667 1. 0.40824829 0.57735027 -0.57735027 1. 0.57735027] mean value: 0.4597970995349284 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.5 0.8 0.66666667 1. 0.5 0.66666667 0.4 1. 0.8 ] mean value: 0.7 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.66666667 0.66666667 1. 1. 1. 0.33333333 1. 0.66666667] mean value: 0.7833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 1. 0.33333333 0.5 0.5 1. 1. ] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.6 0.8 0.6 1. 0.6 0.75 0.25 1. 0.75] mean value: 0.7150000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.58333333 0.83333333 0.58333333 1. 0.66666667 0.75 0.25 1. 0.75 ] mean value: 0.7166666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.33333333 0.66666667 0.5 1. 0.33333333 0.5 0.25 1. 0.66666667] mean value: 0.575 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.48 MCC on Training: 0.46 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00803995 0.00790071 0.0081284 0.0079 0.00790191 0.00790572 0.00787449 0.00792575 0.00795746 0.008219 ] mean value: 0.007975339889526367 key: score_time value: [0.00834203 0.00825572 0.00831127 0.00825071 0.00827694 0.0083003 0.0082624 0.00830865 0.00831676 0.00827122] mean value: 0.008289599418640136 key: test_mcc value: [-0.16666667 0.66666667 0.40824829 0.61237244 0. -0.66666667 1. 1. 0. 0.57735027] mean value: 0.34313043286826167 key: train_mcc value: [0.63496528 0.48849265 0.59335232 0.52052166 0.62325386 0.6806903 0.52704628 0.56652882 0.56652882 0.60609153] mean value: 0.5807471523766669 key: test_fscore value: [0.4 0.8 0.66666667 0.85714286 0.75 0.33333333 1. 1. 0.66666667 0.8 ] mean value: 0.7273809523809524 key: train_fscore value: [0.83333333 0.7755102 0.81632653 0.7755102 0.81818182 0.84444444 0.78431373 0.8 0.8 0.81632653] mean value: 0.8063946790837548 key: test_precision value: [0.33333333 0.66666667 0.5 0.75 0.6 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.635 key: train_precision value: [0.74074074 0.67857143 0.71428571 0.65517241 0.75 0.76 0.66666667 0.68965517 0.68965517 0.71428571] mean value: 0.7059033023170953 key: test_recall value: [0.5 1. 1. 1. 1. 0.33333333 1. 1. 1. 1. ] mean value: 0.8833333333333332 key: train_recall value: [0.95238095 0.9047619 0.95238095 0.95 0.9 0.95 0.95238095 0.95238095 0.95238095 0.95238095] mean value: 0.9419047619047619 key: test_accuracy value: [0.4 0.8 0.6 0.8 0.6 0.2 1. 1. 0.5 0.75] mean value: 0.665 key: train_accuracy value: [0.80487805 0.73170732 0.7804878 0.73170732 0.80487805 0.82926829 0.73809524 0.76190476 0.76190476 0.78571429] mean value: 0.773054587688734 key: test_roc_auc value: [0.41666667 0.83333333 0.66666667 0.75 0.5 0.16666667 1. 1. 0.5 0.75 ] mean value: 0.6583333333333334 key: train_roc_auc value: [0.80119048 0.72738095 0.77619048 0.73690476 0.80714286 0.83214286 0.73809524 0.76190476 0.76190476 0.78571429] mean value: 0.7728571428571428 key: test_jcc value: [0.25 0.66666667 0.5 0.75 0.6 0.2 1. 1. 0.5 0.66666667] mean value: 0.6133333333333334 key: train_jcc value: [0.71428571 0.63333333 0.68965517 0.63333333 0.69230769 0.73076923 0.64516129 0.66666667 0.66666667 0.68965517] mean value: 0.6761834272512803 MCC on Blind test: -0.03 MCC on Training: 0.34 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.00970507 0.00996232 0.00973463 0.0096488 0.01098037 0.01389408 0.01121068 0.011127 0.01116967 0.01130724] mean value: 0.010873985290527344 key: score_time value: [0.00843453 0.00850296 0.0084548 0.00932336 0.00942469 0.01070094 0.00947523 0.00959635 0.0094049 0.00951791] mean value: 0.00928356647491455 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [ 0.16666667 -0.40824829 0.66666667 0.16666667 0.61237244 -0.40824829 0.57735027 0. 0.57735027 0. ] mean value: 0.195057639314732 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0. 0.8 0.66666667 0.85714286 0.57142857 0.66666667 0.66666667 0.66666667 0.5 ] mean value: 0.5895238095238096 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.66666667 0.66666667 0.75 0.5 1. 0.5 1. 0.5 ] mean value: 0.6083333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0. 1. 0.66666667 1. 0.66666667 0.5 1. 0.5 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.8 0.6 0.8 0.4 0.75 0.5 0.75 0.5 ] mean value: 0.61 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.33333333 0.83333333 0.58333333 0.75 0.33333333 0.75 0.5 0.75 0.5 ] mean value: 0.5916666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0. 0.66666667 0.5 0.75 0.4 0.5 0.5 0.5 0.33333333] mean value: 0.44833333333333325 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.2 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00864244 0.00888777 0.00833511 0.00815058 0.0082531 0.00774908 0.00829196 0.00880671 0.00760126 0.00764751] mean value: 0.008236551284790039 key: score_time value: [0.01113439 0.00993228 0.01610231 0.01439691 0.01413369 0.01326632 0.01413822 0.0090127 0.00882292 0.00868225] mean value: 0.011962199211120605 key: test_mcc value: [ 0.16666667 -0.16666667 0.40824829 0.16666667 -0.40824829 -1. -0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.032136720504591834 key: train_mcc value: [0.46300848 0.41963703 0.41963703 0.47003614 0.51551459 0.53864117 0.52620136 0.57207755 0.43052839 0.57207755] mean value: 0.4927359304493568 key: test_fscore value: [0.5 0.4 0.66666667 0.66666667 0.57142857 0. 0.4 0.8 0.8 0.8 ] mean value: 0.5604761904761905 key: train_fscore value: [0.74418605 0.73913043 0.73913043 0.74418605 0.76190476 0.7826087 0.77272727 0.79069767 0.72727273 0.79069767] mean value: 0.7592541768982618 key: test_precision value: [0.5 0.33333333 0.5 0.66666667 0.5 0. 0.33333333 0.66666667 0.66666667 0.66666667] mean value: 0.4833333333333334 key: train_precision value: [0.72727273 0.68 0.68 0.69565217 0.72727273 0.69230769 0.73913043 0.77272727 0.69565217 0.77272727] mean value: 0.7182742474916387 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [0.76190476 0.80952381 0.80952381 0.8 0.8 0.9 0.80952381 0.80952381 0.76190476 0.80952381] mean value: 0.8071428571428572 key: test_accuracy value: [0.6 0.4 0.6 0.6 0.4 0. 0.25 0.75 0.75 0.75] mean value: 0.51 key: train_accuracy value: [0.73170732 0.70731707 0.70731707 0.73170732 0.75609756 0.75609756 0.76190476 0.78571429 0.71428571 0.78571429] mean value: 0.7437862950058072 key: test_roc_auc value: [0.58333333 0.41666667 0.66666667 0.58333333 0.33333333 0. 0.25 0.75 0.75 0.75 ] mean value: 0.5083333333333333 key: train_roc_auc value: [0.73095238 0.7047619 0.7047619 0.73333333 0.75714286 0.75952381 0.76190476 0.78571429 0.71428571 0.78571429] mean value: 0.7438095238095237 key: test_jcc value: [0.33333333 0.25 0.5 0.5 0.4 0. 0.25 0.66666667 0.66666667 0.66666667] mean value: 0.42333333333333334 key: train_jcc value: [0.59259259 0.5862069 0.5862069 0.59259259 0.61538462 0.64285714 0.62962963 0.65384615 0.57142857 0.65384615] mean value: 0.6124591245280901 MCC on Blind test: 0.21 MCC on Training: 0.03 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01010442 0.01632833 0.0218389 0.01394343 0.01382661 0.0138669 0.01395917 0.01387024 0.0142827 0.01382041] mean value: 0.014584112167358398 key: score_time value: [0.01163912 0.0207212 0.01156855 0.01176786 0.01140428 0.0114646 0.01178265 0.01222563 0.01182961 0.01165009] mean value: 0.01260535717010498 key: test_mcc value: [ 0.16666667 0.16666667 0.40824829 0.66666667 -0.66666667 -0.16666667 0.57735027 0. 0.57735027 0.57735027] mean value: 0.23069657646994068 key: train_mcc value: [0.8547619 0.8047619 0.8547619 0.90238095 0.90238095 0.8547619 0.9047619 0.9047619 0.9047619 0.80952381] mean value: 0.8697619047619047 key: test_fscore value: [0.5 0.5 0.66666667 0.8 0.33333333 0.4 0.66666667 0. 0.8 0.8 ] mean value: 0.5466666666666666 key: train_fscore value: [0.92682927 0.9047619 0.92682927 0.95 0.95 0.92682927 0.95238095 0.95238095 0.95238095 0.9047619 ] mean value: 0.9347154471544716 key: test_precision value: [0.5 0.5 0.5 1. 0.33333333 0.5 1. 0. 0.66666667 0.66666667] mean value: 0.5666666666666667 key: train_precision value: [0.95 0.9047619 0.95 0.95 0.95 0.9047619 0.95238095 0.95238095 0.95238095 0.9047619 ] mean value: 0.9371428571428572 key: test_recall value: [0.5 0.5 1. 0.66666667 0.33333333 0.33333333 0.5 0. 1. 1. ] mean value: 0.5833333333333333 key: train_recall value: [0.9047619 0.9047619 0.9047619 0.95 0.95 0.95 0.95238095 0.95238095 0.95238095 0.9047619 ] mean value: 0.9326190476190476 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.6 0.6 0.6 0.8 0.2 0.4 0.75 0.5 0.75 0.75] mean value: 0.595 key: train_accuracy value: [0.92682927 0.90243902 0.92682927 0.95121951 0.95121951 0.92682927 0.95238095 0.95238095 0.95238095 0.9047619 ] mean value: 0.9347270615563298 key: test_roc_auc value: [0.58333333 0.58333333 0.66666667 0.83333333 0.16666667 0.41666667 0.75 0.5 0.75 0.75 ] mean value: 0.6 key: train_roc_auc value: [0.92738095 0.90238095 0.92738095 0.95119048 0.95119048 0.92738095 0.95238095 0.95238095 0.95238095 0.9047619 ] mean value: 0.9348809523809523 key: test_jcc value: [0.33333333 0.33333333 0.5 0.66666667 0.2 0.25 0.5 0. 0.66666667 0.66666667] mean value: 0.4116666666666666 key: train_jcc value: [0.86363636 0.82608696 0.86363636 0.9047619 0.9047619 0.86363636 0.90909091 0.90909091 0.90909091 0.82608696] mean value: 0.8779879540749105 MCC on Blind test: -0.3 MCC on Training: 0.23 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02203345 0.0165832 0.0163517 0.01525331 0.01444888 0.0147624 0.01452589 0.0144136 0.01507235 0.01586056] mean value: 0.015930533409118652 key: score_time value: [0.01171517 0.00906587 0.00875568 0.00842404 0.00843978 0.00846004 0.0083797 0.0084908 0.00874519 0.00894594] mean value: 0.008942222595214844 key: test_mcc value: [-0.66666667 0.61237244 -0.16666667 0.66666667 1. -0.61237244 1. 0.57735027 1. 0.57735027] mean value: 0.3988033871712585 key: train_mcc value: [0.95238095 0.95238095 0.90238095 0.95238095 0.85441771 0.90649828 0.85811633 0.9047619 0.85811633 0.90889326] mean value: 0.905032762126693 key: test_fscore value: [0. 0.66666667 0.4 0.8 1. 0. 1. 0.8 1. 0.8 ] mean value: 0.6466666666666666 key: train_fscore value: [0.97560976 0.97560976 0.95238095 0.97560976 0.92307692 0.94736842 0.92682927 0.95238095 0.92682927 0.95 ] mean value: 0.9505695053769507 key: test_precision value: [0. 1. 0.33333333 1. 1. 0. 1. 0.66666667 1. 0.66666667] mean value: 0.6666666666666667 key: train_precision value: [1. 1. 0.95238095 0.95238095 0.94736842 1. 0.95 0.95238095 0.95 1. ] mean value: 0.9704511278195488 key: test_recall value: [0. 0.5 0.5 0.66666667 1. 0. 1. 1. 1. 1. ] mean value: 0.6666666666666666 key: train_recall value: [0.95238095 0.95238095 0.95238095 1. 0.9 0.9 0.9047619 0.95238095 0.9047619 0.9047619 ] mean value: 0.9323809523809524 key: test_accuracy value: [0.2 0.8 0.4 0.8 1. 0.2 1. 0.75 1. 0.75] mean value: 0.6900000000000001 key: train_accuracy value: [0.97560976 0.97560976 0.95121951 0.97560976 0.92682927 0.95121951 0.92857143 0.95238095 0.92857143 0.95238095] mean value: 0.9518002322880372 key: test_roc_auc value: [0.16666667 0.75 0.41666667 0.83333333 1. 0.25 1. 0.75 1. 0.75 ] mean value: 0.6916666666666667 key: train_roc_auc value: [0.97619048 0.97619048 0.95119048 0.97619048 0.92619048 0.95 0.92857143 0.95238095 0.92857143 0.95238095] mean value: 0.9517857142857142 key: test_jcc value: [0. 0.5 0.25 0.66666667 1. 0. 1. 0.66666667 1. 0.66666667] mean value: 0.575 key: train_jcc value: [0.95238095 0.95238095 0.90909091 0.95238095 0.85714286 0.9 0.86363636 0.90909091 0.86363636 0.9047619 ] mean value: 0.9064502164502164 MCC on Blind test: 0.03 MCC on Training: 0.4 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.14964604 0.16165519 0.15029597 0.16086483 0.16988111 0.17450547 0.17299223 0.17471814 0.17008519 0.16893172] mean value: 0.1653575897216797 key: score_time value: [0.00932193 0.00876379 0.00847268 0.00862241 0.00974536 0.00944757 0.00913954 0.00863385 0.00874805 0.00911403] mean value: 0.009000921249389648 key: test_mcc value: [-0.40824829 0. -0.16666667 0.16666667 0.16666667 -0.16666667 1. 0.57735027 1. 0.57735027] mean value: 0.27464522479153886 key: train_mcc value: [0.46428571 0. 0.8047619 1. 0.7565654 0.95227002 1. 1. 0.80952381 1. ] mean value: 0.7787406844289751 key: test_fscore value: [0. 0.57142857 0.4 0.66666667 0.66666667 0.4 1. 0.8 1. 0.8 ] mean value: 0.6304761904761904 key: train_fscore value: [0.73170732 0.67741935 0.9047619 1. 0.87179487 0.97435897 1. 1. 0.9047619 1. ] mean value: 0.9064804327589536 key: test_precision value: [0. 0.4 0.33333333 0.66666667 0.66666667 0.5 1. 0.66666667 1. 0.66666667] mean value: 0.59 key: train_precision value: [0.75 0.51219512 0.9047619 1. 0.89473684 1. 1. 1. 0.9047619 1. ] mean value: 0.8966455773580293 key: test_recall value: [0. 1. 0.5 0.66666667 0.66666667 0.33333333 1. 1. 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [0.71428571 1. 0.9047619 1. 0.85 0.95 1. 1. 0.9047619 1. ] mean value: 0.9323809523809524 key: test_accuracy value: [0.4 0.4 0.4 0.6 0.6 0.4 1. 0.75 1. 0.75] mean value: 0.63 key: train_accuracy value: [0.73170732 0.51219512 0.90243902 1. 0.87804878 0.97560976 1. 1. 0.9047619 1. ] mean value: 0.8904761904761905 key: test_roc_auc value: [0.33333333 0.5 0.41666667 0.58333333 0.58333333 0.41666667 1. 0.75 1. 0.75 ] mean value: 0.6333333333333333 key: train_roc_auc value: [0.73214286 0.5 0.90238095 1. 0.87738095 0.975 1. 1. 0.9047619 1. ] mean value: 0.8891666666666668 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0. 0.4 0.25 0.5 0.5 0.25 1. 0.66666667 1. 0.66666667] mean value: 0.5233333333333333 key: train_jcc value: [0.57692308 0.51219512 0.82608696 1. 0.77272727 0.95 1. 1. 0.82608696 1. ] mean value: 0.8464019384645048 MCC on Blind test: -0.05 MCC on Training: 0.27 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.33279514 0.31931472 0.39896226 0.28957248 0.29794645 0.24529052 0.26019073 0.25452971 0.27223611 0.3730917 ] mean value: 0.30439298152923583 key: score_time value: [0.01210666 0.01241779 0.01237464 0.01243162 0.01235652 0.01247191 0.01259422 0.01238871 0.01258421 0.01243782] mean value: 0.012416410446166991 key: test_mcc value: [-0.16666667 0.61237244 -0.16666667 0.16666667 0.40824829 -0.16666667 1. 0.57735027 -0.57735027 0.57735027] mean value: 0.226463766201595 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.66666667 0.4 0.66666667 0.5 0.4 1. 0.8 0.4 0.8 ] mean value: 0.6033333333333334 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 1. 0.33333333 0.66666667 1. 0.5 1. 0.66666667 0.33333333 0.66666667] mean value: 0.65 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 0.5 0.66666667 0.33333333 0.33333333 1. 1. 0.5 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.8 0.4 0.6 0.6 0.4 1. 0.75 0.25 0.75] mean value: 0.595 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.41666667 0.75 0.41666667 0.58333333 0.66666667 0.41666667 1. 0.75 0.25 0.75 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.5 0.25 0.5 0.33333333 0.25 1. 0.66666667 0.25 0.66666667] mean value: 0.4666666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.23 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01166511 0.01159096 0.00951862 0.00825977 0.00906301 0.00893521 0.00923538 0.0084703 0.0092485 0.00843811] mean value: 0.009442496299743652 key: score_time value: [0.01158214 0.01141 0.00953436 0.00922656 0.00861573 0.00854039 0.00880647 0.00879288 0.00892258 0.00905108] mean value: 0.00944821834564209 key: test_mcc value: [-0.40824829 0.66666667 -0.61237244 -0.16666667 0.16666667 -0.61237244 1. 0. 0.57735027 0. ] mean value: 0.06110237740008403 key: train_mcc value: [0.36515617 0.36718832 0.51320273 0.51190476 0.41428571 0.36666667 0.57207755 0.52620136 0.38138504 0.38490018] mean value: 0.44029684955065196 key: test_fscore value: [0. 0.8 0.33333333 0.4 0.66666667 0. 1. 0.5 0.8 0.5 ] mean value: 0.5 key: train_fscore value: [0.69767442 0.71111111 0.77272727 0.75 0.7 0.68292683 0.7804878 0.75 0.69767442 0.71111111] mean value: 0.7253712966305139 key: test_precision value: [0. 0.66666667 0.25 0.5 0.66666667 0. 1. 0.5 0.66666667 0.5 ] mean value: 0.475 key: train_precision value: [0.68181818 0.66666667 0.73913043 0.75 0.7 0.66666667 0.8 0.78947368 0.68181818 0.66666667] mean value: 0.7142240482629499 key: test_recall value: [0. 1. 0.5 0.33333333 0.66666667 0. 1. 0.5 1. 0.5 ] mean value: 0.55 key: train_recall value: [0.71428571 0.76190476 0.80952381 0.75 0.7 0.7 0.76190476 0.71428571 0.71428571 0.76190476] mean value: 0.7388095238095238 key: test_accuracy value: [0.4 0.8 0.2 0.4 0.6 0.2 1. 0.5 0.75 0.5 ] mean value: 0.5349999999999999 key: train_accuracy value: [0.68292683 0.68292683 0.75609756 0.75609756 0.70731707 0.68292683 0.78571429 0.76190476 0.69047619 0.69047619] mean value: 0.7196864111498258 key: test_roc_auc value: [0.33333333 0.83333333 0.25 0.41666667 0.58333333 0.25 1. 0.5 0.75 0.5 ] mean value: 0.5416666666666666 key: train_roc_auc value: [0.68214286 0.68095238 0.7547619 0.75595238 0.70714286 0.68333333 0.78571429 0.76190476 0.69047619 0.69047619] mean value: 0.7192857142857143 key: test_jcc value: [0. 0.66666667 0.2 0.25 0.5 0. 1. 0.33333333 0.66666667 0.33333333] mean value: 0.395 key: train_jcc value: [0.53571429 0.55172414 0.62962963 0.6 0.53846154 0.51851852 0.64 0.6 0.53571429 0.55172414] mean value: 0.5701486533900326 MCC on Blind test: 0.23 MCC on Training: 0.06 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.00852966 0.00825644 0.00840259 0.00828195 0.00880599 0.00947118 0.0091517 0.0092752 0.01042128 0.00870538] mean value: 0.008930134773254394 key: score_time value: [0.00865936 0.00845098 0.00846434 0.00878215 0.0088613 0.0089004 0.00931454 0.00922561 0.00845647 0.00936937] mean value: 0.008848452568054199 key: test_mcc value: [ 0.16666667 -0.16666667 -0.16666667 -0.16666667 -0.61237244 -0.16666667 0. 0. 0.57735027 0. ] mean value: -0.053502216650616864 key: train_mcc value: [0.76500781 0.71121921 0.6806903 0.62048368 0.67700771 0.73786479 0.71754731 0.78446454 0.76980036 0.78446454] mean value: 0.7248550246146053 key: test_fscore value: [0.5 0.4 0.4 0.4 0. 0.4 0.5 0. 0.8 0.5] mean value: 0.39 key: train_fscore value: [0.87179487 0.85 0.81081081 0.70967742 0.8 0.82352941 0.85 0.86486486 0.87179487 0.86486486] mean value: 0.831733711524983 key: test_precision value: [0.5 0.33333333 0.33333333 0.5 0. 0.5 0.5 0. 0.66666667 0.5 ] mean value: 0.3833333333333333 key: train_precision value: [0.94444444 0.89473684 0.9375 1. 0.93333333 1. 0.89473684 1. 0.94444444 1. ] mean value: 0.9549195906432748 key: test_recall value: [0.5 0.5 0.5 0.33333333 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.41666666666666663 key: train_recall value: [0.80952381 0.80952381 0.71428571 0.55 0.7 0.7 0.80952381 0.76190476 0.80952381 0.76190476] mean value: 0.7426190476190475 key: test_accuracy value: [0.6 0.4 0.4 0.4 0.2 0.4 0.5 0.5 0.75 0.5 ] mean value: 0.465 key: train_accuracy value: [0.87804878 0.85365854 0.82926829 0.7804878 0.82926829 0.85365854 0.85714286 0.88095238 0.88095238 0.88095238] mean value: 0.852439024390244 key: test_roc_auc value: [0.58333333 0.41666667 0.41666667 0.41666667 0.25 0.41666667 0.5 0.5 0.75 0.5 ] mean value: 0.475 key: train_roc_auc value: [0.8797619 0.8547619 0.83214286 0.775 0.82619048 0.85 0.85714286 0.88095238 0.88095238 0.88095238] mean value: 0.8517857142857143 key: test_jcc value: [0.33333333 0.25 0.25 0.25 0. 0.25 0.33333333 0. 0.66666667 0.33333333] mean value: 0.26666666666666666 key: train_jcc value: [0.77272727 0.73913043 0.68181818 0.55 0.66666667 0.7 0.73913043 0.76190476 0.77272727 0.76190476] mean value: 0.7146009787314135 MCC on Blind test: -0.07 MCC on Training: -0.05 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01015067 0.01059628 0.00950813 0.00868607 0.0084815 0.00890899 0.00874043 0.00846529 0.00898051 0.00891709] mean value: 0.009143495559692382 key: score_time value: [0.01014233 0.00992036 0.00894094 0.00868011 0.00862646 0.00828934 0.00884557 0.00813222 0.00836205 0.00890255] mean value: 0.008884191513061523 key: test_mcc value: [-0.16666667 0.16666667 0.40824829 0.16666667 0.66666667 -0.61237244 0.57735027 0. 0.57735027 0.57735027] mean value: 0.2361259995670279 key: train_mcc value: [0.65915306 0.86333169 0.81975606 1. 0.86240942 0.90649828 0.78446454 0.70710678 0.90889326 0.8660254 ] mean value: 0.8377638497019989 key: test_fscore value: [0.4 0.5 0.66666667 0.66666667 0.8 0. 0.66666667 0.5 0.66666667 0.8 ] mean value: 0.5666666666666667 key: train_fscore value: [0.84 0.92307692 0.91304348 1. 0.91891892 0.94736842 0.86486486 0.8 0.95 0.92307692] mean value: 0.908034952925113 key: test_precision value: [0.33333333 0.5 0.5 0.66666667 1. 0. 1. 0.5 1. 0.66666667] mean value: 0.6166666666666667 key: train_precision value: [0.72413793 1. 0.84 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9564137931034482 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 0.5 0.5 1. ] mean value: 0.5833333333333333 key: train_recall value: [1. 0.85714286 1. 1. 0.85 0.9 0.76190476 0.66666667 0.9047619 0.85714286] mean value: 0.8797619047619047 key: test_accuracy value: [0.4 0.6 0.6 0.6 0.8 0.2 0.75 0.5 0.75 0.75] mean value: 0.595 key: train_accuracy value: [0.80487805 0.92682927 0.90243902 1. 0.92682927 0.95121951 0.88095238 0.83333333 0.95238095 0.92857143] mean value: 0.9107433217189316 key: test_roc_auc value: [0.41666667 0.58333333 0.66666667 0.58333333 0.83333333 0.25 0.75 0.5 0.75 0.75 ] mean value: 0.6083333333333333 key: train_roc_auc value: [0.8 0.92857143 0.9 1. 0.925 0.95 0.88095238 0.83333333 0.95238095 0.92857143] mean value: 0.9098809523809525 key: test_jcc value: [0.25 0.33333333 0.5 0.5 0.66666667 0. 0.5 0.33333333 0.5 0.66666667] mean value: 0.425 key: train_jcc value: [0.72413793 0.85714286 0.84 1. 0.85 0.9 0.76190476 0.66666667 0.9047619 0.85714286] mean value: 0.8361756978653532 MCC on Blind test: -0.14 MCC on Training: 0.24 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01009822 0.01035261 0.01045465 0.01008892 0.01049113 0.00967264 0.00954032 0.0100472 0.0095284 0.0099771 ] mean value: 0.010025119781494141 key: score_time value: [0.0103395 0.01032519 0.01000428 0.00988507 0.0097394 0.01031828 0.00934315 0.00926995 0.0092988 0.00997996] mean value: 0.009850358963012696 key: test_mcc value: [ 0.66666667 0.16666667 -0.66666667 -0.16666667 0.16666667 0.61237244 0. 1. 0.57735027 0.57735027] mean value: 0.29337396407417127 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.5 0. 0.4 0.66666667 0.85714286 0.5 1. 0.66666667 0.66666667] mean value: 0.6057142857142858 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.5 0. 0.5 0.66666667 0.75 0.5 1. 1. 1. ] mean value: 0.6583333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.5 0. 0.33333333 0.66666667 1. 0.5 1. 0.5 0.5 ] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.6 0.2 0.4 0.6 0.8 0.5 1. 0.75 0.75] mean value: 0.64 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.58333333 0.16666667 0.41666667 0.58333333 0.75 0.5 1. 0.75 0.75 ] mean value: 0.6333333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.33333333 0. 0.25 0.5 0.75 0.33333333 1. 0.5 0.5 ] mean value: 0.4833333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.04 MCC on Training: 0.29 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.57373047 0.54999232 0.57766914 0.58795047 0.52892327 0.52793503 0.58941269 0.53113842 0.55133843 0.58302736] mean value: 0.5601117610931396 key: score_time value: [0.13069916 0.1531682 0.17327976 0.23457599 0.17344546 0.15258312 0.1468904 0.17300105 0.17896342 0.14371395] mean value: 0.1660320520401001 key: test_mcc value: [ 0.16666667 0.16666667 0.66666667 0.16666667 0.61237244 -0.16666667 0.57735027 0. 1. 0. ] mean value: 0.31897227048854204 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.5 0.8 0.66666667 0.85714286 0.4 0.66666667 0.66666667 1. 0.5 ] mean value: 0.6557142857142857 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.5 0.66666667 0.66666667 0.75 0.5 1. 0.5 1. 0.5 ] mean value: 0.6583333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 1. 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.6 0.8 0.6 0.8 0.4 0.75 0.5 1. 0.5 ] mean value: 0.655 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.58333333 0.83333333 0.58333333 0.75 0.41666667 0.75 0.5 1. 0.5 ] mean value: 0.65 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.33333333 0.66666667 0.5 0.75 0.25 0.5 0.5 1. 0.33333333] mean value: 0.5166666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.22 MCC on Training: 0.32 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.87420106 0.87551212 0.81906438 0.86709476 0.84636211 0.86742973 0.82707047 0.95120144 0.87372661 0.86673975] mean value: 0.8668402433395386 key: score_time value: [0.21467638 0.19172621 0.16076136 0.1923728 0.17124486 0.19162178 0.20504785 0.20591426 0.22132683 0.19397855] mean value: 0.19486708641052247 key: test_mcc value: [ 0.16666667 0.16666667 0.40824829 0.66666667 0.61237244 -0.16666667 1. 0.57735027 1. 0. ] mean value: 0.4431304328682617 key: train_mcc value: [0.8047619 0.86333169 0.8047619 0.90238095 0.7565654 0.75714286 0.80952381 0.9047619 0.80952381 0.76277007] mean value: 0.8175524309391557 key: test_fscore value: [0.5 0.5 0.66666667 0.8 0.85714286 0.4 1. 0.8 1. 0.5 ] mean value: 0.7023809523809523 key: train_fscore value: [0.9047619 0.92307692 0.9047619 0.95 0.87179487 0.87804878 0.9047619 0.95238095 0.9047619 0.87804878] mean value: 0.9072397927275976 key: test_precision value: [0.5 0.5 0.5 1. 0.75 0.5 1. 0.66666667 1. 0.5 ] mean value: 0.6916666666666667 key: train_precision value: [0.9047619 1. 0.9047619 0.95 0.89473684 0.85714286 0.9047619 0.95238095 0.9047619 0.9 ] mean value: 0.9173308270676692 key: test_recall value: [0.5 0.5 1. 0.66666667 1. 0.33333333 1. 1. 1. 0.5 ] mean value: 0.75 key: train_recall value: [0.9047619 0.85714286 0.9047619 0.95 0.85 0.9 0.9047619 0.95238095 0.9047619 0.85714286] mean value: 0.8985714285714286 key: test_accuracy value: [0.6 0.6 0.6 0.8 0.8 0.4 1. 0.75 1. 0.5 ] mean value: 0.705 key: train_accuracy value: [0.90243902 0.92682927 0.90243902 0.95121951 0.87804878 0.87804878 0.9047619 0.95238095 0.9047619 0.88095238] mean value: 0.9081881533101045 key: test_roc_auc value: [0.58333333 0.58333333 0.66666667 0.83333333 0.75 0.41666667 1. 0.75 1. 0.5 ] mean value: 0.7083333333333334 key: train_roc_auc value: [0.90238095 0.92857143 0.90238095 0.95119048 0.87738095 0.87857143 0.9047619 0.95238095 0.9047619 0.88095238] mean value: 0.9083333333333334 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.33333333 0.33333333 0.5 0.66666667 0.75 0.25 1. 0.66666667 1. 0.33333333] mean value: 0.5833333333333333 key: train_jcc value: [0.82608696 0.85714286 0.82608696 0.9047619 0.77272727 0.7826087 0.82608696 0.90909091 0.82608696 0.7826087 ] mean value: 0.831328816111425 MCC on Blind test: -0.22 MCC on Training: 0.44 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01691031 0.00902176 0.0110662 0.01007366 0.00877571 0.00883031 0.0098176 0.01377273 0.00856304 0.00866246] mean value: 0.010549378395080567 key: score_time value: [0.01528168 0.00843811 0.00928664 0.00897598 0.00914454 0.00914335 0.00917363 0.00824451 0.00827432 0.00816512] mean value: 0.009412789344787597 key: test_mcc value: [-0.66666667 0.61237244 -0.16666667 -0.16666667 0.66666667 -0.16666667 1. 0.57735027 0.57735027 0.57735027] mean value: 0.2844423243264672 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.66666667 0.4 0.4 0.8 0.4 1. 0.8 0.8 0.8 ] mean value: 0.6066666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 1. 0.33333333 0.5 1. 0.5 1. 0.66666667 0.66666667 0.66666667] mean value: 0.6333333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.5 0.5 0.33333333 0.66666667 0.33333333 1. 1. 1. 1. ] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.2 0.8 0.4 0.4 0.8 0.4 1. 0.75 0.75 0.75] mean value: 0.625 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.16666667 0.75 0.41666667 0.41666667 0.83333333 0.41666667 1. 0.75 0.75 0.75 ] mean value: 0.625 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.5 0.25 0.25 0.66666667 0.25 1. 0.66666667 0.66666667 0.66666667] mean value: 0.4916666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.31 MCC on Training: 0.28 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.02787042 0.02770114 0.02770042 0.02771497 0.0283761 0.02803659 0.02715516 0.02729034 0.02629352 0.02705431] mean value: 0.02751929759979248 key: score_time value: [0.00946212 0.00962543 0.0094471 0.00961018 0.00997925 0.00969052 0.00949192 0.00899172 0.00890756 0.00926733] mean value: 0.009447312355041504 key: test_mcc value: [-0.66666667 0.61237244 -0.16666667 -0.16666667 0.61237244 -0.16666667 1. 0.57735027 0.57735027 0.57735027] mean value: 0.27901290122938 key: train_mcc value: [1. 0.75714286 0.8047619 1. 0.7565654 1. 1. 1. 1. 1. ] mean value: 0.9318470162439271 key: test_fscore value: [0. 0.66666667 0.4 0.4 0.85714286 0.4 1. 0.8 0.8 0.8 ] mean value: 0.6123809523809524 key: train_fscore value: [1. 0.87804878 0.9047619 1. 0.87179487 1. 1. 1. 1. 1. ] mean value: 0.9654605557044581 key: test_precision value: [0. 1. 0.33333333 0.5 0.75 0.5 1. 0.66666667 0.66666667 0.66666667] mean value: 0.6083333333333334 key: train_precision value: [1. 0.9 0.9047619 1. 0.89473684 1. 1. 1. 1. 1. ] mean value: 0.9699498746867168 key: test_recall value: [0. 0.5 0.5 0.33333333 1. 0.33333333 1. 1. 1. 1. ] mean value: 0.6666666666666666 key: train_recall value: [1. 0.85714286 0.9047619 1. 0.85 1. 1. 1. 1. 1. ] mean value: 0.9611904761904763 key: test_accuracy value: [0.2 0.8 0.4 0.4 0.8 0.4 1. 0.75 0.75 0.75] mean value: 0.625 key: train_accuracy value: [1. 0.87804878 0.90243902 1. 0.87804878 1. 1. 1. 1. 1. ] mean value: 0.9658536585365853 key: test_roc_auc value: [0.16666667 0.75 0.41666667 0.41666667 0.75 0.41666667 1. 0.75 0.75 0.75 ] mean value: 0.6166666666666666 key: train_roc_auc value: [1. 0.87857143 0.90238095 1. 0.87738095 1. 1. 1. 1. 1. ] mean value: 0.9658333333333333 key: test_jcc value: [0. 0.5 0.25 0.25 0.75 0.25 1. 0.66666667 0.66666667 0.66666667] mean value: 0.5 key: train_jcc value: [1. 0.7826087 0.82608696 1. 0.77272727 1. 1. 1. 1. 1. ] mean value: 0.9381422924901186 MCC on Blind test: -0.33 MCC on Training: 0.28 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00823736 0.00855374 0.00793791 0.00796914 0.00793433 0.00794601 0.00793457 0.0080235 0.00824285 0.00828838] mean value: 0.00810678005218506 key: score_time value: [0.00830483 0.00833702 0.00831413 0.00819254 0.00848055 0.00816464 0.00872397 0.00823665 0.00896573 0.0083251 ] mean value: 0.00840451717376709 key: test_mcc value: [-0.40824829 0.66666667 -0.16666667 0.40824829 0.16666667 -0.61237244 1. 0. 0.57735027 0. ] mean value: 0.1631644500160498 key: train_mcc value: [0.80907152 0.8547619 0.65952381 0.80817439 0.65871309 0.8047619 0.81322028 0.71754731 0.61904762 0.71428571] mean value: 0.7459107548495205 key: test_fscore value: [0. 0.8 0.4 0.5 0.66666667 0. 1. 0.5 0.66666667 0.5 ] mean value: 0.5033333333333333 key: train_fscore value: [0.9 0.92682927 0.82926829 0.89473684 0.82051282 0.9 0.9 0.85 0.80952381 0.85714286] mean value: 0.868801389026036 key: test_precision value: [0. 0.66666667 0.33333333 1. 0.66666667 0. 1. 0.5 1. 0.5 ] mean value: 0.5666666666666667 key: train_precision value: [0.94736842 0.95 0.85 0.94444444 0.84210526 0.9 0.94736842 0.89473684 0.80952381 0.85714286] mean value: 0.8942690058479533 key: test_recall value: [0. 1. 0.5 0.33333333 0.66666667 0. 1. 0.5 0.5 0.5 ] mean value: 0.5 key: train_recall value: [0.85714286 0.9047619 0.80952381 0.85 0.8 0.9 0.85714286 0.80952381 0.80952381 0.85714286] mean value: 0.8454761904761904 key: test_accuracy value: [0.4 0.8 0.4 0.6 0.6 0.2 1. 0.5 0.75 0.5 ] mean value: 0.575 key: train_accuracy value: [0.90243902 0.92682927 0.82926829 0.90243902 0.82926829 0.90243902 0.9047619 0.85714286 0.80952381 0.85714286] mean value: 0.8721254355400696 key: test_roc_auc value: [0.33333333 0.83333333 0.41666667 0.66666667 0.58333333 0.25 1. 0.5 0.75 0.5 ] mean value: 0.5833333333333333 key: train_roc_auc value: [0.90357143 0.92738095 0.8297619 0.90119048 0.82857143 0.90238095 0.9047619 0.85714286 0.80952381 0.85714286] mean value: 0.8721428571428571 key: test_jcc value: [0. 0.66666667 0.25 0.33333333 0.5 0. 1. 0.33333333 0.5 0.33333333] mean value: 0.39166666666666666 key: train_jcc value: [0.81818182 0.86363636 0.70833333 0.80952381 0.69565217 0.81818182 0.81818182 0.73913043 0.68 0.75 ] mean value: 0.7700821569734613 MCC on Blind test: 0.12 MCC on Training: 0.16 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00908589 0.00824165 0.00832438 0.00916982 0.00817895 0.00874543 0.00825739 0.00870991 0.00894213 0.00866628] mean value: 0.008632183074951172 key: score_time value: [0.00825143 0.00820041 0.00884247 0.00877833 0.00866914 0.00845718 0.00933552 0.00842166 0.00858283 0.00831676] mean value: 0.008585572242736816 key: test_mcc value: [-0.66666667 0.16666667 -0.66666667 0.40824829 0. -0.66666667 1. 0.57735027 0.57735027 0. ] mean value: 0.07296154955097814 key: train_mcc value: [1. 1. 1. 0.81975606 0.66496381 0.90692382 0.85811633 0.85811633 1. 0.63245553] mean value: 0.8740331889545139 key: test_fscore value: [0. 0.5 0. 0.5 0.75 0.33333333 1. 0.8 0.8 0.5 ] mean value: 0.5183333333333333 key: train_fscore value: [1. 1. 1. 0.88888889 0.83333333 0.95238095 0.93023256 0.92682927 1. 0.72727273] mean value: 0.9258937728308119 key: test_precision value: [0. 0.5 0. 1. 0.6 0.33333333 1. 0.66666667 0.66666667 0.5 ] mean value: 0.5266666666666666 key: train_precision value: [1. 1. 1. 1. 0.71428571 0.90909091 0.90909091 0.95 1. 1. ] mean value: 0.9482467532467531 key: test_recall value: [0. 0.5 0. 0.33333333 1. 0.33333333 1. 1. 1. 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 0.8 1. 1. 0.95238095 0.9047619 1. 0.57142857] mean value: 0.9228571428571428 key: test_accuracy value: [0.2 0.6 0.2 0.6 0.6 0.2 1. 0.75 0.75 0.5 ] mean value: 0.54 key: train_accuracy value: [1. 1. 1. 0.90243902 0.80487805 0.95121951 0.92857143 0.92857143 1. 0.78571429] mean value: 0.9301393728222997 key: test_roc_auc value: [0.16666667 0.58333333 0.16666667 0.66666667 0.5 0.16666667 1. 0.75 0.75 0.5 ] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 0.9 0.80952381 0.95238095 0.92857143 0.92857143 1. 0.78571429] mean value: 0.9304761904761906 key: test_jcc value: [0. 0.33333333 0. 0.33333333 0.6 0.2 1. 0.66666667 0.66666667 0.33333333] mean value: 0.4133333333333333 key: train_jcc value: [1. 1. 1. 0.8 0.71428571 0.90909091 0.86956522 0.86363636 1. 0.57142857] mean value: 0.8728006775832862 MCC on Blind test: 0.04 MCC on Training: 0.07 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.04524159 0.0334878 0.03596449 0.03431392 0.03716421 0.03736687 0.03562546 0.03832245 0.06303716 0.03363514] mean value: 0.03941590785980224 key: score_time value: [0.01013994 0.01006317 0.00984788 0.01044226 0.01103115 0.01145387 0.0101254 0.01009893 0.01097631 0.01054716] mean value: 0.010472607612609864 key: test_mcc value: [ 0.61237244 0.16666667 0.66666667 0.16666667 0.16666667 -0.16666667 0.57735027 0.57735027 1. 0. ] mean value: 0.37670729740750464 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.5 0.8 0.66666667 0.66666667 0.4 0.66666667 0.8 1. 0.5 ] mean value: 0.6666666666666666 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.66666667 0.66666667 0.66666667 0.5 1. 0.66666667 1. 0.5 ] mean value: 0.7166666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0.33333333 0.5 1. 1. 0.5 ] mean value: 0.6666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.6 0.8 0.6 0.6 0.4 0.75 0.75 1. 0.5 ] mean value: 0.6799999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.58333333 0.83333333 0.58333333 0.58333333 0.41666667 0.75 0.75 1. 0.5 ] mean value: 0.675 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.33333333 0.66666667 0.5 0.5 0.25 0.5 0.66666667 1. 0.33333333] mean value: 0.5249999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.38 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.07587266 0.07213521 0.07120967 0.07029963 0.07128644 0.07081151 0.07174897 0.07106471 0.07000303 0.0706172 ] mean value: 0.07150490283966064 key: score_time value: [0.0150547 0.01507974 0.015347 0.01513362 0.014328 0.01518846 0.01506209 0.01508427 0.01501298 0.01500583] mean value: 0.015029668807983398 key: test_mcc value: [-0.16666667 0.16666667 0.40824829 0.40824829 0.16666667 -0.16666667 0. 0.57735027 1. 1. ] mean value: 0.3393846850117352 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... D@ @Wк?e?@@Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... m>1 =1 C?*?b?1 =*?1 C??%I>1 C?1 C?*?n6?1 C?zg?%I?>1 >>%I>%I>m[?m[??rR?r@?qq?*R@@m? ?9@ ףp= ?$@N?r ? .@@? @??З?0{9?G@0{PbPbPPbS@8@,@0@(@@@,@@P@ @>@@;@Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.12793899 0.12214732 0.12213111 0.12378001 0.12457776 0.11417603 0.11325645 0.11431479 0.11446452 0.11424851] mean value: 0.11910355091094971 key: score_time value: [0.01555252 0.01663589 0.01620436 0.0169704 0.01464248 0.01474237 0.01461005 0.0155673 0.01466918 0.01476192] mean value: 0.01543564796447754 key: test_mcc value: [0.33333333 0.16903085 0.0836242 0.2508726 0.58002308 0.58930667 0.39393939 0.56490196 0.05427825 0.37057951] mean value: 0.33898898360120666 key: train_mcc value: [0.99052111 0.98095238 0.98113038 0.98113038 0.96225401 0.94329113 0.97160827 0.98122024 0.99056519 0.98104223] mean value: 0.9763715321301181 key: test_fscore value: [0.66666667 0.61538462 0.52173913 0.64 0.73684211 0.8 0.69565217 0.8 0.47619048 0.55555556] mean value: 0.6508030723408298 key: train_fscore value: [0.99526066 0.99047619 0.99056604 0.99056604 0.98130841 0.97196262 0.98578199 0.99056604 0.99521531 0.99047619] mean value: 0.9882179487230532 key: test_precision value: [0.66666667 0.57142857 0.54545455 0.61538462 0.875 0.71428571 0.66666667 0.76923077 0.55555556 0.83333333] mean value: 0.6813006438006438 key: train_precision value: [0.99056604 0.99047619 0.98130841 0.98130841 0.97222222 0.96296296 0.99047619 0.98130841 1. 0.99047619] mean value: 0.9841105027994466 key: test_recall value: [0.66666667 0.66666667 0.5 0.66666667 0.63636364 0.90909091 0.72727273 0.83333333 0.41666667 0.41666667] mean value: 0.6439393939393939 key: train_recall value: [1. 0.99047619 1. 1. 0.99056604 0.98113208 0.98113208 1. 0.99047619 0.99047619] mean value: 0.9924258760107817 key: test_accuracy value: [0.66666667 0.58333333 0.54166667 0.625 0.7826087 0.7826087 0.69565217 0.7826087 0.52173913 0.65217391] mean value: 0.6634057971014493 key: train_accuracy value: [0.9952381 0.99047619 0.99047619 0.99047619 0.98104265 0.97156398 0.98578199 0.99052133 0.99526066 0.99052133] mean value: 0.9881358609794629 key: test_roc_auc value: [0.66666667 0.58333333 0.54166667 0.625 0.77651515 0.78787879 0.6969697 0.78030303 0.52651515 0.66287879] mean value: 0.6647727272727273 key: train_roc_auc value: [0.9952381 0.99047619 0.99047619 0.99047619 0.9809973 0.97151842 0.98580413 0.99056604 0.9952381 0.99052111] mean value: 0.9881311769991015 key: test_jcc value: [0.5 0.44444444 0.35294118 0.47058824 0.58333333 0.66666667 0.53333333 0.66666667 0.3125 0.38461538] mean value: 0.49150892408245356 key: train_jcc value: [0.99056604 0.98113208 0.98130841 0.98130841 0.96330275 0.94545455 0.97196262 0.98130841 0.99047619 0.98113208] mean value: 0.976795152737085 MCC on Blind test: 0.31 MCC on Training: 0.34 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.148803 0.19723296 0.19339418 0.17748094 0.16422772 0.19386029 0.17639232 0.18323612 0.18785429 0.15427136] mean value: 0.17767531871795655 key: score_time value: [0.05385709 0.05773163 0.04181004 0.03797078 0.04341221 0.06213951 0.06285095 0.07354331 0.07249808 0.06463814] mean value: 0.05704517364501953 key: test_mcc value: [0.43033148 0.60246408 0.3380617 0.70710678 0.65151515 0.76764947 0.31298622 0.76277007 0.21969697 0.48856385] mean value: 0.5281145779979035 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.81481481 0.63636364 0.8 0.81818182 0.88 0.66666667 0.88888889 0.60869565 0.72727273] mean value: 0.7507550871029133 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.77777778 0.73333333 0.7 1. 0.81818182 0.78571429 0.61538462 0.8 0.63636364 0.8 ] mean value: 0.7666755466755467 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.58333333 0.91666667 0.58333333 0.66666667 0.81818182 1. 0.72727273 1. 0.58333333 0.66666667] mean value: 0.7545454545454546 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.70833333 0.79166667 0.66666667 0.83333333 0.82608696 0.86956522 0.65217391 0.86956522 0.60869565 0.73913043] mean value: 0.7565217391304347 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.70833333 0.79166667 0.66666667 0.83333333 0.82575758 0.875 0.65530303 0.86363636 0.60984848 0.74242424] mean value: 0.7571969696969697 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.6875 0.46666667 0.66666667 0.69230769 0.78571429 0.5 0.8 0.4375 0.57142857] mean value: 0.6107783882783883 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.43 MCC on Training: 0.53 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01487184 0.01618195 0.01543689 0.01733398 0.01736307 0.01775718 0.01727414 0.01735425 0.01714301 0.01648855] mean value: 0.01672048568725586 key: score_time value: [0.00854826 0.00870848 0.00968122 0.00962043 0.00958014 0.00967884 0.00960279 0.00960517 0.00959802 0.00956321] mean value: 0.009418654441833495 key: test_mcc value: [0.50709255 0.25819889 0.3380617 0.50709255 0.65151515 0.58930667 0.25495628 0.56490196 0.31298622 0.13740858] mean value: 0.4121520554729011 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.66666667 0.63636364 0.76923077 0.81818182 0.8 0.66666667 0.8 0.63636364 0.54545455] mean value: 0.7066200466200467 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.6 0.7 0.71428571 0.81818182 0.71428571 0.5625 0.76923077 0.7 0.6 ] mean value: 0.6978484015984016 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.75 0.58333333 0.83333333 0.81818182 0.90909091 0.81818182 0.83333333 0.58333333 0.5 ] mean value: 0.7295454545454545 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.625 0.66666667 0.75 0.82608696 0.7826087 0.60869565 0.7826087 0.65217391 0.56521739] mean value: 0.7009057971014492 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.625 0.66666667 0.75 0.82575758 0.78787879 0.61742424 0.78030303 0.65530303 0.56818182] mean value: 0.7026515151515152 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.5 0.46666667 0.625 0.69230769 0.66666667 0.5 0.66666667 0.46666667 0.375 ] mean value: 0.553040293040293 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.41 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01136327 0.00980568 0.00978446 0.00899673 0.01014328 0.00874949 0.01006293 0.01002407 0.00991821 0.0102005 ] mean value: 0.009904861450195312 key: score_time value: [0.00895238 0.00850034 0.00888252 0.00835156 0.00859213 0.00893402 0.00925398 0.00921297 0.00920272 0.00947905] mean value: 0.008936166763305664 key: test_mcc value: [ 0. -0.09166985 0.1767767 0.58536941 0.12336594 0.65151515 0.12878788 0.48075018 0.23262105 0.21969697] mean value: 0.2507213423375897 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.45454545 0.55172414 0.5 0.8 0.44444444 0.81818182 0.54545455 0.76923077 0.57142857 0.60869565] mean value: 0.606370539339055 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.47058824 0.625 0.76923077 0.57142857 0.81818182 0.54545455 0.71428571 0.66666667 0.63636364] mean value: 0.6317199956905839 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.41666667 0.66666667 0.41666667 0.83333333 0.36363636 0.81818182 0.54545455 0.83333333 0.5 0.58333333] mean value: 0.5977272727272727 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.45833333 0.58333333 0.79166667 0.56521739 0.82608696 0.56521739 0.73913043 0.60869565 0.60869565] mean value: 0.6246376811594203 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.45833333 0.58333333 0.79166667 0.55681818 0.82575758 0.56439394 0.73484848 0.61363636 0.60984848] mean value: 0.6238636363636363 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.29411765 0.38095238 0.33333333 0.66666667 0.28571429 0.69230769 0.375 0.625 0.4 0.4375 ] mean value: 0.44905920060331833 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.25 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10137606 0.09852624 0.10859299 0.10187507 0.09856892 0.10316992 0.10743713 0.10815549 0.10431838 0.10042763] mean value: 0.10324478149414062 key: score_time value: [0.01710057 0.02039218 0.01907992 0.01883221 0.0171237 0.01833534 0.01879239 0.01889038 0.01749659 0.01833701] mean value: 0.018438029289245605 key: test_mcc value: [0.16666667 0.5 0.1767767 0.66666667 0.56818182 0.74242424 0.21969697 0.39727608 0.13740858 0.13740858] mean value: 0.3712506305233937 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.58333333 0.75 0.5 0.83333333 0.7826087 0.86956522 0.60869565 0.74074074 0.54545455 0.54545455] mean value: 0.6759186063533889 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.58333333 0.75 0.625 0.83333333 0.75 0.83333333 0.58333333 0.66666667 0.6 0.6 ] mean value: 0.6824999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.58333333 0.75 0.41666667 0.83333333 0.81818182 0.90909091 0.63636364 0.83333333 0.5 0.5 ] mean value: 0.6780303030303031 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.58333333 0.75 0.58333333 0.83333333 0.7826087 0.86956522 0.60869565 0.69565217 0.56521739 0.56521739] mean value: 0.683695652173913 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.75 0.58333333 0.83333333 0.78409091 0.87121212 0.60984848 0.68939394 0.56818182 0.56818182] mean value: 0.6840909090909092 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.41176471 0.6 0.33333333 0.71428571 0.64285714 0.76923077 0.4375 0.58823529 0.375 0.375 ] mean value: 0.5247206959706959 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.37 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.38857698 0.38259149 0.38746023 0.37395763 0.37676954 0.37874603 0.39405131 0.3902719 0.38977432 0.38728714] mean value: 0.38494865894317626 key: score_time value: [0.00955296 0.00955105 0.00898981 0.01016307 0.0094049 0.00917315 0.01013088 0.01016378 0.01005983 0.00949335] mean value: 0.009668278694152831 key: test_mcc value: [0.60246408 0.60246408 0.43033148 0.45834925 0.65909298 0.66414149 0.48856385 0.56490196 0.21969697 0.48856385] mean value: 0.5178569985437772 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76190476 0.81481481 0.66666667 0.63157895 0.8 0.83333333 0.75 0.8 0.60869565 0.72727273] mean value: 0.7394266903534639 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88888889 0.73333333 0.77777778 0.85714286 0.88888889 0.76923077 0.69230769 0.76923077 0.63636364 0.8 ] mean value: 0.7813164613164614 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.91666667 0.58333333 0.5 0.72727273 0.90909091 0.81818182 0.83333333 0.58333333 0.66666667] mean value: 0.7204545454545455 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.79166667 0.79166667 0.70833333 0.70833333 0.82608696 0.82608696 0.73913043 0.7826087 0.60869565 0.73913043] mean value: 0.7521739130434782 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.79166667 0.79166667 0.70833333 0.70833333 0.8219697 0.82954545 0.74242424 0.78030303 0.60984848 0.74242424] mean value: 0.7526515151515152 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.61538462 0.6875 0.5 0.46153846 0.66666667 0.71428571 0.6 0.66666667 0.4375 0.57142857] mean value: 0.5920970695970695 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.46 MCC on Training: 0.52 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00979972 0.00997591 0.00967002 0.01007867 0.00991106 0.00977135 0.00956011 0.00994635 0.00959182 0.0103159 ] mean value: 0.009862089157104492 key: score_time value: [0.00939846 0.00936675 0.00856352 0.00955844 0.00958443 0.00919604 0.0084846 0.00885034 0.00951552 0.00965929] mean value: 0.00921773910522461 key: test_mcc value: [ 0.16903085 0.41812101 0.0860663 0.60246408 0.13740858 0.04545455 0.39727608 0.21374669 0.12406456 -0.13740858] mean value: 0.2056224100095044 key: train_mcc value: [0.34992711 0.34287269 0.3478328 0.32382421 0.3176386 0.33647799 0.32704403 0.28994561 0.34651524 0.35544173] mean value: 0.3337520003906342 key: test_fscore value: [0.54545455 0.72 0.59259259 0.76190476 0.58333333 0.52173913 0.63157895 0.64 0.61538462 0.48 ] mean value: 0.6091987926473053 key: train_fscore value: [0.63492063 0.66985646 0.70833333 0.66350711 0.65714286 0.66981132 0.66350711 0.65437788 0.66009852 0.67307692] mean value: 0.6654632148919907 key: test_precision value: [0.6 0.69230769 0.53333333 0.88888889 0.53846154 0.5 0.75 0.61538462 0.57142857 0.46153846] mean value: 0.6151343101343101 key: train_precision value: [0.71428571 0.67307692 0.62962963 0.66037736 0.66346154 0.66981132 0.66666667 0.63392857 0.68367347 0.67961165] mean value: 0.6674522842667518 key: test_recall value: [0.5 0.75 0.66666667 0.66666667 0.63636364 0.54545455 0.54545455 0.66666667 0.66666667 0.5 ] mean value: 0.6143939393939394 key: train_recall value: [0.57142857 0.66666667 0.80952381 0.66666667 0.6509434 0.66981132 0.66037736 0.67619048 0.63809524 0.66666667] mean value: 0.6676370170709794 key: test_accuracy value: [0.58333333 0.70833333 0.54166667 0.79166667 0.56521739 0.52173913 0.69565217 0.60869565 0.56521739 0.43478261] mean value: 0.6016304347826087 key: train_accuracy value: [0.67142857 0.67142857 0.66666667 0.66190476 0.65876777 0.66824645 0.66350711 0.64454976 0.67298578 0.67772512] mean value: 0.6657210561949898 key: test_roc_auc value: [0.58333333 0.70833333 0.54166667 0.79166667 0.56818182 0.52272727 0.68939394 0.60606061 0.56060606 0.43181818] mean value: 0.6003787878787878 key: train_roc_auc value: [0.67142857 0.67142857 0.66666667 0.66190476 0.65880503 0.66823899 0.66352201 0.64469901 0.6728212 0.67767296] mean value: 0.6657187780772686 key: test_jcc value: [0.375 0.5625 0.42105263 0.61538462 0.41176471 0.35294118 0.46153846 0.47058824 0.44444444 0.31578947] mean value: 0.44310037442777384 key: train_jcc value: [0.46511628 0.50359712 0.5483871 0.4964539 0.4893617 0.5035461 0.4964539 0.48630137 0.49264706 0.50724638] mean value: 0.49891109064811356 MCC on Blind test: 0.32 MCC on Training: 0.21 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.02842474 0.02652812 0.04513931 0.02689505 0.0304172 0.08052015 0.06719708 0.02851248 0.02815223 0.03486037] mean value: 0.03966467380523682 key: score_time value: [0.01288724 0.01283407 0.02426314 0.01263762 0.01295996 0.02167296 0.01397204 0.0132668 0.01305175 0.02099514] mean value: 0.015854072570800782 key: test_mcc value: [-0.0836242 0.0836242 0.3380617 0.5 0.21452908 0.41096386 -0.12878788 0.30240737 0.03816905 -0.04545455] mean value: 0.16298886392359305 key: train_mcc value: [0.95242415 0.94285714 0.95242415 0.93337566 0.95265049 0.96225401 0.95265049 0.97160827 0.96226076 0.98104223] mean value: 0.9563547359838888 key: test_fscore value: [0.48 0.56 0.63636364 0.75 0.52631579 0.72 0.43478261 0.69230769 0.56 0.5 ] mean value: 0.5859769726840665 key: train_fscore value: [0.97630332 0.97142857 0.97630332 0.96682464 0.97630332 0.98130841 0.97630332 0.98578199 0.98113208 0.99047619] mean value: 0.9782165153804684 key: test_precision value: [0.46153846 0.53846154 0.7 0.75 0.625 0.64285714 0.41666667 0.64285714 0.53846154 0.5 ] mean value: 0.5815842490842491 key: train_precision value: [0.97169811 0.97142857 0.97169811 0.96226415 0.98095238 0.97222222 0.98095238 0.98113208 0.97196262 0.99047619] mean value: 0.9754786815684364 key: test_recall value: [0.5 0.58333333 0.58333333 0.75 0.45454545 0.81818182 0.45454545 0.75 0.58333333 0.5 ] mean value: 0.5977272727272728 key: train_recall value: [0.98095238 0.97142857 0.98095238 0.97142857 0.97169811 0.99056604 0.97169811 0.99047619 0.99047619 0.99047619] mean value: 0.9810152740341419 key: test_accuracy value: [0.45833333 0.54166667 0.66666667 0.75 0.60869565 0.69565217 0.43478261 0.65217391 0.52173913 0.47826087] mean value: 0.5807971014492754 key: train_accuracy value: [0.97619048 0.97142857 0.97619048 0.96666667 0.97630332 0.98104265 0.97630332 0.98578199 0.98104265 0.99052133] mean value: 0.9781471451139696 key: test_roc_auc value: [0.45833333 0.54166667 0.66666667 0.75 0.60227273 0.70075758 0.43560606 0.64772727 0.51893939 0.47727273] mean value: 0.5799242424242425 key: train_roc_auc value: [0.97619048 0.97142857 0.97619048 0.96666667 0.97632525 0.9809973 0.97632525 0.98580413 0.98108715 0.99052111] mean value: 0.9781536388140163 key: test_jcc value: [0.31578947 0.38888889 0.46666667 0.6 0.35714286 0.5625 0.27777778 0.52941176 0.38888889 0.33333333] mean value: 0.42203996510885056 key: train_jcc value: [0.9537037 0.94444444 0.9537037 0.93577982 0.9537037 0.96330275 0.9537037 0.97196262 0.96296296 0.98113208] mean value: 0.9574399483323692 MCC on Blind test: 0.08 MCC on Training: 0.16 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01172996 0.00895977 0.00846601 0.00882363 0.00899982 0.00982571 0.00850868 0.00860405 0.00832701 0.00835705] mean value: 0.009060168266296386 key: score_time value: [0.0527122 0.00981879 0.00949979 0.01493287 0.01525617 0.01523113 0.01492977 0.01509809 0.00984597 0.01014161] mean value: 0.0167466402053833 key: test_mcc value: [0.16903085 0. 0.16903085 0.0836242 0.03816905 0.21969697 0.12878788 0.48075018 0.21969697 0.13740858] mean value: 0.16461955312053092 key: train_mcc value: [0.44761905 0.45716359 0.4298208 0.45766204 0.46921903 0.45250711 0.44189475 0.39382553 0.44136708 0.52610769] mean value: 0.4517186673223185 key: test_fscore value: [0.61538462 0.5 0.61538462 0.52173913 0.47619048 0.60869565 0.54545455 0.76923077 0.60869565 0.54545455] mean value: 0.5806230001882176 key: train_fscore value: [0.72380952 0.72727273 0.7029703 0.72195122 0.73831776 0.71287129 0.71219512 0.68627451 0.7255814 0.75961538] mean value: 0.721085922348157 key: test_precision value: [0.57142857 0.5 0.57142857 0.54545455 0.5 0.58333333 0.54545455 0.71428571 0.63636364 0.6 ] mean value: 0.5767748917748918 key: train_precision value: [0.72380952 0.73076923 0.73195876 0.74 0.73148148 0.75 0.73737374 0.70707071 0.70909091 0.76699029] mean value: 0.7328544643744322 key: test_recall value: [0.66666667 0.5 0.66666667 0.5 0.45454545 0.63636364 0.54545455 0.83333333 0.58333333 0.5 ] mean value: 0.5886363636363636 key: train_recall value: [0.72380952 0.72380952 0.67619048 0.7047619 0.74528302 0.67924528 0.68867925 0.66666667 0.74285714 0.75238095] mean value: 0.7103683737646002 key: test_accuracy value: [0.58333333 0.5 0.58333333 0.54166667 0.52173913 0.60869565 0.56521739 0.73913043 0.60869565 0.56521739] mean value: 0.5817028985507247 key: train_accuracy value: [0.72380952 0.72857143 0.71428571 0.72857143 0.73459716 0.72511848 0.72037915 0.69668246 0.72037915 0.76303318] mean value: 0.7255427668697811 key: test_roc_auc value: [0.58333333 0.5 0.58333333 0.54166667 0.51893939 0.60984848 0.56439394 0.73484848 0.60984848 0.56818182] mean value: 0.5814393939393939 key: train_roc_auc value: [0.72380952 0.72857143 0.71428571 0.72857143 0.73454627 0.72533693 0.7205301 0.69654088 0.72048518 0.76298293] mean value: 0.725566037735849 key: test_jcc value: [0.44444444 0.33333333 0.44444444 0.35294118 0.3125 0.4375 0.375 0.625 0.4375 0.375 ] mean value: 0.413766339869281 key: train_jcc value: [0.56716418 0.57142857 0.54198473 0.5648855 0.58518519 0.55384615 0.5530303 0.52238806 0.56934307 0.6124031 ] mean value: 0.5641658847772443 MCC on Blind test: -0.03 MCC on Training: 0.16 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02790093 0.06252885 0.06161404 0.05151892 0.05404115 0.05485201 0.04804397 0.02665257 0.02654457 0.04229093] mean value: 0.04559879302978516 key: score_time value: [0.02017426 0.02047276 0.02341604 0.0202117 0.01486492 0.01642036 0.01180792 0.01184916 0.01181841 0.022609 ] mean value: 0.01736445426940918 key: test_mcc value: [0.16903085 0.16903085 0.16903085 0.6761234 0.31298622 0.58930667 0.05427825 0.48075018 0.03816905 0.48856385] mean value: 0.3147270166944373 key: train_mcc value: [0.82857143 0.84823477 0.86729623 0.86729623 0.87773359 0.87711268 0.84892121 0.88691131 0.86984604 0.86794331] mean value: 0.8639866788882411 key: test_fscore value: [0.61538462 0.61538462 0.54545455 0.84615385 0.66666667 0.8 0.56 0.76923077 0.56 0.72727273] mean value: 0.6705547785547786 key: train_fscore value: [0.91428571 0.92523364 0.93457944 0.93457944 0.94009217 0.93953488 0.92592593 0.94392523 0.93577982 0.93457944] mean value: 0.9328515702606632 key: test_precision value: [0.57142857 0.57142857 0.6 0.78571429 0.61538462 0.71428571 0.5 0.71428571 0.53846154 0.8 ] mean value: 0.641098901098901 key: train_precision value: [0.91428571 0.90825688 0.91743119 0.91743119 0.91891892 0.9266055 0.90909091 0.9266055 0.90265487 0.91743119] mean value: 0.9158711877442087 key: test_recall value: [0.66666667 0.66666667 0.5 0.91666667 0.72727273 0.90909091 0.63636364 0.83333333 0.58333333 0.66666667] mean value: 0.7106060606060606 key: train_recall value: [0.91428571 0.94285714 0.95238095 0.95238095 0.96226415 0.95283019 0.94339623 0.96190476 0.97142857 0.95238095] mean value: 0.9506109613656782 key: test_accuracy value: [0.58333333 0.58333333 0.58333333 0.83333333 0.65217391 0.7826087 0.52173913 0.73913043 0.52173913 0.73913043] mean value: 0.6539855072463767 key: train_accuracy value: [0.91428571 0.92380952 0.93333333 0.93333333 0.93838863 0.93838863 0.92417062 0.94312796 0.93364929 0.93364929] mean value: 0.9316136312344844 key: test_roc_auc value: [0.58333333 0.58333333 0.58333333 0.83333333 0.65530303 0.78787879 0.52651515 0.73484848 0.51893939 0.74242424] mean value: 0.6549242424242424 key: train_roc_auc value: [0.91428571 0.92380952 0.93333333 0.93333333 0.93827493 0.93831986 0.92407907 0.94321653 0.93382749 0.93373765] mean value: 0.9316217430368374 key: test_jcc value: [0.44444444 0.44444444 0.375 0.73333333 0.5 0.66666667 0.38888889 0.625 0.38888889 0.57142857] mean value: 0.5138095238095237 key: train_jcc value: [0.84210526 0.86086957 0.87719298 0.87719298 0.88695652 0.88596491 0.86206897 0.89380531 0.87931034 0.87719298] mean value: 0.874265982984288 MCC on Blind test: 0.18 MCC on Training: 0.31 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03298306 0.03041768 0.03451085 0.03556538 0.03493166 0.03467727 0.03482342 0.03387475 0.04895186 0.05662346] mean value: 0.037735939025878906 key: score_time value: [0.01228476 0.0122304 0.01434088 0.01422858 0.01430964 0.01368451 0.01450753 0.0145905 0.01439142 0.01438427] mean value: 0.013895249366760254 key: test_mcc value: [0.0836242 0.33333333 0.25819889 0.41812101 0.13740858 0.74242424 0.39727608 0.50168817 0.03816905 0.13740858] mean value: 0.3047652136251281 key: train_mcc value: [0.59050297 0.69536425 0.63913958 0.600982 0.6209221 0.54577467 0.63052868 0.60376701 0.6823151 0.65991963] mean value: 0.6269215999274851 key: test_fscore value: [0.56 0.66666667 0.57142857 0.69565217 0.58333333 0.86956522 0.63157895 0.78571429 0.56 0.54545455] mean value: 0.6469393741270171 key: train_fscore value: [0.79620853 0.8490566 0.82407407 0.80555556 0.81308411 0.77981651 0.81860465 0.80733945 0.84684685 0.83333333] mean value: 0.8173919671004158 key: test_precision value: [0.53846154 0.66666667 0.66666667 0.72727273 0.53846154 0.83333333 0.75 0.6875 0.53846154 0.6 ] mean value: 0.6546824009324009 key: train_precision value: [0.79245283 0.8411215 0.8018018 0.78378378 0.80555556 0.75892857 0.80733945 0.77876106 0.8034188 0.81081081] mean value: 0.7983974163803296 key: test_recall value: [0.58333333 0.66666667 0.5 0.66666667 0.63636364 0.90909091 0.54545455 0.91666667 0.58333333 0.5 ] mean value: 0.6507575757575756 key: train_recall value: [0.8 0.85714286 0.84761905 0.82857143 0.82075472 0.80188679 0.83018868 0.83809524 0.8952381 0.85714286] mean value: 0.8376639712488769 key: test_accuracy value: [0.54166667 0.66666667 0.625 0.70833333 0.56521739 0.86956522 0.69565217 0.73913043 0.52173913 0.56521739] mean value: 0.6498188405797101 key: train_accuracy value: [0.7952381 0.84761905 0.81904762 0.8 0.81042654 0.77251185 0.81516588 0.80094787 0.83886256 0.82938389] mean value: 0.8129203340103814 key: test_roc_auc value: [0.54166667 0.66666667 0.625 0.70833333 0.56818182 0.87121212 0.68939394 0.73106061 0.51893939 0.56818182] mean value: 0.6488636363636365 key: train_roc_auc value: [0.7952381 0.84761905 0.81904762 0.8 0.81037736 0.77237197 0.81509434 0.80112309 0.83912848 0.82951482] mean value: 0.8129514824797843 key: test_jcc value: [0.38888889 0.5 0.4 0.53333333 0.41176471 0.76923077 0.46153846 0.64705882 0.38888889 0.375 ] mean value: 0.48757038712921064 key: train_jcc value: [0.66141732 0.73770492 0.7007874 0.6744186 0.68503937 0.63909774 0.69291339 0.67692308 0.734375 0.71428571] mean value: 0.6916962538568604 MCC on Blind test: 0.31 MCC on Training: 0.3 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.44977427 0.46484542 0.46696162 0.46973228 0.5795095 0.47411799 0.46748734 0.49569988 0.67120051 0.48661208] mean value: 0.5025940895080566 key: score_time value: [0.01220417 0.01202035 0.01206827 0.01213074 0.01209331 0.01200771 0.01217437 0.01211429 0.01222062 0.01212168] mean value: 0.012115550041198731 key: test_mcc value: [0.2508726 0.5 0.1767767 0.33333333 0.21969697 0.48856385 0.38932432 0.50168817 0.12878788 0.13740858] mean value: 0.3126452405042234 key: train_mcc value: [0.51430904 0.52390457 0.59179275 0.49544036 0.58376196 0.44099939 0.56429879 0.57485025 0.63111536 0.59473902] mean value: 0.551521148832717 key: test_fscore value: [0.64 0.75 0.5 0.66666667 0.60869565 0.75 0.66666667 0.78571429 0.58333333 0.54545455] mean value: 0.649653115000941 key: train_fscore value: [0.75829384 0.76415094 0.80184332 0.75117371 0.79816514 0.71770335 0.78703704 0.79262673 0.81860465 0.80365297] mean value: 0.7793251680395256 key: test_precision value: [0.61538462 0.75 0.625 0.66666667 0.58333333 0.69230769 0.7 0.6875 0.58333333 0.6 ] mean value: 0.650352564102564 key: train_precision value: [0.75471698 0.75700935 0.77678571 0.74074074 0.77678571 0.72815534 0.77272727 0.76785714 0.8 0.77192982] mean value: 0.7646708076190281 key: test_recall value: [0.66666667 0.75 0.41666667 0.66666667 0.63636364 0.81818182 0.63636364 0.91666667 0.58333333 0.5 ] mean value: 0.6590909090909091 key: train_recall value: [0.76190476 0.77142857 0.82857143 0.76190476 0.82075472 0.70754717 0.80188679 0.81904762 0.83809524 0.83809524] mean value: 0.7949236298292901 key: test_accuracy value: [0.625 0.75 0.58333333 0.66666667 0.60869565 0.73913043 0.69565217 0.73913043 0.56521739 0.56521739] mean value: 0.6538043478260869 key: train_accuracy value: [0.75714286 0.76190476 0.7952381 0.74761905 0.79146919 0.72037915 0.78199052 0.78672986 0.81516588 0.79620853] mean value: 0.7753847889866847 key: test_roc_auc value: [0.625 0.75 0.58333333 0.66666667 0.60984848 0.74242424 0.69318182 0.73106061 0.56439394 0.56818182] mean value: 0.6534090909090909 key: train_roc_auc value: [0.75714286 0.76190476 0.7952381 0.74761905 0.79132974 0.72044025 0.78189578 0.7868823 0.81527403 0.79640611] mean value: 0.7754132973944295 key: test_jcc value: [0.47058824 0.6 0.33333333 0.5 0.4375 0.6 0.5 0.64705882 0.41176471 0.375 ] mean value: 0.48752450980392154 key: train_jcc value: [0.61068702 0.61832061 0.66923077 0.60150376 0.66412214 0.55970149 0.64885496 0.65648855 0.69291339 0.67175573] mean value: 0.6393578414626938 MCC on Blind test: 0.34 MCC on Training: 0.31 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.87220836 0.70430398 0.25105882 0.55526018 0.40803099 0.85156965 0.39652443 0.32692862 0.43334723 0.93357396] mean value: 0.5732806205749512 key: score_time value: [0.01222396 0.01217961 0.0122366 0.01212072 0.01216602 0.01211643 0.01274419 0.0122385 0.0123508 0.01220226] mean value: 0.012257909774780274 key: test_mcc value: [0.27500955 0.64168895 0.25819889 0.3380617 0.12336594 0.76764947 0.38932432 0.48075018 0.13740858 0.04545455] mean value: 0.34569121301540584 key: train_mcc value: [0.69726485 0.64230629 0.64230366 0.6666969 0.54618957 0.68356842 0.62250193 0.48351089 0.6327778 0.81103931] mean value: 0.6428159626985638 key: test_fscore value: [0.68965517 0.82758621 0.57142857 0.63636364 0.44444444 0.88 0.66666667 0.76923077 0.54545455 0.52173913] mean value: 0.655256914333376 key: train_fscore value: [0.85321101 0.83193277 0.82882883 0.83253589 0.72826087 0.84955752 0.81818182 0.69565217 0.82191781 0.90654206] mean value: 0.8166620744357764 key: test_precision value: [0.58823529 0.70588235 0.66666667 0.7 0.57142857 0.78571429 0.7 0.71428571 0.6 0.54545455] mean value: 0.6577667430608607 key: train_precision value: [0.82300885 0.7443609 0.78632479 0.83653846 0.85897436 0.8 0.78947368 0.81012658 0.78947368 0.88990826] mean value: 0.8128189566231037 key: test_recall value: [0.83333333 1. 0.5 0.58333333 0.36363636 1. 0.63636364 0.83333333 0.5 0.5 ] mean value: 0.675 key: train_recall value: [0.88571429 0.94285714 0.87619048 0.82857143 0.63207547 0.90566038 0.8490566 0.60952381 0.85714286 0.92380952] mean value: 0.8310601976639711 key: test_accuracy value: [0.625 0.79166667 0.625 0.66666667 0.56521739 0.86956522 0.69565217 0.73913043 0.56521739 0.52173913] mean value: 0.6664855072463767 key: train_accuracy value: [0.84761905 0.80952381 0.81904762 0.83333333 0.76303318 0.83886256 0.81042654 0.73459716 0.81516588 0.90521327] mean value: 0.8176822387722862 key: test_roc_auc value: [0.625 0.79166667 0.625 0.66666667 0.55681818 0.875 0.69318182 0.73484848 0.56818182 0.52272727] mean value: 0.6659090909090909 key: train_roc_auc value: [0.84761905 0.80952381 0.81904762 0.83333333 0.76365678 0.83854447 0.81024259 0.73400719 0.81536388 0.90530099] mean value: 0.8176639712488768 key: test_jcc value: [0.52631579 0.70588235 0.4 0.46666667 0.28571429 0.78571429 0.5 0.625 0.375 0.35294118] mean value: 0.5023234556980687 key: train_jcc value: [0.744 0.71223022 0.70769231 0.71311475 0.57264957 0.73846154 0.69230769 0.53333333 0.69767442 0.82905983] mean value: 0.6940523662034623 MCC on Blind test: 0.2 MCC on Training: 0.35 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01500559 0.01268458 0.00893331 0.00882697 0.00854063 0.0085268 0.00854874 0.00866318 0.00841141 0.00848866] mean value: 0.009662985801696777 key: score_time value: [0.01154733 0.01016569 0.00878024 0.00859618 0.00835013 0.00835562 0.00838804 0.00837159 0.00826812 0.00846887] mean value: 0.008929181098937988 key: test_mcc value: [ 0. 0.41812101 0.3380617 0.50709255 0.13740858 0.15096491 0.48075018 0.3030303 0.21452908 -0.39393939] mean value: 0.21560189204639615 key: train_mcc value: [0.25756371 0.30488637 0.27664251 0.25789248 0.28949831 0.27986367 0.26113352 0.23296468 0.27059363 0.29101939] mean value: 0.2722058276618288 key: test_fscore value: [0.53846154 0.72 0.63636364 0.72727273 0.58333333 0.61538462 0.7 0.66666667 0.66666667 0.33333333] mean value: 0.6187482517482518 key: train_fscore value: [0.63888889 0.657277 0.64814815 0.64220183 0.65753425 0.65137615 0.64545455 0.62672811 0.64186047 0.66063348] mean value: 0.6470102865901719 key: test_precision value: [0.5 0.69230769 0.7 0.8 0.53846154 0.53333333 0.77777778 0.66666667 0.6 0.33333333] mean value: 0.6141880341880341 key: train_precision value: [0.62162162 0.64814815 0.63063063 0.61946903 0.63716814 0.63392857 0.62280702 0.60714286 0.62727273 0.62931034] mean value: 0.6277499086757594 key: test_recall value: [0.58333333 0.75 0.58333333 0.66666667 0.63636364 0.72727273 0.63636364 0.66666667 0.75 0.33333333] mean value: 0.6333333333333333 key: train_recall value: [0.65714286 0.66666667 0.66666667 0.66666667 0.67924528 0.66981132 0.66981132 0.64761905 0.65714286 0.6952381 ] mean value: 0.667601078167116 key: test_accuracy value: [0.5 0.70833333 0.66666667 0.75 0.56521739 0.56521739 0.73913043 0.65217391 0.60869565 0.30434783] mean value: 0.6059782608695652 key: train_accuracy value: [0.62857143 0.65238095 0.63809524 0.62857143 0.64454976 0.63981043 0.63033175 0.61611374 0.63507109 0.64454976] mean value: 0.6358045587903408 key: test_roc_auc value: [0.5 0.70833333 0.66666667 0.75 0.56818182 0.5719697 0.73484848 0.65151515 0.60227273 0.3030303 ] mean value: 0.6056818181818182 key: train_roc_auc value: [0.62857143 0.65238095 0.63809524 0.62857143 0.64438455 0.63966757 0.63014376 0.61626235 0.6351752 0.64478886] mean value: 0.6358041329739442 key: test_jcc value: [0.36842105 0.5625 0.46666667 0.57142857 0.41176471 0.44444444 0.53846154 0.5 0.5 0.2 ] mean value: 0.4563686979515153 key: train_jcc value: [0.46938776 0.48951049 0.47945205 0.47297297 0.48979592 0.4829932 0.47651007 0.45637584 0.47260274 0.49324324] mean value: 0.4782844277035821 MCC on Blind test: 0.26 MCC on Training: 0.22 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00867271 0.00923276 0.00954914 0.00954556 0.00965333 0.00866938 0.00970244 0.00882864 0.00952911 0.00953031] mean value: 0.009291338920593261 key: score_time value: [0.00837541 0.00923061 0.00898623 0.00837779 0.00902724 0.0086906 0.00921631 0.00869703 0.0087626 0.00905776] mean value: 0.008842158317565917 key: test_mcc value: [ 0.16903085 0.5 0.27500955 0.3380617 0.31252706 0.56490196 0.39727608 0.3030303 -0.33371191 0.06579517] mean value: 0.25919207575051406 key: train_mcc value: [0.46200712 0.48418344 0.46481611 0.47367875 0.46959234 0.4309405 0.45736428 0.46688943 0.5374259 0.45980998] mean value: 0.47067078393067074 key: test_fscore value: [0.54545455 0.75 0.52631579 0.63636364 0.55555556 0.76190476 0.63157895 0.66666667 0.44444444 0.42105263] mean value: 0.5939336978810664 key: train_fscore value: [0.68478261 0.71204188 0.70157068 0.70833333 0.70157068 0.68062827 0.70103093 0.70157068 0.73404255 0.69148936] mean value: 0.7017060983710535 key: test_precision value: [0.6 0.75 0.71428571 0.7 0.71428571 0.8 0.75 0.66666667 0.4 0.57142857] mean value: 0.6666666666666666 key: train_precision value: [0.79746835 0.79069767 0.77906977 0.7816092 0.78823529 0.76470588 0.77272727 0.77906977 0.8313253 0.78313253] mean value: 0.7868041039658166 key: test_recall value: [0.5 0.75 0.41666667 0.58333333 0.45454545 0.72727273 0.54545455 0.66666667 0.5 0.33333333] mean value: 0.5477272727272726 key: train_recall value: [0.6 0.64761905 0.63809524 0.64761905 0.63207547 0.61320755 0.64150943 0.63809524 0.65714286 0.61904762] mean value: 0.6334411500449237 key: test_accuracy value: [0.58333333 0.75 0.625 0.66666667 0.65217391 0.7826087 0.69565217 0.65217391 0.34782609 0.52173913] mean value: 0.6277173913043478 key: train_accuracy value: [0.72380952 0.73809524 0.72857143 0.73333333 0.72985782 0.71090047 0.72511848 0.72985782 0.76303318 0.72511848] mean value: 0.7307695779733695 key: test_roc_auc value: [0.58333333 0.75 0.625 0.66666667 0.64393939 0.78030303 0.68939394 0.65151515 0.34090909 0.53030303] mean value: 0.6261363636363637 key: train_roc_auc value: [0.72380952 0.73809524 0.72857143 0.73333333 0.73032345 0.71136568 0.72551662 0.72942498 0.76253369 0.72461815] mean value: 0.7307592093441151 key: test_jcc value: [0.375 0.6 0.35714286 0.46666667 0.38461538 0.61538462 0.46153846 0.5 0.28571429 0.26666667] mean value: 0.4312728937728938 key: train_jcc value: [0.52066116 0.55284553 0.54032258 0.5483871 0.54032258 0.51587302 0.53968254 0.54032258 0.57983193 0.52845528] mean value: 0.5406704297071265 MCC on Blind test: 0.21 MCC on Training: 0.26 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01286459 0.01426744 0.0142374 0.01420355 0.01665211 0.0146699 0.014709 0.01530814 0.01447821 0.01445246] mean value: 0.01458427906036377 key: score_time value: [0.00834966 0.01146698 0.01147652 0.01142836 0.01146865 0.01147223 0.01150584 0.01150656 0.01466799 0.01159501] mean value: 0.011493778228759766 key: test_mcc value: [0.09166985 0.3380617 0.25819889 0.53033009 0.13740858 0.65151515 0.32232919 0.47923384 0.03816905 0.20412415] mean value: 0.305104048264589 key: train_mcc value: [0.6350853 0.56561069 0.64999548 0.56854432 0.56333908 0.57362468 0.40909995 0.52007984 0.62147509 0.22410949] mean value: 0.533096390694719 key: test_fscore value: [0.62068966 0.63636364 0.57142857 0.7 0.58333333 0.81818182 0.30769231 0.77419355 0.56 0.15384615] mean value: 0.5725729024405332 key: train_fscore value: [0.82666667 0.76767677 0.83478261 0.7628866 0.7965368 0.79069767 0.48951049 0.7804878 0.8220339 0.17391304] mean value: 0.7045192348104516 key: test_precision value: [0.52941176 0.7 0.66666667 0.875 0.53846154 0.81818182 1. 0.63157895 0.53846154 1. ] mean value: 0.7297762273845866 key: train_precision value: [0.775 0.8172043 0.768 0.83146067 0.736 0.77981651 0.94594595 0.68085106 0.74045802 1. ] mean value: 0.807473651403695 key: test_recall value: [0.75 0.58333333 0.5 0.58333333 0.63636364 0.81818182 0.18181818 1. 0.58333333 0.08333333] mean value: 0.571969696969697 key: train_recall value: [0.88571429 0.72380952 0.91428571 0.7047619 0.86792453 0.80188679 0.33018868 0.91428571 0.92380952 0.0952381 ] mean value: 0.7161904761904763 key: test_accuracy value: [0.54166667 0.66666667 0.625 0.75 0.56521739 0.82608696 0.60869565 0.69565217 0.52173913 0.52173913] mean value: 0.6322463768115941 key: train_accuracy value: [0.81428571 0.78095238 0.81904762 0.78095238 0.77725118 0.78672986 0.65402844 0.74407583 0.80094787 0.54976303] mean value: 0.7508034303768901 key: test_roc_auc value: [0.54166667 0.66666667 0.625 0.75 0.56818182 0.82575758 0.59090909 0.68181818 0.51893939 0.54166667] mean value: 0.6310606060606061 key: train_roc_auc value: [0.81428571 0.78095238 0.81904762 0.78095238 0.77681941 0.78665768 0.65557053 0.74487871 0.8015274 0.54761905] mean value: 0.7508310871518418 key: test_jcc value: [0.45 0.46666667 0.4 0.53846154 0.41176471 0.69230769 0.18181818 0.63157895 0.38888889 0.08333333] mean value: 0.4244819954727076 key: train_jcc value: [0.70454545 0.62295082 0.71641791 0.61666667 0.6618705 0.65384615 0.32407407 0.64 0.69784173 0.0952381 ] mean value: 0.5733451404706164 MCC on Blind test: 0.26 MCC on Training: 0.31 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02062821 0.0245235 0.02095246 0.02456141 0.02106595 0.02358389 0.02084994 0.02491093 0.02107596 0.03569317] mean value: 0.023784542083740236 key: score_time value: [0.01644683 0.01232553 0.01507902 0.01239014 0.0148139 0.01240206 0.01479077 0.0123384 0.01486158 0.01236796] mean value: 0.01378161907196045 key: test_mcc value: [ 0.0836242 0. 0.2508726 0.33333333 0.65151515 0.5164589 0.06579517 0.50168817 0.03816905 -0.13740858] mean value: 0.23040479955723764 key: train_mcc value: [1. 1. 1. 1. 0.96277017 0.99056519 0.99056519 1. 0.98122024 0.99056604] mean value: 0.9915686838675395 key: test_fscore value: [0.52173913 0.625 0.64 0.66666667 0.81818182 0.76923077 0.59259259 0.78571429 0.56 0.48 ] mean value: 0.6459125262820915 key: train_fscore value: [1. 1. 1. 1. 0.98148148 0.99530516 0.99530516 1. 0.99056604 0.99526066] mean value: 0.9957918511362938 key: test_precision value: [0.54545455 0.5 0.61538462 0.66666667 0.81818182 0.66666667 0.5 0.6875 0.53846154 0.46153846] mean value: 0.5999854312354312 key: train_precision value: [1. 1. 1. 1. 0.96363636 0.99065421 0.99065421 1. 0.98130841 0.99056604] mean value: 0.991681922380212 key: test_recall value: [0.5 0.83333333 0.66666667 0.66666667 0.81818182 0.90909091 0.72727273 0.91666667 0.58333333 0.5 ] mean value: 0.7121212121212122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.54166667 0.5 0.625 0.66666667 0.82608696 0.73913043 0.52173913 0.73913043 0.52173913 0.43478261] mean value: 0.6115942028985507 key: train_accuracy value: [1. 1. 1. 1. 0.98104265 0.99526066 0.99526066 1. 0.99052133 0.99526066] mean value: 0.9957345971563981 key: test_roc_auc value: [0.54166667 0.5 0.625 0.66666667 0.82575758 0.74621212 0.53030303 0.73106061 0.51893939 0.43181818] mean value: 0.6117424242424242 key: train_roc_auc value: [1. 1. 1. 1. 0.98095238 0.9952381 0.9952381 1. 0.99056604 0.99528302] mean value: 0.9957277628032346 key: test_jcc value: [0.35294118 0.45454545 0.47058824 0.5 0.69230769 0.625 0.42105263 0.64705882 0.38888889 0.31578947] mean value: 0.48681723762993123 key: train_jcc value: [1. 1. 1. 1. 0.96363636 0.99065421 0.99065421 1. 0.98130841 0.99056604] mean value: 0.991681922380212 MCC on Blind test: 0.06 MCC on Training: 0.23 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.60305738 0.59016943 0.5603404 0.6613338 0.58712959 0.63041854 0.66684294 0.60210705 0.58082509 0.57971358] mean value: 0.6061937808990479 key: score_time value: [0.17108274 0.19113159 0.13092279 0.14182544 0.16424799 0.18305707 0.14756179 0.13294721 0.14118624 0.15010047] mean value: 0.15540633201599122 key: test_mcc value: [0.33333333 0.70710678 0.25819889 0.58536941 0.66414149 0.66414149 0.3030303 0.69084928 0.04545455 0.23262105] mean value: 0.4484246568065829 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.85714286 0.57142857 0.7826087 0.83333333 0.83333333 0.63636364 0.85714286 0.52173913 0.57142857] mean value: 0.7131187652926783 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.75 0.66666667 0.81818182 0.76923077 0.76923077 0.63636364 0.75 0.54545455 0.66666667] mean value: 0.7038461538461539 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.5 0.75 0.90909091 0.90909091 0.63636364 1. 0.5 0.5 ] mean value: 0.7371212121212121 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.83333333 0.625 0.79166667 0.82608696 0.82608696 0.65217391 0.82608696 0.52173913 0.60869565] mean value: 0.7177536231884056 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.83333333 0.625 0.79166667 0.82954545 0.82954545 0.65151515 0.81818182 0.52272727 0.61363636] mean value: 0.7181818181818181 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.75 0.4 0.64285714 0.71428571 0.71428571 0.46666667 0.75 0.35294118 0.4 ] mean value: 0.5691036414565827 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.45 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.92450237 0.86099005 0.86282849 0.94416547 0.91874242 0.86138916 0.92955279 0.88233066 0.93001795 0.89827776] mean value: 0.9012797117233277 key: score_time value: [0.24590278 0.16156793 0.17400312 0.16356587 0.21929407 0.17719865 0.20500374 0.17760921 0.1658721 0.19615078] mean value: 0.18861682415008546 key: test_mcc value: [0.41812101 0.77459667 0.25819889 0.5 0.47727273 0.6992059 0.48856385 0.48075018 0.04545455 0.23262105] mean value: 0.4374784816713692 key: train_mcc value: [0.87623022 0.90492608 0.90541914 0.85718173 0.86746339 0.89576782 0.89608068 0.8672956 0.89576782 0.88642286] mean value: 0.8852555322757955 key: test_fscore value: [0.72 0.88888889 0.57142857 0.75 0.72727273 0.84615385 0.75 0.76923077 0.52173913 0.57142857] mean value: 0.7116142504838157 key: train_fscore value: [0.93838863 0.95192308 0.95327103 0.92890995 0.93333333 0.94835681 0.94883721 0.93333333 0.94736842 0.94339623] mean value: 0.9427118014107968 key: test_precision value: [0.69230769 0.8 0.66666667 0.75 0.72727273 0.73333333 0.69230769 0.71428571 0.54545455 0.66666667] mean value: 0.6988295038295038 key: train_precision value: [0.93396226 0.96116505 0.93577982 0.9245283 0.94230769 0.94392523 0.93577982 0.93333333 0.95192308 0.93457944] mean value: 0.9397284023070247 key: test_recall value: [0.75 1. 0.5 0.75 0.72727273 1. 0.81818182 0.83333333 0.5 0.5 ] mean value: 0.7378787878787879 key: train_recall value: [0.94285714 0.94285714 0.97142857 0.93333333 0.9245283 0.95283019 0.96226415 0.93333333 0.94285714 0.95238095] mean value: 0.9458670260557053 key: test_accuracy value: [0.70833333 0.875 0.625 0.75 0.73913043 0.82608696 0.73913043 0.73913043 0.52173913 0.60869565] mean value: 0.7132246376811594 key: train_accuracy value: [0.93809524 0.95238095 0.95238095 0.92857143 0.93364929 0.9478673 0.9478673 0.93364929 0.9478673 0.94312796] mean value: 0.9425457007447529 key: test_roc_auc value: [0.70833333 0.875 0.625 0.75 0.73863636 0.83333333 0.74242424 0.73484848 0.52272727 0.61363636] mean value: 0.7143939393939394 key: train_roc_auc value: [0.93809524 0.95238095 0.95238095 0.92857143 0.93369272 0.94784367 0.94779874 0.9336478 0.94784367 0.94317161] mean value: 0.9425426774483376 key: test_jcc value: [0.5625 0.8 0.4 0.6 0.57142857 0.73333333 0.6 0.625 0.35294118 0.4 ] mean value: 0.5645203081232493 key: train_jcc value: [0.88392857 0.90825688 0.91071429 0.86725664 0.875 0.90178571 0.90265487 0.875 0.9 0.89285714] mean value: 0.8917454099444437 MCC on Blind test: 0.44 MCC on Training: 0.44 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03358984 0.0335083 0.03346014 0.02774668 0.03363514 0.03357577 0.03361392 0.03358865 0.03372002 0.03350782] mean value: 0.032994627952575684 key: score_time value: [0.02098298 0.02328897 0.02248788 0.02129364 0.02043962 0.02337074 0.02234101 0.02138257 0.02039456 0.02358174] mean value: 0.021956372261047363 key: test_mcc value: [0. 0.2508726 0.1767767 0.3380617 0.05427825 0.74242424 0.38932432 0.39727608 0.21969697 0.15096491] mean value: 0.2719675767286368 key: train_mcc value: [0.64788354 0.70504974 0.73453355 0.65762022 0.70688323 0.65973653 0.69677364 0.70640068 0.71619931 0.75483649] mean value: 0.6985916938633673 key: test_fscore value: [0.53846154 0.60869565 0.5 0.63636364 0.56 0.86956522 0.66666667 0.74074074 0.60869565 0.5 ] mean value: 0.6229189103971713 key: train_fscore value: [0.82125604 0.85446009 0.87037037 0.8317757 0.85714286 0.83486239 0.85046729 0.85024155 0.85981308 0.87962963] mean value: 0.8510018995668089 key: test_precision value: [0.5 0.63636364 0.625 0.7 0.5 0.83333333 0.7 0.66666667 0.63636364 0.625 ] mean value: 0.6422727272727273 key: train_precision value: [0.83333333 0.84259259 0.84684685 0.81651376 0.83783784 0.8125 0.84259259 0.8627451 0.8440367 0.85585586] mean value: 0.8394854615813871 key: test_recall value: [0.58333333 0.58333333 0.41666667 0.58333333 0.63636364 0.90909091 0.63636364 0.83333333 0.58333333 0.41666667] mean value: 0.6181818181818182 key: train_recall value: [0.80952381 0.86666667 0.8952381 0.84761905 0.87735849 0.85849057 0.85849057 0.83809524 0.87619048 0.9047619 ] mean value: 0.8632434860736747 key: test_accuracy value: [0.5 0.625 0.58333333 0.66666667 0.52173913 0.86956522 0.69565217 0.69565217 0.60869565 0.56521739] mean value: 0.6331521739130435 key: train_accuracy value: [0.82380952 0.85238095 0.86666667 0.82857143 0.85308057 0.82938389 0.84834123 0.85308057 0.85781991 0.87677725] mean value: 0.8489911983750845 key: test_roc_auc value: [0.5 0.625 0.58333333 0.66666667 0.52651515 0.87121212 0.69318182 0.68939394 0.60984848 0.5719697 ] mean value: 0.6337121212121213 key: train_roc_auc value: [0.82380952 0.85238095 0.86666667 0.82857143 0.85296496 0.82924528 0.8482929 0.85300988 0.85790656 0.87690925] mean value: 0.8489757412398922 key: test_jcc value: [0.36842105 0.4375 0.33333333 0.46666667 0.38888889 0.76923077 0.5 0.58823529 0.4375 0.33333333] mean value: 0.4623109338202217 key: train_jcc value: [0.69672131 0.74590164 0.7704918 0.712 0.75 0.71653543 0.7398374 0.7394958 0.75409836 0.78512397] mean value: 0.7410205711460425 MCC on Blind test: 0.12 MCC on Training: 0.27 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13834262 0.12272286 0.13430333 0.10757828 0.10964346 0.10319495 0.10202432 0.10106087 0.09780383 0.0960784 ] mean value: 0.1112752914428711 key: score_time value: [0.02385759 0.02222061 0.02212667 0.0219152 0.0230813 0.02135849 0.02197075 0.02371621 0.01617312 0.0242126 ] mean value: 0.022063255310058594 key: test_mcc value: [0.0836242 0.33333333 0.1767767 0.41812101 0.21969697 0.74242424 0.30240737 0.50168817 0.03816905 0.13740858] mean value: 0.29536496173005955 key: train_mcc value: [0.56282441 0.59071735 0.62006079 0.58264597 0.58376196 0.53603288 0.58327362 0.57571963 0.6417783 0.60376701] mean value: 0.5880581910516357 key: test_fscore value: [0.56 0.66666667 0.5 0.69565217 0.60869565 0.86956522 0.6 0.78571429 0.56 0.54545455] mean value: 0.6391748541313758 key: train_fscore value: [0.78703704 0.79812207 0.81481481 0.79816514 0.79816514 0.77419355 0.7962963 0.79452055 0.82568807 0.80733945] mean value: 0.7994342108373289 key: test_precision value: [0.53846154 0.66666667 0.625 0.72727273 0.58333333 0.83333333 0.66666667 0.6875 0.53846154 0.6 ] mean value: 0.6466695804195803 key: train_precision value: [0.76576577 0.78703704 0.79279279 0.7699115 0.77678571 0.75675676 0.78181818 0.76315789 0.79646018 0.77876106] mean value: 0.7769246886555923 key: test_recall value: [0.58333333 0.66666667 0.41666667 0.66666667 0.63636364 0.90909091 0.54545455 0.91666667 0.58333333 0.5 ] mean value: 0.6424242424242423 key: train_recall value: [0.80952381 0.80952381 0.83809524 0.82857143 0.82075472 0.79245283 0.81132075 0.82857143 0.85714286 0.83809524] mean value: 0.8234052111410601 key: test_accuracy value: [0.54166667 0.66666667 0.58333333 0.70833333 0.60869565 0.86956522 0.65217391 0.73913043 0.52173913 0.56521739] mean value: 0.6456521739130434 key: train_accuracy value: [0.78095238 0.7952381 0.80952381 0.79047619 0.79146919 0.76777251 0.79146919 0.78672986 0.81990521 0.80094787] mean value: 0.7934484315053036 key: test_roc_auc value: [0.54166667 0.66666667 0.58333333 0.70833333 0.60984848 0.87121212 0.64772727 0.73106061 0.51893939 0.56818182] mean value: 0.6446969696969698 key: train_roc_auc value: [0.78095238 0.7952381 0.80952381 0.79047619 0.79132974 0.76765499 0.79137466 0.78692722 0.82008086 0.80112309] mean value: 0.7934681042228212 key: test_jcc value: [0.38888889 0.5 0.33333333 0.53333333 0.4375 0.76923077 0.42857143 0.64705882 0.38888889 0.375 ] mean value: 0.4801805465776054 key: train_jcc value: [0.64885496 0.6640625 0.6875 0.66412214 0.66412214 0.63157895 0.66153846 0.65909091 0.703125 0.67692308] mean value: 0.666091813156209 MCC on Blind test: 0.4 MCC on Training: 0.3 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02727127 0.01274085 0.01221085 0.01271701 0.0113523 0.01128554 0.01122403 0.01211596 0.01319265 0.01360941] mean value: 0.013771986961364746 key: score_time value: [0.01153517 0.00962973 0.00990653 0.00979447 0.0097785 0.00939155 0.00955057 0.00934768 0.01027346 0.01041222] mean value: 0.009961986541748047 key: test_mcc value: [ 0.41812101 0.70710678 0.25819889 0.41812101 0.12406456 0.58930667 0.38932432 0.42228828 -0.05427825 -0.13740858] mean value: 0.31348446802968716 key: train_mcc value: [0.56354282 0.63821102 0.60273974 0.60174912 0.61196637 0.52740498 0.6027556 0.56788791 0.614906 0.58575133] mean value: 0.5916914887161479 key: test_fscore value: [0.72 0.85714286 0.57142857 0.69565217 0.5 0.8 0.66666667 0.75862069 0.53846154 0.48 ] mean value: 0.6587972497267851 key: train_fscore value: [0.78899083 0.82075472 0.80909091 0.80733945 0.81105991 0.77272727 0.80733945 0.79279279 0.81447964 0.8 ] mean value: 0.80245749622059 key: test_precision value: [0.69230769 0.75 0.66666667 0.72727273 0.55555556 0.71428571 0.7 0.64705882 0.5 0.46153846] mean value: 0.6414685641156229 key: train_precision value: [0.76106195 0.81308411 0.77391304 0.77876106 0.79279279 0.74561404 0.78571429 0.75213675 0.77586207 0.76521739] mean value: 0.7744157490478765 key: test_recall value: [0.75 1. 0.5 0.66666667 0.45454545 0.90909091 0.63636364 0.91666667 0.58333333 0.5 ] mean value: 0.6916666666666667 key: train_recall value: [0.81904762 0.82857143 0.84761905 0.83809524 0.83018868 0.80188679 0.83018868 0.83809524 0.85714286 0.83809524] mean value: 0.8328930817610063 key: test_accuracy value: [0.70833333 0.83333333 0.625 0.70833333 0.56521739 0.7826087 0.69565217 0.69565217 0.47826087 0.43478261] mean value: 0.6527173913043478 key: train_accuracy value: [0.78095238 0.81904762 0.8 0.8 0.8056872 0.76303318 0.80094787 0.78199052 0.8056872 0.79146919] mean value: 0.7948815165876778 key: test_roc_auc value: [0.70833333 0.83333333 0.625 0.70833333 0.56060606 0.78787879 0.69318182 0.68560606 0.47348485 0.43181818] mean value: 0.6507575757575758 key: train_roc_auc value: [0.78095238 0.81904762 0.8 0.8 0.80557053 0.76284816 0.80080863 0.78225517 0.80592992 0.79168913] mean value: 0.7949101527403415 key: test_jcc value: [0.5625 0.75 0.4 0.53333333 0.33333333 0.66666667 0.5 0.61111111 0.36842105 0.31578947] mean value: 0.5041154970760233 key: train_jcc value: [0.65151515 0.696 0.67938931 0.67692308 0.68217054 0.62962963 0.67692308 0.65671642 0.6870229 0.66666667] mean value: 0.6702956775944167 MCC on Blind test: 0.29 MCC on Training: 0.31 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01130414 0.01373267 0.01478577 0.01677394 0.01582932 0.01575899 0.01449895 0.01631045 0.01679516 0.01670623] mean value: 0.01524956226348877 key: score_time value: [0.0082643 0.01108956 0.01128626 0.01171899 0.01161718 0.01162314 0.01160717 0.01164651 0.01172924 0.01194501] mean value: 0.011252737045288086 key: test_mcc value: [ 0. 0.16903085 0.25819889 0.38490018 0.25495628 0.58930667 0.40451992 0.33946383 -0.06579517 -0.02585438] mean value: 0.23087270595980688 key: train_mcc value: [0.62462474 0.54173634 0.66014353 0.67206021 0.58143833 0.67956009 0.44877406 0.50663149 0.4564627 0.66078394] mean value: 0.5832215421837577 key: test_fscore value: [0.6 0.54545455 0.57142857 0.55555556 0.66666667 0.8 0.42857143 0.73333333 0.57142857 0.33333333] mean value: 0.5805772005772005 key: train_fscore value: [0.82142857 0.71186441 0.83636364 0.80851064 0.80672269 0.84545455 0.55629139 0.7751938 0.75457875 0.83486239] mean value: 0.7751270816477861 key: test_precision value: [0.5 0.6 0.66666667 0.83333333 0.5625 0.71428571 1. 0.61111111 0.5 0.5 ] mean value: 0.6487896825396826 key: train_precision value: [0.77310924 0.875 0.8 0.91566265 0.72727273 0.81578947 0.93333333 0.65359477 0.61309524 0.80530973] mean value: 0.7912167172440502 key: test_recall value: [0.75 0.5 0.5 0.41666667 0.81818182 0.90909091 0.27272727 0.91666667 0.66666667 0.25 ] mean value: 0.6000000000000001 key: train_recall value: [0.87619048 0.6 0.87619048 0.72380952 0.90566038 0.87735849 0.39622642 0.95238095 0.98095238 0.86666667] mean value: 0.8055435759209345 key: test_accuracy value: [0.5 0.58333333 0.625 0.66666667 0.60869565 0.7826087 0.65217391 0.65217391 0.47826087 0.47826087] mean value: 0.6027173913043479 key: train_accuracy value: [0.80952381 0.75714286 0.82857143 0.82857143 0.78199052 0.83886256 0.68246445 0.72511848 0.68246445 0.82938389] mean value: 0.7764093883999097 key: test_roc_auc value: [0.5 0.58333333 0.625 0.66666667 0.61742424 0.78787879 0.63636364 0.64015152 0.46969697 0.48863636] mean value: 0.6015151515151514 key: train_roc_auc value: [0.80952381 0.75714286 0.82857143 0.82857143 0.78140162 0.83867925 0.68382749 0.72619048 0.68387242 0.82955975] mean value: 0.7767340521114107 key: test_jcc value: [0.42857143 0.375 0.4 0.38461538 0.5 0.66666667 0.27272727 0.57894737 0.4 0.2 ] mean value: 0.4206528121001806 key: train_jcc value: [0.6969697 0.55263158 0.71875 0.67857143 0.67605634 0.73228346 0.3853211 0.63291139 0.60588235 0.71653543] mean value: 0.6395912786418129 MCC on Blind test: 0.31 MCC on Training: 0.23 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.12990785 0.06458378 0.07561016 0.06519628 0.06966972 0.07552505 0.06632233 0.06685185 0.06380606 0.06874275] mean value: 0.07462158203125 key: score_time value: [0.01308036 0.01088381 0.01121831 0.01079464 0.01116204 0.01054907 0.01055884 0.01063466 0.01053572 0.01135373] mean value: 0.011077117919921876 key: test_mcc value: [0.50709255 0.58536941 0.43033148 0.6761234 0.74047959 0.66414149 0.31298622 0.76277007 0.39393939 0.5164589 ] mean value: 0.5589692510934511 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.7826087 0.66666667 0.81818182 0.85714286 0.83333333 0.66666667 0.88888889 0.69565217 0.7 ] mean value: 0.7636413827718175 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.81818182 0.77777778 0.9 0.9 0.76923077 0.61538462 0.8 0.72727273 0.875 ] mean value: 0.7982847707847708 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.75 0.58333333 0.75 0.81818182 0.90909091 0.72727273 1. 0.66666667 0.58333333] mean value: 0.7454545454545455 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.79166667 0.70833333 0.83333333 0.86956522 0.82608696 0.65217391 0.86956522 0.69565217 0.73913043] mean value: 0.7735507246376812 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.79166667 0.70833333 0.83333333 0.86742424 0.82954545 0.65530303 0.86363636 0.6969697 0.74621212] mean value: 0.7742424242424242 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.64285714 0.5 0.69230769 0.75 0.71428571 0.5 0.8 0.53333333 0.53846154] mean value: 0.6242673992673993 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.56 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 3@q"QgU@$b[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... 4ZVОZV[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Yn"$b^Iw5S@&%4jm M(\!Rf~y9l[ Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.14843869 0.14341378 0.13961625 0.13638043 0.13852978 0.13826847 0.13820958 0.13102293 0.13188434 0.13406277] mean value: 0.13798270225524903 key: score_time value: [0.01633048 0.01562572 0.01573634 0.01492095 0.01558375 0.01527524 0.01462364 0.01468468 0.01476264 0.01490068] mean value: 0.015244412422180175 key: test_mcc value: [0.65158377 0.58925565 0.5976143 0.64705882 0.63602941 0.40105559 0.57720588 0.65806217 0.39407655 0.33210558] mean value: 0.5484047731767584 key: train_mcc value: [0.92131124 0.92032729 0.92032729 0.94675083 0.9205971 0.94692923 0.92742942 0.8943837 0.9274135 0.92034969] mean value: 0.9245819269457604 key: test_fscore value: [0.8125 0.78787879 0.77419355 0.82352941 0.8125 0.64285714 0.78787879 0.8 0.72222222 0.68571429] mean value: 0.764927418670303 key: train_fscore value: [0.95890411 0.95945946 0.95945946 0.97315436 0.95973154 0.97333333 0.96296296 0.94557823 0.96271186 0.9602649 ] mean value: 0.9615560227206073 key: test_precision value: [0.86666667 0.8125 0.85714286 0.82352941 0.8125 0.75 0.76470588 0.92307692 0.68421053 0.66666667] mean value: 0.7960998933986551 key: train_precision value: [0.98591549 0.97260274 0.97260274 0.97972973 0.97278912 0.97986577 0.97945205 0.96527778 0.97931034 0.95394737] mean value: 0.9741493135418808 key: test_recall value: [0.76470588 0.76470588 0.70588235 0.82352941 0.8125 0.5625 0.8125 0.70588235 0.76470588 0.70588235] mean value: 0.7422794117647059 key: train_recall value: [0.93333333 0.94666667 0.94666667 0.96666667 0.94701987 0.96688742 0.94701987 0.92666667 0.94666667 0.96666667] mean value: 0.9494260485651214 key: test_accuracy value: [0.82352941 0.79411765 0.79411765 0.82352941 0.81818182 0.6969697 0.78787879 0.81818182 0.6969697 0.66666667] mean value: 0.7720142602495544 key: train_accuracy value: [0.96 0.96 0.96 0.97333333 0.96013289 0.97342193 0.96345515 0.94684385 0.96345515 0.96013289] mean value: 0.962077519379845 key: test_roc_auc value: [0.82352941 0.79411765 0.79411765 0.82352941 0.81801471 0.69301471 0.78860294 0.82169118 0.69485294 0.66544118] mean value: 0.7716911764705883 key: train_roc_auc value: [0.96 0.96 0.96 0.97333333 0.9601766 0.97344371 0.96350993 0.94677704 0.96339956 0.96015453] mean value: 0.9620794701986755 key: test_jcc value: [0.68421053 0.65 0.63157895 0.7 0.68421053 0.47368421 0.65 0.66666667 0.56521739 0.52173913] mean value: 0.6227307398932111 key: train_jcc value: [0.92105263 0.92207792 0.92207792 0.94771242 0.92258065 0.94805195 0.92857143 0.89677419 0.92810458 0.92356688] mean value: 0.926057056351279 MCC on Blind test: 0.28 MCC on Training: 0.55 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.16630983 0.20627904 0.2167666 0.194417 0.20981383 0.26563835 0.20427775 0.22519565 0.20406437 0.2124722 ] mean value: 0.21052346229553223 key: score_time value: [0.05558014 0.0756309 0.04638696 0.03827381 0.08370471 0.05080271 0.06721973 0.05629706 0.05962324 0.05142665] mean value: 0.05849459171295166 key: test_mcc value: [0.65158377 0.58925565 0.77005354 0.65158377 0.81919377 0.51470588 0.69852941 0.68599434 0.45588235 0.63944497] mean value: 0.6476227451525367 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8125 0.78787879 0.875 0.8125 0.90322581 0.75 0.84848485 0.78571429 0.72727273 0.83333333] mean value: 0.8135909789135596 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86666667 0.8125 0.93333333 0.86666667 0.93333333 0.75 0.82352941 1. 0.75 0.78947368] mean value: 0.8525503095975232 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.76470588 0.76470588 0.82352941 0.76470588 0.875 0.75 0.875 0.64705882 0.70588235 0.88235294] mean value: 0.7852941176470587 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82352941 0.79411765 0.88235294 0.82352941 0.90909091 0.75757576 0.84848485 0.81818182 0.72727273 0.81818182] mean value: 0.8202317290552583 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82352941 0.79411765 0.88235294 0.82352941 0.90808824 0.75735294 0.84926471 0.82352941 0.72794118 0.81617647] mean value: 0.8205882352941176 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68421053 0.65 0.77777778 0.68421053 0.82352941 0.6 0.73684211 0.64705882 0.57142857 0.71428571] mean value: 0.6889343456680919 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.44 MCC on Training: 0.65 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03207135 0.01882935 0.02113914 0.0244031 0.02321386 0.02196598 0.02037096 0.02044177 0.01859593 0.02119875] mean value: 0.022223019599914552 key: score_time value: [0.01149631 0.00893474 0.00921202 0.00929642 0.0096333 0.00868011 0.00852513 0.00893807 0.00878787 0.00866914] mean value: 0.009217309951782226 key: test_mcc value: [0.47140452 0.47809144 0.71713717 0.5976143 0.70694678 0.39338235 0.63602941 0.5573704 0.15073529 0.51470588] mean value: 0.5223417561857766 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.75675676 0.83870968 0.77419355 0.82758621 0.6875 0.8125 0.71428571 0.58823529 0.76470588] mean value: 0.749174580748879 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.7 0.92857143 0.85714286 0.92307692 0.6875 0.8125 0.90909091 0.58823529 0.76470588] mean value: 0.7920823294352706 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.70588235 0.82352941 0.76470588 0.70588235 0.75 0.6875 0.8125 0.58823529 0.58823529 0.76470588] mean value: 0.7191176470588234 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73529412 0.73529412 0.85294118 0.79411765 0.84848485 0.6969697 0.81818182 0.75757576 0.57575758 0.75757576] mean value: 0.7572192513368984 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.73529412 0.73529412 0.85294118 0.79411765 0.84558824 0.69669118 0.81801471 0.76286765 0.57536765 0.75735294] mean value: 0.7573529411764705 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.60869565 0.72222222 0.63157895 0.70588235 0.52380952 0.68421053 0.55555556 0.41666667 0.61904762] mean value: 0.603909763752946 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.2 MCC on Training: 0.52 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.017591 0.01171827 0.00921941 0.00920153 0.01019311 0.01024985 0.00926685 0.01065803 0.0093019 0.00935411] mean value: 0.010675406455993653 key: score_time value: [0.01831937 0.00924683 0.00858188 0.00849247 0.00886202 0.00931239 0.00853181 0.00885487 0.00853467 0.00855827] mean value: 0.009729456901550294 key: test_mcc value: [0.47809144 0.06052275 0.71713717 0.47809144 0.51676076 0.27205882 0.39338235 0.53397044 0.57564968 0.27205882] mean value: 0.4297723687256528 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.70967742 0.46666667 0.83870968 0.70967742 0.73333333 0.625 0.6875 0.73333333 0.8 0.64705882] mean value: 0.6950956672991777 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.78571429 0.53846154 0.92857143 0.78571429 0.78571429 0.625 0.6875 0.84615385 0.77777778 0.64705882] mean value: 0.7407666271636859 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.64705882 0.41176471 0.76470588 0.64705882 0.6875 0.625 0.6875 0.64705882 0.82352941 0.64705882] mean value: 0.6588235294117647 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73529412 0.52941176 0.85294118 0.73529412 0.75757576 0.63636364 0.6969697 0.75757576 0.78787879 0.63636364] mean value: 0.7125668449197862 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.73529412 0.52941176 0.85294118 0.73529412 0.75551471 0.63602941 0.69669118 0.76102941 0.78676471 0.63602941] mean value: 0.7124999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.55 0.30434783 0.72222222 0.55 0.57894737 0.45454545 0.52380952 0.57894737 0.66666667 0.47826087] mean value: 0.5407747299738148 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.43 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11966443 0.11320066 0.11678886 0.10809565 0.11235976 0.10666728 0.10712647 0.1072576 0.10699821 0.11059785] mean value: 0.11087567806243896 key: score_time value: [0.01917219 0.017946 0.01994944 0.01744437 0.01742792 0.01713157 0.01710033 0.01720548 0.01706457 0.01881957] mean value: 0.01792614459991455 key: test_mcc value: [0.70710678 0.52941176 0.77005354 0.65158377 0.81985294 0.3985267 0.63944497 0.81985294 0.33210558 0.27500955] mean value: 0.5942948536172542 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84848485 0.76470588 0.875 0.8125 0.90909091 0.70588235 0.8 0.90909091 0.68571429 0.68421053] mean value: 0.799467971399086 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.76470588 0.93333333 0.86666667 0.88235294 0.66666667 0.85714286 0.9375 0.66666667 0.61904762] mean value: 0.8069082633053222 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82352941 0.76470588 0.82352941 0.76470588 0.9375 0.75 0.75 0.88235294 0.70588235 0.76470588] mean value: 0.7966911764705882 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85294118 0.76470588 0.88235294 0.82352941 0.90909091 0.6969697 0.81818182 0.90909091 0.66666667 0.63636364] mean value: 0.7959893048128343 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85294118 0.76470588 0.88235294 0.82352941 0.90992647 0.69852941 0.81617647 0.90992647 0.66544118 0.63235294] mean value: 0.7955882352941177 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73684211 0.61904762 0.77777778 0.68421053 0.83333333 0.54545455 0.66666667 0.83333333 0.52173913 0.52 ] mean value: 0.6738405037627005 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.59 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.49968219 0.49696374 0.50612879 0.50840759 0.49402905 0.50346947 0.50014472 0.50664353 0.50207472 0.48569894] mean value: 0.5003242731094361 key: score_time value: [0.00993586 0.00987768 0.01037359 0.00963902 0.0103085 0.0099802 0.00939965 0.00966311 0.0097332 0.00928879] mean value: 0.009819960594177246 key: test_mcc value: [0.65158377 0.47140452 0.82495791 0.70710678 0.76212918 0.46471292 0.75735294 0.60731275 0.39338235 0.63944497] mean value: 0.6279388095646923 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8125 0.74285714 0.90909091 0.84848485 0.86666667 0.74285714 0.875 0.75862069 0.70588235 0.83333333] mean value: 0.8095293085886393 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86666667 0.72222222 0.9375 0.875 0.92857143 0.68421053 0.875 0.91666667 0.70588235 0.78947368] mean value: 0.8301193547594476 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.76470588 0.76470588 0.88235294 0.82352941 0.8125 0.8125 0.875 0.64705882 0.70588235 0.88235294] mean value: 0.7970588235294118 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82352941 0.73529412 0.91176471 0.85294118 0.87878788 0.72727273 0.87878788 0.78787879 0.6969697 0.81818182] mean value: 0.8111408199643494 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82352941 0.73529412 0.91176471 0.85294118 0.87683824 0.72977941 0.87867647 0.79227941 0.69669118 0.81617647] mean value: 0.8113970588235293 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68421053 0.59090909 0.83333333 0.73684211 0.76470588 0.59090909 0.77777778 0.61111111 0.54545455 0.71428571] mean value: 0.6849539177712551 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.63 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00958061 0.00968814 0.01033187 0.01090837 0.01041102 0.01126266 0.01399159 0.01095557 0.01089358 0.01015759] mean value: 0.010818099975585938 key: score_time value: [0.00910878 0.00926638 0.00946021 0.01029301 0.00999856 0.00995803 0.01049614 0.00956845 0.00974035 0.00998998] mean value: 0.009787988662719727 key: test_mcc value: [ 0.23570226 0.53311399 0.36927447 0.36927447 0.46471292 -0.02941176 -0.02941176 0.33455882 0.33210558 0.33210558] mean value: 0.2912024569953509 key: train_mcc value: [0.36706658 0.36080268 0.32831235 0.32725626 0.34245451 0.39190664 0.35109339 0.35595932 0.26904918 0.38487575] mean value: 0.34787766598706515 key: test_fscore value: [0.60606061 0.75 0.71794872 0.71794872 0.74285714 0.48484848 0.48484848 0.66666667 0.68571429 0.68571429] mean value: 0.6542607392607394 key: train_fscore value: [0.69055375 0.69032258 0.67936508 0.67313916 0.67961165 0.71428571 0.69375 0.68403909 0.68681319 0.66903915] mean value: 0.6860919349954325 key: test_precision value: [0.625 0.8 0.63636364 0.63636364 0.68421053 0.47058824 0.47058824 0.6875 0.66666667 0.66666667] mean value: 0.6343947602964631 key: train_precision value: [0.67515924 0.66875 0.64848485 0.65408805 0.66455696 0.67251462 0.65680473 0.66878981 0.58411215 0.71755725] mean value: 0.6610817660462576 key: test_recall value: [0.58823529 0.70588235 0.82352941 0.82352941 0.8125 0.5 0.5 0.64705882 0.70588235 0.70588235] mean value: 0.68125 key: train_recall value: [0.70666667 0.71333333 0.71333333 0.69333333 0.69536424 0.7615894 0.73509934 0.7 0.83333333 0.62666667] mean value: 0.7178719646799118 key: test_accuracy value: [0.61764706 0.76470588 0.67647059 0.67647059 0.72727273 0.48484848 0.48484848 0.66666667 0.66666667 0.66666667] mean value: 0.6432263814616757 key: train_accuracy value: [0.68333333 0.68 0.66333333 0.66333333 0.67109635 0.69435216 0.6744186 0.67774086 0.62126246 0.6910299 ] mean value: 0.6719900332225913 key: test_roc_auc value: [0.61764706 0.76470588 0.67647059 0.67647059 0.72977941 0.48529412 0.48529412 0.66727941 0.66544118 0.66544118] mean value: 0.6433823529411764 key: train_roc_auc value: [0.68333333 0.68 0.66333333 0.66333333 0.67101545 0.69412804 0.67421634 0.67781457 0.62196468 0.69081678] mean value: 0.6719955849889624 key: test_jcc value: [0.43478261 0.6 0.56 0.56 0.59090909 0.32 0.32 0.5 0.52173913 0.52173913] mean value: 0.4929169960474308 key: train_jcc value: [0.52736318 0.5270936 0.51442308 0.50731707 0.51470588 0.55555556 0.53110048 0.51980198 0.52301255 0.5026738 ] mean value: 0.5223047175900639 MCC on Blind test: 0.29 MCC on Training: 0.29 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.04768991 0.05763865 0.04772449 0.04992962 0.04801822 0.08722663 0.08276033 0.11362314 0.09283543 0.07646346] mean value: 0.07039098739624024 key: score_time value: [0.01394558 0.01389623 0.01387691 0.01382017 0.01747584 0.01364422 0.02708268 0.02075744 0.02074909 0.0132587 ] mean value: 0.01685068607330322 key: test_mcc value: [0.36927447 0.36927447 0.70710678 0.49236596 0.52029875 0.27139234 0.63944497 0.64207079 0.15073529 0.33455882] mean value: 0.4496522660133456 key: train_mcc value: [0.94018806 0.92668726 0.90666667 0.93341631 0.91380758 0.94760348 0.94718251 0.92692834 0.94758978 0.94021811] mean value: 0.9330288092633735 key: test_fscore value: [0.62068966 0.62068966 0.84848485 0.68965517 0.76470588 0.6 0.8 0.8125 0.58823529 0.66666667] mean value: 0.7011627174380725 key: train_fscore value: [0.96969697 0.9632107 0.95333333 0.96644295 0.95652174 0.97297297 0.97315436 0.9632107 0.97278912 0.96989967] mean value: 0.9661232516450327 key: test_precision value: [0.75 0.75 0.875 0.83333333 0.72222222 0.64285714 0.85714286 0.86666667 0.58823529 0.6875 ] mean value: 0.7572957516339869 key: train_precision value: [0.97959184 0.96644295 0.95333333 0.97297297 0.96621622 0.99310345 0.98639456 0.96644295 0.99305556 0.97315436] mean value: 0.9750708189368138 key: test_recall value: [0.52941176 0.52941176 0.82352941 0.58823529 0.8125 0.5625 0.75 0.76470588 0.58823529 0.64705882] mean value: 0.6595588235294118 key: train_recall value: [0.96 0.96 0.95333333 0.96 0.94701987 0.95364238 0.9602649 0.96 0.95333333 0.96666667] mean value: 0.9574260485651214 key: test_accuracy value: [0.67647059 0.67647059 0.85294118 0.73529412 0.75757576 0.63636364 0.81818182 0.81818182 0.57575758 0.66666667] mean value: 0.7213903743315508 key: train_accuracy value: [0.97 0.96333333 0.95333333 0.96666667 0.95681063 0.97342193 0.97342193 0.96345515 0.97342193 0.97009967] mean value: 0.9663964562569214 key: test_roc_auc value: [0.67647059 0.67647059 0.85294118 0.73529412 0.75919118 0.63419118 0.81617647 0.81985294 0.57536765 0.66727941] mean value: 0.7213235294117647 key: train_roc_auc value: [0.97 0.96333333 0.95333333 0.96666667 0.95684327 0.97348786 0.97346578 0.96344371 0.97335541 0.9700883 ] mean value: 0.9664017660044151 key: test_jcc value: [0.45 0.45 0.73684211 0.52631579 0.61904762 0.42857143 0.66666667 0.68421053 0.41666667 0.5 ] mean value: 0.5478320802005012 key: train_jcc value: [0.94117647 0.92903226 0.91082803 0.93506494 0.91666667 0.94736842 0.94771242 0.92903226 0.94701987 0.94155844] mean value: 0.9345459762387971 MCC on Blind test: 0.1 MCC on Training: 0.45 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0209043 0.01098728 0.01069832 0.01052451 0.01035047 0.01012397 0.00994682 0.0104568 0.01055121 0.0102613 ] mean value: 0.011480498313903808 key: score_time value: [0.01281333 0.01522231 0.0142858 0.01313829 0.0116539 0.01179552 0.01189017 0.0116365 0.012712 0.01191378] mean value: 0.012706160545349121 key: test_mcc value: [ 0.18156826 0. 0.42365927 0.24618298 0.16169528 0.09191176 0.33210558 0.09191176 -0.09191176 0.34202871] mean value: 0.17791518422684677 key: train_mcc value: [0.52018499 0.51425984 0.49350883 0.54686114 0.5287335 0.54152501 0.51528274 0.54152501 0.53487859 0.54166115] mean value: 0.5278420795229041 key: test_fscore value: [0.53333333 0.4516129 0.66666667 0.55172414 0.61111111 0.54545455 0.64516129 0.54545455 0.47058824 0.64516129] mean value: 0.5666268059116322 key: train_fscore value: [0.76315789 0.76375405 0.75 0.77631579 0.77022654 0.77227723 0.75420875 0.76923077 0.76666667 0.77227723] mean value: 0.7658114912286532 key: test_precision value: [0.61538462 0.5 0.76923077 0.66666667 0.55 0.52941176 0.66666667 0.5625 0.47058824 0.71428571] mean value: 0.6044734432234432 key: train_precision value: [0.75324675 0.74213836 0.74025974 0.76623377 0.75316456 0.76973684 0.76712329 0.77181208 0.76666667 0.76470588] mean value: 0.7595087940815176 key: test_recall value: [0.47058824 0.41176471 0.58823529 0.47058824 0.6875 0.5625 0.625 0.52941176 0.47058824 0.58823529] mean value: 0.5404411764705882 key: train_recall value: [0.77333333 0.78666667 0.76 0.78666667 0.78807947 0.77483444 0.74172185 0.76666667 0.76666667 0.78 ] mean value: 0.7724635761589405 key: test_accuracy value: [0.58823529 0.5 0.70588235 0.61764706 0.57575758 0.54545455 0.66666667 0.54545455 0.45454545 0.66666667] mean value: 0.5866310160427808 key: train_accuracy value: [0.76 0.75666667 0.74666667 0.77333333 0.7641196 0.77076412 0.75747508 0.77076412 0.76744186 0.77076412] mean value: 0.7637995570321151 key: test_roc_auc value: [0.58823529 0.5 0.70588235 0.61764706 0.57904412 0.54595588 0.66544118 0.54595588 0.45404412 0.66911765] mean value: 0.5871323529411765 key: train_roc_auc value: [0.76 0.75666667 0.74666667 0.77333333 0.76403974 0.77075055 0.75752759 0.77075055 0.76743929 0.7707947 ] mean value: 0.7637969094922736 key: test_jcc value: [0.36363636 0.29166667 0.5 0.38095238 0.44 0.375 0.47619048 0.375 0.30769231 0.47619048] mean value: 0.3986328671328671 key: train_jcc value: [0.61702128 0.61780105 0.6 0.6344086 0.62631579 0.62903226 0.60540541 0.625 0.62162162 0.62903226] mean value: 0.6205638258496445 MCC on Blind test: 0.07 MCC on Training: 0.18 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02637291 0.08454609 0.07177496 0.07822537 0.06755686 0.0491848 0.07944179 0.09766483 0.05391884 0.03047943] mean value: 0.06391658782958984 key: score_time value: [0.012537 0.02454877 0.02084088 0.02626276 0.01799107 0.024544 0.02388382 0.03588986 0.0132556 0.01201701] mean value: 0.021177077293395997 key: test_mcc value: [0.35355339 0.49236596 0.54470478 0.54470478 0.3985267 0.27139234 0.57564968 0.3985267 0.27205882 0.45588235] mean value: 0.4307365504981025 key: train_mcc value: [0.7867366 0.80668459 0.76006756 0.80007112 0.78086913 0.82751775 0.80732892 0.74758268 0.79403974 0.78745199] mean value: 0.7898350074928323 key: test_fscore value: [0.66666667 0.68965517 0.73333333 0.73333333 0.70588235 0.6 0.77419355 0.6875 0.64705882 0.72727273] mean value: 0.6964895957877539 key: train_fscore value: [0.89403974 0.90365449 0.8807947 0.89932886 0.89180328 0.91503268 0.90365449 0.87417219 0.89700997 0.89403974] mean value: 0.8953530111980459 key: test_precision value: [0.6875 0.83333333 0.84615385 0.84615385 0.66666667 0.64285714 0.8 0.73333333 0.64705882 0.75 ] mean value: 0.745305699202758 key: train_precision value: [0.88815789 0.90066225 0.875 0.90540541 0.88311688 0.90322581 0.90666667 0.86842105 0.89403974 0.88815789] mean value: 0.8912853590500799 key: test_recall value: [0.64705882 0.58823529 0.64705882 0.64705882 0.75 0.5625 0.75 0.64705882 0.64705882 0.70588235] mean value: 0.6591911764705882 key: train_recall value: [0.9 0.90666667 0.88666667 0.89333333 0.90066225 0.92715232 0.90066225 0.88 0.9 0.9 ] mean value: 0.8995143487858721 key: test_accuracy value: [0.67647059 0.73529412 0.76470588 0.76470588 0.6969697 0.63636364 0.78787879 0.6969697 0.63636364 0.72727273] mean value: 0.7122994652406418 key: train_accuracy value: [0.89333333 0.90333333 0.88 0.9 0.89036545 0.91362126 0.90365449 0.87375415 0.89700997 0.89368771] mean value: 0.8948759689922481 key: test_roc_auc value: [0.67647059 0.73529412 0.76470588 0.76470588 0.69852941 0.63419118 0.78676471 0.69852941 0.63602941 0.72794118] mean value: 0.7123161764705882 key: train_roc_auc value: [0.89333333 0.90333333 0.88 0.9 0.89033113 0.91357616 0.90366446 0.87377483 0.89701987 0.89370861] mean value: 0.8948741721854303 key: test_jcc value: [0.5 0.52631579 0.57894737 0.57894737 0.54545455 0.42857143 0.63157895 0.52380952 0.47826087 0.57142857] mean value: 0.5363314412513497 key: train_jcc value: [0.80838323 0.82424242 0.78698225 0.81707317 0.80473373 0.84337349 0.82424242 0.77647059 0.81325301 0.80838323] mean value: 0.8107137556873176 MCC on Blind test: 0.18 MCC on Training: 0.43 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04134369 0.05668759 0.07766294 0.03767109 0.03909254 0.04192877 0.03879094 0.03609324 0.0364244 0.0477488 ] mean value: 0.04534440040588379 key: score_time value: [0.01259995 0.01405811 0.01560521 0.01252437 0.01261425 0.01252818 0.01251197 0.01270938 0.01367402 0.0127902 ] mean value: 0.013161563873291015 key: test_mcc value: [0.53311399 0.53311399 0.47809144 0.47140452 0.69852941 0.21323529 0.33210558 0.34202871 0.45387763 0.34299717] mean value: 0.4398497737328967 key: train_mcc value: [0.64 0.62067622 0.58687537 0.58001289 0.62135667 0.6345534 0.60799117 0.6345534 0.60834812 0.61461369] mean value: 0.6148980925733345 key: test_fscore value: [0.77777778 0.75 0.75675676 0.74285714 0.84848485 0.60606061 0.64516129 0.64516129 0.74285714 0.71794872] mean value: 0.7233065573388154 key: train_fscore value: [0.82 0.81433225 0.79605263 0.79069767 0.81311475 0.81848185 0.80398671 0.81605351 0.80655738 0.80666667] mean value: 0.8085943422222721 key: test_precision value: [0.73684211 0.8 0.7 0.72222222 0.82352941 0.58823529 0.66666667 0.71428571 0.72222222 0.63636364] mean value: 0.7110367272905973 key: train_precision value: [0.82 0.79617834 0.78571429 0.78807947 0.80519481 0.81578947 0.80666667 0.81879195 0.79354839 0.80666667] mean value: 0.8036630045479853 key: test_recall value: [0.82352941 0.70588235 0.82352941 0.76470588 0.875 0.625 0.625 0.58823529 0.76470588 0.82352941] mean value: 0.7419117647058823 key: train_recall value: [0.82 0.83333333 0.80666667 0.79333333 0.82119205 0.82119205 0.8013245 0.81333333 0.82 0.80666667] mean value: 0.8137041942604857 key: test_accuracy value: [0.76470588 0.76470588 0.73529412 0.73529412 0.84848485 0.60606061 0.66666667 0.66666667 0.72727273 0.66666667] mean value: 0.7181818181818183 key: train_accuracy value: [0.82 0.81 0.79333333 0.79 0.81063123 0.81727575 0.80398671 0.81727575 0.80398671 0.80730897] mean value: 0.8073798449612403 key: test_roc_auc value: [0.76470588 0.76470588 0.73529412 0.73529412 0.84926471 0.60661765 0.66544118 0.66911765 0.72610294 0.66176471] mean value: 0.7178308823529411 key: train_roc_auc value: [0.82 0.81 0.79333333 0.79 0.81059603 0.81726269 0.80399558 0.81726269 0.80403974 0.80730684] mean value: 0.8073796909492273 key: test_jcc value: [0.63636364 0.6 0.60869565 0.59090909 0.73684211 0.43478261 0.47619048 0.47619048 0.59090909 0.56 ] mean value: 0.5710883136695493 key: train_jcc value: [0.69491525 0.68681319 0.66120219 0.65384615 0.68508287 0.69273743 0.67222222 0.68926554 0.67582418 0.67597765] mean value: 0.67878866721856 MCC on Blind test: 0.37 MCC on Training: 0.44 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48790836 0.60249805 0.50840831 0.5167141 0.51071095 0.6102984 0.5012753 0.50499964 0.49826097 0.54318881] mean value: 0.5284262895584106 key: score_time value: [0.01447845 0.01444507 0.01212835 0.01486588 0.01456642 0.01481748 0.01486564 0.01202536 0.01206684 0.01214671] mean value: 0.013640618324279786 key: test_mcc value: [0.53311399 0.42365927 0.53311399 0.47140452 0.58739713 0.21323529 0.33210558 0.40987872 0.45876334 0.45876334] mean value: 0.4421435172865914 key: train_mcc value: [0.68680404 0.72006401 0.58011603 0.68006045 0.80777393 0.68786153 0.66127965 0.58175801 0.55548214 0.50172233] mean value: 0.6462922124271582 key: test_fscore value: [0.77777778 0.66666667 0.77777778 0.72727273 0.8 0.60606061 0.64516129 0.66666667 0.75675676 0.75675676] mean value: 0.7180897026058317 key: train_fscore value: [0.84175084 0.8590604 0.79207921 0.83892617 0.9023569 0.84280936 0.82943144 0.79344262 0.78175896 0.74747475] mean value: 0.8229090659965619 key: test_precision value: [0.73684211 0.76923077 0.73684211 0.75 0.73684211 0.58823529 0.66666667 0.76923077 0.7 0.7 ] mean value: 0.7153889815035326 key: train_precision value: [0.85034014 0.86486486 0.78431373 0.84459459 0.91780822 0.85135135 0.83783784 0.78064516 0.76433121 0.75510204] mean value: 0.825118914166908 key: test_recall value: [0.82352941 0.58823529 0.82352941 0.70588235 0.875 0.625 0.625 0.58823529 0.82352941 0.82352941] mean value: 0.7301470588235294 key: train_recall value: [0.83333333 0.85333333 0.8 0.83333333 0.88741722 0.83443709 0.82119205 0.80666667 0.8 0.74 ] mean value: 0.8209713024282561 key: test_accuracy value: [0.76470588 0.70588235 0.76470588 0.73529412 0.78787879 0.60606061 0.66666667 0.6969697 0.72727273 0.72727273] mean value: 0.7182709447415331 key: train_accuracy value: [0.84333333 0.86 0.79 0.84 0.90365449 0.84385382 0.83056478 0.79069767 0.77740864 0.75083056] mean value: 0.8230343300110743 key: test_roc_auc value: [0.76470588 0.70588235 0.76470588 0.73529412 0.79044118 0.60661765 0.66544118 0.70036765 0.72426471 0.72426471] mean value: 0.7181985294117648 key: train_roc_auc value: [0.84333333 0.86 0.79 0.84 0.90370861 0.84388521 0.83059603 0.79075055 0.77748344 0.7507947 ] mean value: 0.8230551876379693 key: test_jcc value: [0.63636364 0.5 0.63636364 0.57142857 0.66666667 0.43478261 0.47619048 0.5 0.60869565 0.60869565] mean value: 0.5639186900056465 key: train_jcc value: [0.72674419 0.75294118 0.6557377 0.72254335 0.82208589 0.7283237 0.70857143 0.6576087 0.64171123 0.59677419] mean value: 0.7013041556747319 MCC on Blind test: 0.42 MCC on Training: 0.44 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.46593904 1.33741689 1.53873634 1.43221641 1.34052849 1.47996783 1.29264426 1.37763429 1.41544867 1.29166603] mean value: 1.3972198247909546 key: score_time value: [0.01716399 0.01525116 0.01527667 0.01543617 0.01540279 0.01586342 0.0150516 0.01416755 0.01503539 0.01538038] mean value: 0.015402913093566895 key: test_mcc value: [0.47809144 0.29617444 0.64705882 0.5976143 0.69852941 0.21323529 0.52710164 0.65806217 0.27675465 0.21323529] mean value: 0.46058574644532424 key: train_mcc value: [0.92131124 0.92767735 0.92718191 0.91384116 0.92158147 0.90827583 0.91462322 0.92058313 0.94717324 0.9274135 ] mean value: 0.922966205675867 key: test_fscore value: [0.70967742 0.625 0.82352941 0.77419355 0.84848485 0.60606061 0.71428571 0.8 0.625 0.60606061] mean value: 0.7132292154398416 key: train_fscore value: [0.95890411 0.96245734 0.96271186 0.9559322 0.95918367 0.95238095 0.9559322 0.95945946 0.97297297 0.96271186] mean value: 0.9602646641348993 key: test_precision value: [0.78571429 0.66666667 0.82352941 0.85714286 0.82352941 0.58823529 0.83333333 0.92307692 0.66666667 0.625 ] mean value: 0.7592894850247791 key: train_precision value: [0.98591549 0.98601399 0.97931034 0.97241379 0.98601399 0.97902098 0.97916667 0.97260274 0.98630137 0.97931034] mean value: 0.9806069703021028 key: test_recall value: [0.64705882 0.58823529 0.82352941 0.70588235 0.875 0.625 0.625 0.70588235 0.58823529 0.58823529] mean value: 0.6772058823529411 key: train_recall value: [0.93333333 0.94 0.94666667 0.94 0.93377483 0.92715232 0.93377483 0.94666667 0.96 0.94666667] mean value: 0.94080353200883 key: test_accuracy value: [0.73529412 0.64705882 0.82352941 0.79411765 0.84848485 0.60606061 0.75757576 0.81818182 0.63636364 0.60606061] mean value: 0.7272727272727273 key: train_accuracy value: [0.96 0.96333333 0.96333333 0.95666667 0.96013289 0.95348837 0.95681063 0.96013289 0.97342193 0.96345515] mean value: 0.961077519379845 key: test_roc_auc value: [0.73529412 0.64705882 0.82352941 0.79411765 0.84926471 0.60661765 0.75367647 0.82169118 0.63786765 0.60661765] mean value: 0.7275735294117647 key: train_roc_auc value: [0.96 0.96333333 0.96333333 0.95666667 0.96022075 0.95357616 0.95688742 0.9600883 0.97337748 0.96339956] mean value: 0.9610883002207504 key: test_jcc value: [0.55 0.45454545 0.7 0.63157895 0.73684211 0.43478261 0.55555556 0.66666667 0.45454545 0.43478261] mean value: 0.5619299401336014 key: train_jcc value: [0.92105263 0.92763158 0.92810458 0.91558442 0.92156863 0.90909091 0.91558442 0.92207792 0.94736842 0.92810458] mean value: 0.9236168071694388 MCC on Blind test: 0.32 MCC on Training: 0.46 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01280999 0.01319289 0.01262236 0.00894904 0.0089643 0.00920987 0.00958872 0.00904918 0.00955033 0.00937772] mean value: 0.010331439971923827 key: score_time value: [0.01194692 0.00942445 0.00870895 0.00866365 0.00845742 0.00865769 0.00861883 0.00866294 0.00854135 0.00858974] mean value: 0.009027194976806641 key: test_mcc value: [ 0. 0.47140452 0.25819889 0.31434731 0.40987872 -0.02941176 0.03321056 0.08856149 0.53397044 0.28386378] mean value: 0.23640239413760028 key: train_mcc value: [0.28203804 0.28365431 0.24262374 0.26168604 0.25085492 0.29270739 0.28510101 0.2589459 0.26870902 0.27826776] mean value: 0.2704588140266934 key: test_fscore value: [0.54054054 0.72727273 0.68292683 0.7 0.72222222 0.48484848 0.52941176 0.57142857 0.73333333 0.7 ] mean value: 0.6391984473620055 key: train_fscore value: [0.66037736 0.66666667 0.64596273 0.64984227 0.64797508 0.67278287 0.66666667 0.65217391 0.66465257 0.65830721] mean value: 0.6585407339586543 key: test_precision value: [0.5 0.75 0.58333333 0.60869565 0.65 0.47058824 0.5 0.55555556 0.84615385 0.60869565] mean value: 0.6073022274684678 key: train_precision value: [0.625 0.62068966 0.60465116 0.61676647 0.61176471 0.625 0.62427746 0.61046512 0.60773481 0.62130178] mean value: 0.6167651145615564 key: test_recall value: [0.58823529 0.70588235 0.82352941 0.82352941 0.8125 0.5 0.5625 0.58823529 0.64705882 0.82352941] mean value: 0.6875 key: train_recall value: [0.7 0.72 0.69333333 0.68666667 0.68874172 0.72847682 0.71523179 0.7 0.73333333 0.7 ] mean value: 0.7065783664459161 key: test_accuracy value: [0.5 0.73529412 0.61764706 0.64705882 0.6969697 0.48484848 0.51515152 0.54545455 0.75757576 0.63636364] mean value: 0.6136363636363636 key: train_accuracy value: [0.64 0.64 0.62 0.63 0.62458472 0.64451827 0.64119601 0.62790698 0.63122924 0.63787375] mean value: 0.6337308970099669 key: test_roc_auc value: [0.5 0.73529412 0.61764706 0.64705882 0.70036765 0.48529412 0.51654412 0.54411765 0.76102941 0.63051471] mean value: 0.6137867647058823 key: train_roc_auc value: [0.64 0.64 0.62 0.63 0.62437086 0.64423841 0.64094923 0.6281457 0.63156733 0.63807947] mean value: 0.6337350993377484 key: test_jcc value: [0.37037037 0.57142857 0.51851852 0.53846154 0.56521739 0.32 0.36 0.4 0.57894737 0.53846154] mean value: 0.4761405296965938 key: train_jcc value: [0.49295775 0.5 0.47706422 0.48130841 0.47926267 0.50691244 0.5 0.48387097 0.49773756 0.49065421] mean value: 0.49097682229951845 MCC on Blind test: 0.3 MCC on Training: 0.24 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00933456 0.01051569 0.01062942 0.01083946 0.01089215 0.01075816 0.009233 0.01049662 0.00960302 0.0097599 ] mean value: 0.010206198692321778 key: score_time value: [0.00901294 0.00936413 0.0094254 0.00948095 0.00958276 0.00958562 0.00902581 0.0102644 0.00885487 0.00859237] mean value: 0.009318923950195313 key: test_mcc value: [0.41464421 0.44008623 0.65158377 0.23570226 0.33210558 0.21057989 0.33467162 0.11194628 0.48126671 0.15863619] mean value: 0.3371222741807206 key: train_mcc value: [0.43322429 0.40942518 0.4093038 0.45201192 0.44787532 0.46210252 0.42485451 0.46178195 0.4511817 0.5063831 ] mean value: 0.44581443027223483 key: test_fscore value: [0.6875 0.64285714 0.8125 0.60606061 0.64516129 0.55172414 0.62068966 0.44444444 0.68965517 0.66666667] mean value: 0.6367259115868683 key: train_fscore value: [0.68613139 0.6641791 0.68551237 0.70036101 0.69784173 0.72727273 0.69473684 0.70072993 0.70671378 0.7311828 ] mean value: 0.6994661669282064 key: test_precision value: [0.73333333 0.81818182 0.86666667 0.625 0.66666667 0.61538462 0.69230769 0.6 0.83333333 0.56 ] mean value: 0.7010874125874125 key: train_precision value: [0.75806452 0.75423729 0.72932331 0.76377953 0.76377953 0.73972603 0.73880597 0.77419355 0.7518797 0.79069767] mean value: 0.7564487087253748 key: test_recall value: [0.64705882 0.52941176 0.76470588 0.58823529 0.625 0.5 0.5625 0.35294118 0.58823529 0.82352941] mean value: 0.5981617647058823 key: train_recall value: [0.62666667 0.59333333 0.64666667 0.64666667 0.64238411 0.71523179 0.65562914 0.64 0.66666667 0.68 ] mean value: 0.6513245033112584 key: test_accuracy value: [0.70588235 0.70588235 0.82352941 0.61764706 0.66666667 0.60606061 0.66666667 0.54545455 0.72727273 0.57575758] mean value: 0.6640819964349377 key: train_accuracy value: [0.71333333 0.7 0.70333333 0.72333333 0.72093023 0.73089701 0.71096346 0.72757475 0.72425249 0.75083056] mean value: 0.7205448504983388 key: test_roc_auc value: [0.70588235 0.70588235 0.82352941 0.61764706 0.66544118 0.60294118 0.66360294 0.55147059 0.73161765 0.56801471] mean value: 0.6636029411764706 key: train_roc_auc value: [0.71333333 0.7 0.70333333 0.72333333 0.72119205 0.73094923 0.7111479 0.72728477 0.72406181 0.75059603] mean value: 0.7205231788079469 key: test_jcc value: [0.52380952 0.47368421 0.68421053 0.43478261 0.47619048 0.38095238 0.45 0.28571429 0.52631579 0.5 ] mean value: 0.4735659801678109 key: train_jcc value: [0.52222222 0.4972067 0.52150538 0.53888889 0.5359116 0.57142857 0.53225806 0.53932584 0.54644809 0.57627119] mean value: 0.5381466546089457 MCC on Blind test: 0.24 MCC on Training: 0.34 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0146966 0.01543975 0.01618838 0.01500964 0.01524711 0.01573944 0.01867366 0.01522231 0.01610875 0.01799488] mean value: 0.0160320520401001 key: score_time value: [0.00915027 0.01195478 0.011832 0.01201868 0.01179123 0.01187181 0.01192832 0.011729 0.01183558 0.01177907] mean value: 0.01158907413482666 key: test_mcc value: [0.33333333 0.31108551 0.36514837 0.33333333 0.45794324 0.15192709 0.30012252 0.30012252 0.3985267 0.21437323] mean value: 0.31659158557917866 key: train_mcc value: [0.53116225 0.45116865 0.35166545 0.45579765 0.44066864 0.51363356 0.51013965 0.53802798 0.58925704 0.59482493] mean value: 0.49763458015730827 key: test_fscore value: [0.53846154 0.3 0.72340426 0.53846154 0.75 0.46153846 0.68421053 0.57142857 0.6875 0.66666667] mean value: 0.5921671558191715 key: train_fscore value: [0.64 0.51960784 0.72319202 0.53846154 0.75338753 0.63755459 0.77900552 0.73800738 0.73092369 0.79867987] mean value: 0.6858819988278689 key: test_precision value: [0.77777778 1. 0.56666667 0.77777778 0.625 0.6 0.59090909 0.72727273 0.73333333 0.59090909] mean value: 0.6989646464646465 key: train_precision value: [0.96 0.98148148 0.57768924 0.96551724 0.63761468 0.93589744 0.66824645 0.82644628 0.91919192 0.79084967] mean value: 0.82629343995691 key: test_recall value: [0.41176471 0.17647059 1. 0.41176471 0.9375 0.375 0.8125 0.47058824 0.64705882 0.76470588] mean value: 0.6007352941176471 key: train_recall value: [0.48 0.35333333 0.96666667 0.37333333 0.9205298 0.48344371 0.93377483 0.66666667 0.60666667 0.80666667] mean value: 0.6591081677704194 key: test_accuracy value: [0.64705882 0.58823529 0.61764706 0.64705882 0.6969697 0.57575758 0.63636364 0.63636364 0.6969697 0.60606061] mean value: 0.634848484848485 key: train_accuracy value: [0.73 0.67333333 0.63 0.68 0.69767442 0.72425249 0.73421927 0.7641196 0.77740864 0.79734219] mean value: 0.7208349944629014 key: test_roc_auc value: [0.64705882 0.58823529 0.61764706 0.64705882 0.70404412 0.56985294 0.64154412 0.64154412 0.69852941 0.60110294] mean value: 0.6356617647058822 key: train_roc_auc value: [0.73 0.67333333 0.63 0.68 0.69693157 0.72505519 0.73355408 0.76379691 0.77684327 0.79737307] mean value: 0.7206887417218544 key: test_jcc value: [0.36842105 0.17647059 0.56666667 0.36842105 0.6 0.3 0.52 0.4 0.52380952 0.5 ] mean value: 0.43237888839746424 key: train_jcc value: [0.47058824 0.35099338 0.56640625 0.36842105 0.60434783 0.46794872 0.63800905 0.58479532 0.57594937 0.66483516] mean value: 0.529229436277977 MCC on Blind test: 0.34 MCC on Training: 0.32 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02681756 0.03118038 0.03216791 0.03158522 0.03118181 0.03206205 0.03131533 0.02926445 0.0270853 0.02832866] mean value: 0.030098867416381837 key: score_time value: [0.01660419 0.01648521 0.01645088 0.01651096 0.01609421 0.01594472 0.01581836 0.01450419 0.01417589 0.01430058] mean value: 0.01568892002105713 key: test_mcc value: [0.58925565 0.33333333 0.58925565 0.5976143 0.51470588 0.33455882 0.57720588 0.63944497 0.21057989 0.2227832 ] mean value: 0.4608737590168127 key: train_mcc value: [1. 1. 0.99335541 0.98019606 0.95452779 0.99337748 0.98026229 1. 0.95451404 0.99337719] mean value: 0.984961026159765 key: test_fscore value: [0.78787879 0.71428571 0.8 0.81081081 0.75 0.66666667 0.78787879 0.83333333 0.64864865 0.68292683] mean value: 0.7482429578771042 key: train_fscore value: [1. 1. 0.99665552 0.99009901 0.97627119 0.99667774 0.98996656 1. 0.97610922 0.99665552] mean value: 0.9922434744195764 key: test_precision value: [0.8125 0.6 0.77777778 0.75 0.75 0.64705882 0.76470588 0.78947368 0.6 0.58333333] mean value: 0.707484950120399 key: train_precision value: [1. 1. 1. 0.98039216 1. 1. 1. 1. 1. 1. ] mean value: 0.9980392156862745 key: test_recall value: [0.76470588 0.88235294 0.82352941 0.88235294 0.75 0.6875 0.8125 0.88235294 0.70588235 0.82352941] mean value: 0.801470588235294 key: train_recall value: [1. 1. 0.99333333 1. 0.95364238 0.99337748 0.98013245 1. 0.95333333 0.99333333] mean value: 0.9867152317880796 key: test_accuracy value: [0.79411765 0.64705882 0.79411765 0.79411765 0.75757576 0.66666667 0.78787879 0.81818182 0.60606061 0.60606061] mean value: 0.7271836007130126 key: train_accuracy value: [1. 1. 0.99666667 0.99 0.97674419 0.99667774 0.99003322 1. 0.97674419 0.99667774] mean value: 0.9923543743078629 key: test_roc_auc value: [0.79411765 0.64705882 0.79411765 0.79411765 0.75735294 0.66727941 0.78860294 0.81617647 0.60294118 0.59926471] mean value: 0.7261029411764707 key: train_roc_auc value: [1. 1. 0.99666667 0.99 0.97682119 0.99668874 0.99006623 1. 0.97666667 0.99666667] mean value: 0.9923576158940397 key: test_jcc value: [0.65 0.55555556 0.66666667 0.68181818 0.6 0.5 0.65 0.71428571 0.48 0.51851852] mean value: 0.6016844636844637 key: train_jcc value: [1. 1. 0.99333333 0.98039216 0.95364238 0.99337748 0.98013245 1. 0.95333333 0.99333333] mean value: 0.9847544474743541 MCC on Blind test: 0.06 MCC on Training: 0.46 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.61504078 0.61853433 0.59623408 0.69772983 0.60142732 0.65093637 0.66764665 0.65282845 0.62927532 0.62676024] mean value: 0.63564133644104 key: score_time value: [0.17362475 0.15516782 0.16305757 0.14553714 0.16372967 0.17645073 0.15277743 0.15325856 0.18996716 0.16576838] mean value: 0.16393392086029052 key: test_mcc value: [0.70710678 0.64705882 0.82495791 0.70710678 0.81985294 0.51470588 0.69742172 0.87867647 0.33455882 0.51470588] mean value: 0.6646152019649637 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84848485 0.82352941 0.90909091 0.84848485 0.90909091 0.75 0.83870968 0.94117647 0.66666667 0.76470588] mean value: 0.829993962394342 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.82352941 0.9375 0.875 0.88235294 0.75 0.86666667 0.94117647 0.6875 0.76470588] mean value: 0.8403431372549018 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82352941 0.82352941 0.88235294 0.82352941 0.9375 0.75 0.8125 0.94117647 0.64705882 0.76470588] mean value: 0.8205882352941177 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85294118 0.82352941 0.91176471 0.85294118 0.90909091 0.75757576 0.84848485 0.93939394 0.66666667 0.75757576] mean value: 0.8319964349376114 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85294118 0.82352941 0.91176471 0.85294118 0.90992647 0.75735294 0.84742647 0.93933824 0.66727941 0.75735294] mean value: 0.8319852941176469 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73684211 0.7 0.83333333 0.73684211 0.83333333 0.6 0.72222222 0.88888889 0.5 0.61904762] mean value: 0.7170509607351713 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.35 MCC on Training: 0.66 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.01190782 0.90555048 0.94626713 1.01974034 0.96415997 0.9244523 0.95228004 0.94059896 0.9872272 0.90653896] mean value: 0.9558723211288452 key: score_time value: [0.2072084 0.24053216 0.20800662 0.20055079 0.21458507 0.23202157 0.22788239 0.24233437 0.20827079 0.19935608] mean value: 0.21807482242584228 key: test_mcc value: [0.47140452 0.64705882 0.77005354 0.58925565 0.64207079 0.45588235 0.57720588 0.76212918 0.33455882 0.63602941] mean value: 0.5885648980299655 key: train_mcc value: [0.86001911 0.90018005 0.88684405 0.88684405 0.88706402 0.9003532 0.87376926 0.87376926 0.89368653 0.88039735] mean value: 0.8842926900329828 key: test_fscore value: [0.74285714 0.82352941 0.875 0.78787879 0.82352941 0.72727273 0.78787879 0.88888889 0.66666667 0.82352941] mean value: 0.794703123673712 key: train_fscore value: [0.93023256 0.94949495 0.94276094 0.94276094 0.94352159 0.95016611 0.93729373 0.93645485 0.94666667 0.94 ] mean value: 0.9419352346335497 key: test_precision value: [0.72222222 0.82352941 0.93333333 0.8125 0.77777778 0.70588235 0.76470588 0.84210526 0.6875 0.82352941] mean value: 0.7893085655314758 key: train_precision value: [0.92715232 0.95918367 0.95238095 0.95238095 0.94666667 0.95333333 0.93421053 0.93959732 0.94666667 0.94 ] mean value: 0.9451572404530785 key: test_recall value: [0.76470588 0.82352941 0.82352941 0.76470588 0.875 0.75 0.8125 0.94117647 0.64705882 0.82352941] mean value: 0.8025735294117646 key: train_recall value: [0.93333333 0.94 0.93333333 0.93333333 0.94039735 0.94701987 0.94039735 0.93333333 0.94666667 0.94 ] mean value: 0.9387814569536423 key: test_accuracy value: [0.73529412 0.82352941 0.88235294 0.79411765 0.81818182 0.72727273 0.78787879 0.87878788 0.66666667 0.81818182] mean value: 0.7932263814616756 key: train_accuracy value: [0.93 0.95 0.94333333 0.94333333 0.94352159 0.95016611 0.93687708 0.93687708 0.94684385 0.94019934] mean value: 0.9421151716500553 key: test_roc_auc value: [0.73529412 0.82352941 0.88235294 0.79411765 0.81985294 0.72794118 0.78860294 0.87683824 0.66727941 0.81801471] mean value: 0.7933823529411763 key: train_roc_auc value: [0.93 0.95 0.94333333 0.94333333 0.94353201 0.9501766 0.93686534 0.93686534 0.94684327 0.94019868] mean value: 0.9421147902869758 key: test_jcc value: [0.59090909 0.7 0.77777778 0.65 0.7 0.57142857 0.65 0.8 0.5 0.7 ] mean value: 0.664011544011544 key: train_jcc value: [0.86956522 0.90384615 0.89171975 0.89171975 0.89308176 0.90506329 0.88198758 0.88050314 0.89873418 0.88679245] mean value: 0.8903013266168067 MCC on Blind test: 0.44 MCC on Training: 0.59 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03423572 0.03603745 0.0354507 0.0354538 0.03214025 0.03596711 0.0345521 0.01433039 0.0366118 0.03674293] mean value: 0.033152222633361816 key: score_time value: [0.02113938 0.02398849 0.02362132 0.02285361 0.02086568 0.02385545 0.01198983 0.0119741 0.02149701 0.02265167] mean value: 0.020443654060363768 key: test_mcc value: [0.41464421 0.35856858 0.53311399 0.47140452 0.63602941 0.21323529 0.33210558 0.45588235 0.33210558 0.27500955] mean value: 0.4022099080092961 key: train_mcc value: [0.70014004 0.69339497 0.66013204 0.68006045 0.66781473 0.68113383 0.67482339 0.6611479 0.68804963 0.67452749] mean value: 0.6781224472876617 key: test_fscore value: [0.72222222 0.64516129 0.77777778 0.72727273 0.8125 0.60606061 0.64516129 0.72727273 0.68571429 0.68421053] mean value: 0.7033353453281297 key: train_fscore value: [0.85148515 0.84768212 0.83168317 0.83892617 0.83552632 0.84 0.83501684 0.83056478 0.84067797 0.83501684] mean value: 0.838657934651162 key: test_precision value: [0.68421053 0.71428571 0.73684211 0.75 0.8125 0.58823529 0.66666667 0.75 0.66666667 0.61904762] mean value: 0.6988454592363261 key: train_precision value: [0.84313725 0.84210526 0.82352941 0.84459459 0.83006536 0.84563758 0.84931507 0.82781457 0.85517241 0.84353741] mean value: 0.8404908934577563 key: test_recall value: [0.76470588 0.58823529 0.82352941 0.70588235 0.8125 0.625 0.625 0.70588235 0.70588235 0.76470588] mean value: 0.7121323529411765 key: train_recall value: [0.86 0.85333333 0.84 0.83333333 0.8410596 0.83443709 0.82119205 0.83333333 0.82666667 0.82666667] mean value: 0.8370022075055188 key: test_accuracy value: [0.70588235 0.67647059 0.76470588 0.73529412 0.81818182 0.60606061 0.66666667 0.72727273 0.66666667 0.63636364] mean value: 0.7003565062388593 key: train_accuracy value: [0.85 0.84666667 0.83 0.84 0.83388704 0.84053156 0.8372093 0.83056478 0.84385382 0.8372093 ] mean value: 0.8389922480620156 key: test_roc_auc value: [0.70588235 0.67647059 0.76470588 0.73529412 0.81801471 0.60661765 0.66544118 0.72794118 0.66544118 0.63235294] mean value: 0.6998161764705884 key: train_roc_auc value: [0.85 0.84666667 0.83 0.84 0.83386313 0.84055188 0.83726269 0.83057395 0.84379691 0.83717439] mean value: 0.8389889624724061 key: test_jcc value: [0.56521739 0.47619048 0.63636364 0.57142857 0.68421053 0.43478261 0.47619048 0.57142857 0.52173913 0.52 ] mean value: 0.5457551388352304 key: train_jcc value: [0.74137931 0.73563218 0.71186441 0.72254335 0.71751412 0.72413793 0.71676301 0.71022727 0.7251462 0.71676301] mean value: 0.7221970792080336 MCC on Blind test: 0.3 MCC on Training: 0.4 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.0956049 0.09505439 0.08825564 0.12499881 0.09847784 0.14720416 0.10817647 0.13678169 0.12687469 0.12597752] mean value: 0.11474061012268066 key: score_time value: [0.01592541 0.01224995 0.02110982 0.02191877 0.04300451 0.02512884 0.03297758 0.02323866 0.01530766 0.02121687] mean value: 0.023207807540893556 key: test_mcc value: [0.41464421 0.53311399 0.53311399 0.47140452 0.69852941 0.15073529 0.33210558 0.40987872 0.51676076 0.27500955] mean value: 0.4335296034823335 key: train_mcc value: [0.70014004 0.60344274 0.58687537 0.59345204 0.57548494 0.59496368 0.60806397 0.56824304 0.58894042 0.67452749] mean value: 0.6094133733203262 key: test_fscore value: [0.72222222 0.75 0.77777778 0.74285714 0.84848485 0.5625 0.64516129 0.66666667 0.77777778 0.68421053] mean value: 0.7177658252424807 key: train_fscore value: [0.85148515 0.81012658 0.79605263 0.79867987 0.79354839 0.80130293 0.80655738 0.78547855 0.7987013 0.83501684] mean value: 0.8076949607674043 key: test_precision value: [0.68421053 0.8 0.73684211 0.72222222 0.82352941 0.5625 0.66666667 0.76923077 0.73684211 0.61904762] mean value: 0.7121091425774088 key: train_precision value: [0.84313725 0.77108434 0.78571429 0.79084967 0.77358491 0.78846154 0.7987013 0.77777778 0.77848101 0.84353741] mean value: 0.7951329499393465 key: test_recall value: [0.76470588 0.70588235 0.82352941 0.76470588 0.875 0.5625 0.625 0.58823529 0.82352941 0.76470588] mean value: 0.7297794117647058 key: train_recall value: [0.86 0.85333333 0.80666667 0.80666667 0.81456954 0.81456954 0.81456954 0.79333333 0.82 0.82666667] mean value: 0.8210375275938191 key: test_accuracy value: [0.70588235 0.76470588 0.76470588 0.73529412 0.84848485 0.57575758 0.66666667 0.6969697 0.75757576 0.63636364] mean value: 0.71524064171123 key: train_accuracy value: [0.85 0.8 0.79333333 0.79666667 0.78737542 0.79734219 0.80398671 0.78405316 0.79401993 0.8372093 ] mean value: 0.8043986710963456 key: test_roc_auc value: [0.70588235 0.76470588 0.76470588 0.73529412 0.84926471 0.57536765 0.66544118 0.70036765 0.75551471 0.63235294] mean value: 0.7148897058823529 key: train_roc_auc value: [0.85 0.8 0.79333333 0.79666667 0.78728477 0.79728477 0.80395143 0.78408389 0.79410596 0.83717439] mean value: 0.8043885209713023 key: test_jcc value: [0.56521739 0.6 0.63636364 0.59090909 0.73684211 0.39130435 0.47619048 0.5 0.63636364 0.52 ] mean value: 0.5653190684220432 key: train_jcc value: [0.74137931 0.68085106 0.66120219 0.66483516 0.65775401 0.66847826 0.67582418 0.64673913 0.66486486 0.71676301] mean value: 0.6778691173271052 MCC on Blind test: 0.4 MCC on Training: 0.43 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01765275 0.01376319 0.01363349 0.01396585 0.01427412 0.01375127 0.01380944 0.01385093 0.01407003 0.01386976] mean value: 0.014264082908630371 key: score_time value: [0.01083088 0.0101335 0.01009011 0.01017284 0.01032043 0.01010156 0.01030517 0.01032901 0.01051497 0.01028752] mean value: 0.010308599472045899 key: test_mcc value: [0.41464421 0.5976143 0.42365927 0.52941176 0.76384284 0.21323529 0.51470588 0.51470588 0.33455882 0.27500955] mean value: 0.45813878269123426 key: train_mcc value: [0.6733483 0.67406772 0.62941411 0.65426451 0.65495631 0.69462112 0.61461369 0.64784317 0.65455429 0.66803571] mean value: 0.6565718910526267 key: test_fscore value: [0.72222222 0.77419355 0.73684211 0.76470588 0.88235294 0.60606061 0.75 0.76470588 0.66666667 0.68421053] mean value: 0.7351960380797892 key: train_fscore value: [0.8361204 0.84039088 0.82165605 0.83116883 0.83116883 0.84563758 0.80794702 0.82274247 0.82781457 0.83552632] mean value: 0.830017295811215 key: test_precision value: [0.68421053 0.85714286 0.66666667 0.76470588 0.83333333 0.58823529 0.75 0.76470588 0.6875 0.61904762] mean value: 0.7215548061329795 key: train_precision value: [0.83892617 0.82165605 0.78658537 0.81012658 0.81528662 0.85714286 0.80794702 0.82550336 0.82236842 0.82467532] mean value: 0.8210217776231079 key: test_recall value: [0.76470588 0.70588235 0.82352941 0.76470588 0.9375 0.625 0.75 0.76470588 0.64705882 0.76470588] mean value: 0.7547794117647059 key: train_recall value: [0.83333333 0.86 0.86 0.85333333 0.84768212 0.83443709 0.80794702 0.82 0.83333333 0.84666667] mean value: 0.8396732891832229 key: test_accuracy value: [0.70588235 0.79411765 0.70588235 0.76470588 0.87878788 0.60606061 0.75757576 0.75757576 0.66666667 0.63636364] mean value: 0.7273618538324421 key: train_accuracy value: [0.83666667 0.83666667 0.81333333 0.82666667 0.82724252 0.84717608 0.80730897 0.82392027 0.82724252 0.83388704] mean value: 0.8280110741971207 key: test_roc_auc value: [0.70588235 0.79411765 0.70588235 0.76470588 0.88051471 0.60661765 0.75735294 0.75735294 0.66727941 0.63235294] mean value: 0.7272058823529411 key: train_roc_auc value: [0.83666667 0.83666667 0.81333333 0.82666667 0.82717439 0.84721854 0.80730684 0.82390728 0.82726269 0.83392936] mean value: 0.8280132450331126 key: test_jcc value: [0.56521739 0.63157895 0.58333333 0.61904762 0.78947368 0.43478261 0.6 0.61904762 0.5 0.52 ] mean value: 0.5862481203007519 key: train_jcc value: [0.7183908 0.7247191 0.6972973 0.71111111 0.71111111 0.73255814 0.67777778 0.69886364 0.70621469 0.71751412] mean value: 0.7095557792476436 MCC on Blind test: 0.3 MCC on Training: 0.46 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01304889 0.01713562 0.01745105 0.01713657 0.01858878 0.02095652 0.0184989 0.01926422 0.01989818 0.01952291] mean value: 0.01815016269683838 key: score_time value: [0.0098381 0.01189923 0.01217651 0.01215482 0.01225924 0.0121603 0.01221132 0.01210284 0.012254 0.0120573 ] mean value: 0.011911368370056153 key: test_mcc value: [0.41176471 0.06666667 0.24913644 0.08304548 0.70694678 0.2193445 0.46471292 0.45387763 0.18565266 0.27205882] mean value: 0.31132066043235485 key: train_mcc value: [0.65309871 0.49923599 0.3575186 0.35156152 0.59079716 0.6878202 0.54967499 0.55640793 0.41887295 0.66224418] mean value: 0.5327232237663633 key: test_fscore value: [0.70588235 0.61904762 0.36363636 0.27272727 0.82758621 0.62857143 0.74285714 0.74285714 0.46153846 0.64705882] mean value: 0.6011762814602573 key: train_fscore value: [0.81003584 0.77384196 0.36956522 0.36065574 0.75095785 0.84590164 0.7943662 0.78456592 0.47474747 0.82474227] mean value: 0.6789380109363907 key: test_precision value: [0.70588235 0.52 0.8 0.6 0.92307692 0.57894737 0.68421053 0.72222222 0.66666667 0.64705882] mean value: 0.6848064883173243 key: train_precision value: [0.87596899 0.65437788 1. 1. 0.89090909 0.83766234 0.69117647 0.75776398 0.97916667 0.85106383] mean value: 0.8538089243201238 key: test_recall value: [0.70588235 0.76470588 0.23529412 0.17647059 0.75 0.6875 0.8125 0.76470588 0.35294118 0.64705882] mean value: 0.5897058823529411 key: train_recall value: [0.75333333 0.94666667 0.22666667 0.22 0.64900662 0.85430464 0.93377483 0.81333333 0.31333333 0.8 ] mean value: 0.6510419426048565 key: test_accuracy value: [0.70588235 0.52941176 0.58823529 0.52941176 0.84848485 0.60606061 0.72727273 0.72727273 0.57575758 0.63636364] mean value: 0.647415329768271 key: train_accuracy value: [0.82333333 0.72333333 0.61333333 0.61 0.78405316 0.84385382 0.75747508 0.77740864 0.65448505 0.83056478] mean value: 0.7417840531561463 key: test_roc_auc value: [0.70588235 0.52941176 0.58823529 0.52941176 0.84558824 0.60845588 0.72977941 0.72610294 0.58272059 0.63602941] mean value: 0.6481617647058824 key: train_roc_auc value: [0.82333333 0.72333333 0.61333333 0.61 0.78450331 0.84381898 0.75688742 0.77752759 0.65335541 0.83046358] mean value: 0.7416556291390728 key: test_jcc value: [0.54545455 0.44827586 0.22222222 0.15789474 0.70588235 0.45833333 0.59090909 0.59090909 0.3 0.47826087] mean value: 0.44981421042457476 key: train_jcc value: [0.68072289 0.63111111 0.22666667 0.22 0.60122699 0.73295455 0.6588785 0.64550265 0.31125828 0.70175439] mean value: 0.541007602294977 MCC on Blind test: 0.27 MCC on Training: 0.31 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.11803412 0.08497024 0.08691359 0.07831597 0.07686377 0.07563829 0.07711291 0.07906675 0.08137131 0.09011364] mean value: 0.08484005928039551 key: score_time value: [0.01137185 0.01196551 0.01178932 0.01148772 0.01119447 0.01095557 0.0127089 0.01127315 0.01298904 0.01213145] mean value: 0.011786699295043945 key: test_mcc value: [0.54470478 0.52941176 0.70710678 0.53311399 0.70694678 0.51676076 0.75735294 0.71008133 0.33455882 0.63944497] mean value: 0.5979482932510136 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.73333333 0.76470588 0.84848485 0.75 0.82758621 0.73333333 0.875 0.83870968 0.66666667 0.83333333] mean value: 0.7871153281820363 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84615385 0.76470588 0.875 0.8 0.92307692 0.78571429 0.875 0.92857143 0.6875 0.78947368] mean value: 0.8275196050079952 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.64705882 0.76470588 0.82352941 0.70588235 0.75 0.6875 0.875 0.76470588 0.64705882 0.88235294] mean value: 0.7547794117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76470588 0.76470588 0.85294118 0.76470588 0.84848485 0.75757576 0.87878788 0.84848485 0.66666667 0.81818182] mean value: 0.7965240641711231 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76470588 0.76470588 0.85294118 0.76470588 0.84558824 0.75551471 0.87867647 0.85110294 0.66727941 0.81617647] mean value: 0.7961397058823529 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57894737 0.61904762 0.73684211 0.6 0.70588235 0.57894737 0.77777778 0.72222222 0.5 0.71428571] mean value: 0.6533952528379774 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.34 MCC on Training: 0.6 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (424, 173) Data type to split: actual Split type: 80_20 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 173 No.of cols dropped: 2 No. of columns for x_features: 171 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: 80_20 Total no. of input features: 171 --------No. of numerical features: 165 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 69 Train data size: (55, 171) y_train numbers: Counter({0: 28, 1: 27}) Test data size: (14, 171) y_test_numbers: Counter({0: 7, 1: 7}) y_train ratio: 1.037037037037037 y_test ratio: 1.0 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 28, 1: 28}) (56, 171) Simple Random UnderSampling Counter({0: 27, 1: 27}) (54, 171) Simple Combined Over and UnderSampling Counter({0: 28, 1: 28}) (56, 171) SMOTE_NC OverSampling Counter({0: 28, 1: 28}) (56, 171) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (56, 171) y_train numbers: 56 y_train ratio: 1.0 y_test ratio: 1.0 ================================ Resampling: Random underampling ================================ Train data size: (54, 171) y_train numbers: 54 y_train ratio: 1.0 y_test ratio: 1.0 ================================ Resampling:Combined (over+under) ================================ Train data size: (56, 171) y_train numbers: 56 y_train ratio: 1.0 y_test ratio: 1.0 ============================== Resampling: Smote NC ============================== Train data size: (56, 171) y_train numbers: 56 y_train ratio: 1.0 y_test ratio: 1.0 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... 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Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... RNh@Cƀnumpy.core.numeric _frombuffer(Hy"rA'JUuW]tmUcg$numpydtypei8R(KFU`+x~[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... loky_p!֊V[[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.13160992 0.12639785 0.12344837 0.12484384 0.12810993 0.12372136 0.12339664 0.12461996 0.12652469 0.12429953] mean value: 0.1256972074508667 key: score_time value: [0.01478553 0.01526809 0.01470447 0.01541376 0.01452208 0.01488876 0.01465082 0.01459098 0.01466107 0.01478219] mean value: 0.014826774597167969 key: test_mcc value: [-0.14433757 0.57735027 0.57735027 0.50128041 0.21483446 0. 0.21938173 0.4330127 0.25701934 0.51507875] mean value: 0.31509703677432616 key: train_mcc value: [0.96812393 0.94403021 0.88070485 0.91202919 0.92011778 0.95212188 0.9524878 0.94403021 0.93672192 0.91261829] mean value: 0.9322986048888919 key: test_fscore value: [0.38461538 0.8 0.8 0.74074074 0.62068966 0.53333333 0.56 0.73333333 0.58333333 0.6 ] mean value: 0.6356045780528539 key: train_fscore value: [0.98387097 0.97188755 0.93877551 0.95582329 0.95967742 0.97619048 0.97637795 0.97188755 0.96875 0.95652174] mean value: 0.965976245895197 key: test_precision value: [0.41666667 0.75 0.75 0.76923077 0.6 0.5 0.63636364 0.6875 0.63636364 1. ] mean value: 0.6746124708624709 key: train_precision value: [0.99186992 0.97580645 0.95833333 0.95967742 0.96747967 0.96850394 0.96124031 0.97580645 0.95384615 0.9453125 ] mean value: 0.965787615034146 key: test_recall value: [0.35714286 0.85714286 0.85714286 0.71428571 0.64285714 0.57142857 0.5 0.78571429 0.53846154 0.42857143] mean value: 0.6252747252747253 key: train_recall value: [0.976 0.968 0.92 0.952 0.952 0.984 0.992 0.968 0.98412698 0.968 ] mean value: 0.9664126984126984 key: test_accuracy value: [0.42857143 0.78571429 0.78571429 0.75 0.60714286 0.5 0.60714286 0.71428571 0.62962963 0.7037037 ] mean value: 0.6511904761904763 key: train_accuracy value: [0.984 0.972 0.94 0.956 0.96 0.976 0.976 0.972 0.96812749 0.9561753 ] mean value: 0.9660302788844621 key: test_roc_auc value: [0.42857143 0.78571429 0.78571429 0.75 0.60714286 0.5 0.60714286 0.71428571 0.62637363 0.71428571] mean value: 0.6519230769230769 key: train_roc_auc value: [0.984 0.972 0.94 0.956 0.96 0.976 0.976 0.972 0.96806349 0.95622222] mean value: 0.9660285714285713 key: test_jcc value: [0.23809524 0.66666667 0.66666667 0.58823529 0.45 0.36363636 0.38888889 0.57894737 0.41176471 0.42857143] mean value: 0.4781472620946306 key: train_jcc value: [0.96825397 0.9453125 0.88461538 0.91538462 0.92248062 0.95348837 0.95384615 0.9453125 0.93939394 0.91666667] mean value: 0.9344754720408789 MCC on Blind test: 0.38 MCC on Training: 0.32 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.17636514 0.1966691 0.22740793 0.22877216 0.19695377 0.20271945 0.20023298 0.17983294 0.2020154 0.18225169] mean value: 0.19932205677032472 key: score_time value: [0.04450202 0.06447458 0.05629849 0.05983567 0.0725131 0.05488658 0.04455757 0.04585147 0.0382998 0.03723431] mean value: 0.051845359802246097 key: test_mcc value: [-0.07161149 0.59628479 0.28571429 0.71428571 0.50128041 0.35805744 0.53530338 0.36563621 0.33516484 0.58556764] mean value: 0.4205683223805717 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.8125 0.64285714 0.85714286 0.75862069 0.66666667 0.69565217 0.70967742 0.66666667 0.75 ] mean value: 0.7004228060700833 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.46153846 0.72222222 0.64285714 0.85714286 0.73333333 0.69230769 0.88888889 0.64705882 0.64285714 0.9 ] mean value: 0.7188206564677153 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.42857143 0.92857143 0.64285714 0.85714286 0.78571429 0.64285714 0.57142857 0.78571429 0.69230769 0.64285714] mean value: 0.6978021978021978 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.46428571 0.78571429 0.64285714 0.85714286 0.75 0.67857143 0.75 0.67857143 0.66666667 0.77777778] mean value: 0.7051587301587302 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.46428571 0.78571429 0.64285714 0.85714286 0.75 0.67857143 0.75 0.67857143 0.66758242 0.78296703] mean value: 0.7057692307692307 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.68421053 0.47368421 0.75 0.61111111 0.5 0.53333333 0.55 0.5 0.6 ] mean value: 0.5488053467000835 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.38 MCC on Training: 0.42 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02632093 0.01885819 0.02107668 0.01885843 0.01812959 0.01869416 0.01868391 0.0184834 0.0186255 0.01731467] mean value: 0.019504547119140625 key: score_time value: [0.01126361 0.00838757 0.00850558 0.008425 0.00838876 0.00851464 0.00842881 0.00857973 0.00850272 0.00856495] mean value: 0.008756136894226075 key: test_mcc value: [-0.07161149 0.5118907 0.28571429 0.28867513 0.1490712 0.28867513 0.07647191 0.57735027 0.18681319 0.10989011] mean value: 0.24029404400720558 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.77419355 0.64285714 0.66666667 0.625 0.66666667 0.43478261 0.8 0.59259259 0.57142857] mean value: 0.6218632241738834 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.46153846 0.70588235 0.64285714 0.625 0.55555556 0.625 0.55555556 0.75 0.57142857 0.57142857] mean value: 0.6064246211305034 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.42857143 0.85714286 0.64285714 0.71428571 0.71428571 0.71428571 0.35714286 0.85714286 0.61538462 0.57142857] mean value: 0.6472527472527473 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.46428571 0.75 0.64285714 0.64285714 0.57142857 0.64285714 0.53571429 0.78571429 0.59259259 0.55555556] mean value: 0.6183862433862434 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.46428571 0.75 0.64285714 0.64285714 0.57142857 0.64285714 0.53571429 0.78571429 0.59340659 0.55494505] mean value: 0.6184065934065934 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.63157895 0.47368421 0.5 0.45454545 0.5 0.27777778 0.66666667 0.42105263 0.4 ] mean value: 0.46110199741778696 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.1 MCC on Training: 0.24 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00980902 0.00939584 0.01033759 0.00970125 0.00984788 0.00965714 0.00934935 0.0096314 0.01044703 0.00977945] mean value: 0.009795594215393066 key: score_time value: [0.00874376 0.00916171 0.00956011 0.00947332 0.00922918 0.00982332 0.00887179 0.00884223 0.00937247 0.00847387] mean value: 0.009155178070068359 key: test_mcc value: [ 0.07161149 0.21483446 0.21938173 0.21483446 0.21483446 0.4330127 0.1490712 0.28867513 -0.03846154 0.26519742] mean value: 0.203299151545577 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.51851852 0.62068966 0.64516129 0.59259259 0.62068966 0.73333333 0.5 0.66666667 0.46153846 0.61538462] mean value: 0.5974574788701595 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.53846154 0.6 0.58823529 0.61538462 0.6 0.6875 0.6 0.625 0.46153846 0.66666667] mean value: 0.5982786576168929 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.64285714 0.71428571 0.57142857 0.64285714 0.78571429 0.42857143 0.71428571 0.46153846 0.57142857] mean value: 0.6032967032967033 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.53571429 0.60714286 0.60714286 0.60714286 0.60714286 0.71428571 0.57142857 0.64285714 0.48148148 0.62962963] mean value: 0.6003968253968254 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.53571429 0.60714286 0.60714286 0.60714286 0.60714286 0.71428571 0.57142857 0.64285714 0.48076923 0.63186813] mean value: 0.6005494505494505 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.35 0.45 0.47619048 0.42105263 0.45 0.57894737 0.33333333 0.5 0.3 0.44444444] mean value: 0.4303968253968254 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.2 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10129642 0.10158324 0.10171413 0.10225511 0.10149646 0.10538363 0.1018796 0.10166287 0.10162807 0.10112476] mean value: 0.1020024299621582 key: score_time value: [0.0167613 0.01727676 0.01702905 0.01692677 0.01863313 0.0170393 0.01719379 0.01721978 0.01691151 0.0169878 ] mean value: 0.01719791889190674 key: test_mcc value: [0.14433757 0.57142857 0.5118907 0.4330127 0.28571429 0.35805744 0.4472136 0.21483446 0.40659341 0.19555819] mean value: 0.3568640917913492 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.78571429 0.77419355 0.73333333 0.64285714 0.66666667 0.66666667 0.62068966 0.69230769 0.56 ] mean value: 0.6742428991105298 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5625 0.78571429 0.70588235 0.6875 0.64285714 0.69230769 0.8 0.6 0.69230769 0.63636364] mean value: 0.6805432802491627 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.64285714 0.78571429 0.85714286 0.78571429 0.64285714 0.64285714 0.57142857 0.64285714 0.69230769 0.5 ] mean value: 0.6763736263736264 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.57142857 0.78571429 0.75 0.71428571 0.64285714 0.67857143 0.71428571 0.60714286 0.7037037 0.59259259] mean value: 0.6760582010582012 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.57142857 0.78571429 0.75 0.71428571 0.64285714 0.67857143 0.71428571 0.60714286 0.7032967 0.59615385] mean value: 0.6763736263736264 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.64705882 0.63157895 0.57894737 0.47368421 0.5 0.5 0.45 0.52941176 0.38888889] mean value: 0.5128141432011402 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.36 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.4365139 0.43836117 0.43901515 0.45947599 0.44626713 0.43417215 0.43204713 0.43556619 0.44169307 0.43415666] mean value: 0.4397268533706665 key: score_time value: [0.00889635 0.00905752 0.00921154 0.0093286 0.00892043 0.00901937 0.00930524 0.00932193 0.00898623 0.00934553] mean value: 0.009139275550842286 key: test_mcc value: [0. 0.59628479 0.50128041 0.78772636 0.57735027 0.42857143 0.59628479 0.36563621 0.40659341 0.43206933] mean value: 0.46917970058423314 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.46153846 0.8125 0.75862069 0.88888889 0.8 0.71428571 0.75 0.70967742 0.69230769 0.66666667] mean value: 0.7254485532697436 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.72222222 0.73333333 0.92307692 0.75 0.71428571 0.9 0.64705882 0.69230769 0.8 ] mean value: 0.7382284708755297 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.42857143 0.92857143 0.78571429 0.85714286 0.85714286 0.71428571 0.64285714 0.78571429 0.69230769 0.57142857] mean value: 0.7263736263736263 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.78571429 0.75 0.89285714 0.78571429 0.71428571 0.78571429 0.67857143 0.7037037 0.7037037 ] mean value: 0.730026455026455 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.78571429 0.75 0.89285714 0.78571429 0.71428571 0.78571429 0.67857143 0.7032967 0.70879121] mean value: 0.7304945054945055 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.3 0.68421053 0.61111111 0.8 0.66666667 0.55555556 0.6 0.55 0.52941176 0.5 ] mean value: 0.5796955624355006 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.42 MCC on Training: 0.47 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00854754 0.0086019 0.00868583 0.00849032 0.00858498 0.00847602 0.00859118 0.00859785 0.00848937 0.00881624] mean value: 0.008588123321533202 key: score_time value: [0.00841045 0.00842881 0.00847435 0.00835609 0.00835133 0.00837493 0.00829244 0.00841999 0.00833821 0.00832748] mean value: 0.008377408981323243 key: test_mcc value: [ 0.28867513 0.27975144 0.28867513 0.21483446 0.53530338 0.42857143 0.14285714 0.35805744 0.48892056 -0.05241424] mean value: 0.2973231875059101 key: train_mcc value: [0.38549554 0.36115756 0.37720901 0.39205019 0.36895758 0.37774468 0.36041544 0.39280529 0.34912302 0.36817583] mean value: 0.37331341248492067 key: test_fscore value: [0.66666667 0.7027027 0.66666667 0.62068966 0.78787879 0.71428571 0.57142857 0.66666667 0.69565217 0.5625 ] mean value: 0.6655137605381233 key: train_fscore value: [0.70498084 0.69230769 0.7 0.6984127 0.69498069 0.70229008 0.6875 0.70542636 0.69402985 0.71378092] mean value: 0.6993709131012171 key: test_precision value: [0.625 0.56521739 0.625 0.6 0.68421053 0.71428571 0.57142857 0.69230769 0.8 0.5 ] mean value: 0.6377449895642114 key: train_precision value: [0.67647059 0.66666667 0.67407407 0.69291339 0.67164179 0.67153285 0.67175573 0.68421053 0.65492958 0.63924051] mean value: 0.6703435687863444 key: test_recall value: [0.71428571 0.92857143 0.71428571 0.64285714 0.92857143 0.71428571 0.57142857 0.64285714 0.61538462 0.64285714] mean value: 0.7115384615384616 key: train_recall value: [0.736 0.72 0.728 0.704 0.72 0.736 0.704 0.728 0.73809524 0.808 ] mean value: 0.7322095238095238 key: test_accuracy value: [0.64285714 0.60714286 0.64285714 0.60714286 0.75 0.71428571 0.57142857 0.67857143 0.74074074 0.48148148] mean value: 0.6436507936507937 key: train_accuracy value: [0.692 0.68 0.688 0.696 0.684 0.688 0.68 0.696 0.67330677 0.67729084] mean value: 0.6854597609561753 key: test_roc_auc value: [0.64285714 0.60714286 0.64285714 0.60714286 0.75 0.71428571 0.57142857 0.67857143 0.73626374 0.47527473] mean value: 0.6425824175824176 key: train_roc_auc value: [0.692 0.68 0.688 0.696 0.684 0.688 0.68 0.696 0.67304762 0.67780952] mean value: 0.6854857142857143 key: test_jcc value: [0.5 0.54166667 0.5 0.45 0.65 0.55555556 0.4 0.5 0.53333333 0.39130435] mean value: 0.5021859903381642 key: train_jcc value: [0.5443787 0.52941176 0.53846154 0.53658537 0.53254438 0.54117647 0.52380952 0.54491018 0.53142857 0.55494505] mean value: 0.5377651546356259 MCC on Blind test: 0.25 MCC on Training: 0.3 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.08027697 0.05809784 0.11569333 0.09656835 0.0799849 0.06371212 0.06417513 0.03803301 0.05415893 0.07849145] mean value: 0.07291920185089111 key: score_time value: [0.0220077 0.03144598 0.02299809 0.0365622 0.03247333 0.02439117 0.01293159 0.01284289 0.02502656 0.02309513] mean value: 0.02437746524810791 key: test_mcc value: [0.28867513 0.65814518 0.57142857 0.35805744 0. 0.21483446 0.1490712 0.07312724 0.02842561 0.03314968] mean value: 0.23749145187829418 key: train_mcc value: [0.9280297 0.9280297 0.91226277 0.94403021 0.936 0.904 0.904 0.936 0.91237539 0.95231443] mean value: 0.9257042199157871 key: test_fscore value: [0.66666667 0.83870968 0.78571429 0.68965517 0.46153846 0.62068966 0.5 0.48 0.43478261 0.55172414] mean value: 0.6029480665551662 key: train_fscore value: [0.96385542 0.96414343 0.95546559 0.97211155 0.968 0.952 0.952 0.968 0.95652174 0.97619048] mean value: 0.9628288204131874 key: test_precision value: [0.625 0.76470588 0.78571429 0.66666667 0.5 0.6 0.6 0.54545455 0.5 0.53333333] mean value: 0.6120874713521772 key: train_precision value: [0.96774194 0.96031746 0.96721311 0.96825397 0.968 0.952 0.952 0.968 0.95275591 0.96850394] mean value: 0.9624786321329083 key: test_recall value: [0.71428571 0.92857143 0.78571429 0.71428571 0.42857143 0.64285714 0.42857143 0.42857143 0.38461538 0.57142857] mean value: 0.6027472527472527 key: train_recall value: [0.96 0.968 0.944 0.976 0.968 0.952 0.952 0.968 0.96031746 0.984 ] mean value: 0.9632317460317459 key: test_accuracy value: [0.64285714 0.82142857 0.78571429 0.67857143 0.5 0.60714286 0.57142857 0.53571429 0.51851852 0.51851852] mean value: 0.617989417989418 key: train_accuracy value: [0.964 0.964 0.956 0.972 0.968 0.952 0.952 0.968 0.9561753 0.97609562] mean value: 0.9628270916334662 key: test_roc_auc value: [0.64285714 0.82142857 0.78571429 0.67857143 0.5 0.60714286 0.57142857 0.53571429 0.51373626 0.51648352] mean value: 0.6173076923076922 key: train_roc_auc value: [0.964 0.964 0.956 0.972 0.968 0.952 0.952 0.968 0.95615873 0.97612698] mean value: 0.9628285714285714 key: test_jcc value: [0.5 0.72222222 0.64705882 0.52631579 0.3 0.45 0.33333333 0.31578947 0.27777778 0.38095238] mean value: 0.44534498009730206 key: train_jcc value: [0.93023256 0.93076923 0.91472868 0.94573643 0.9379845 0.90839695 0.90839695 0.9379845 0.91666667 0.95348837] mean value: 0.9284384829325358 MCC on Blind test: 0.13 MCC on Training: 0.24 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.03003359 0.00867963 0.00890112 0.00927496 0.00907946 0.008394 0.01295233 0.01039028 0.00993371 0.01025224] mean value: 0.01178913116455078 key: score_time value: [0.01095343 0.01003337 0.01698279 0.01598287 0.01539969 0.01599622 0.04224873 0.01609468 0.01579905 0.01682234] mean value: 0.01763131618499756 key: test_mcc value: [ 0.36563621 0.17407766 0.36563621 0.35805744 -0.21483446 -0.14285714 -0.07647191 0.28867513 0.10989011 0.10989011] mean value: 0.13376993551184177 key: train_mcc value: [0.49601587 0.46437165 0.40104085 0.45605838 0.47206043 0.45746624 0.44141479 0.42448929 0.51426779 0.47473806] mean value: 0.46019233555703043 key: test_fscore value: [0.70967742 0.66666667 0.70967742 0.66666667 0.37037037 0.42857143 0.34782609 0.61538462 0.53846154 0.57142857] mean value: 0.5624730783216055 key: train_fscore value: [0.74900398 0.7372549 0.68879668 0.72580645 0.73387097 0.73846154 0.73076923 0.71875 0.76264591 0.7421875 ] mean value: 0.7327547169504951 key: test_precision value: [0.64705882 0.54545455 0.64705882 0.69230769 0.38461538 0.42857143 0.44444444 0.66666667 0.53846154 0.57142857] mean value: 0.5566067919009094 key: train_precision value: [0.74603175 0.72307692 0.71551724 0.73170732 0.7398374 0.71111111 0.7037037 0.70229008 0.7480916 0.72519084] mean value: 0.7246557959833918 key: test_recall value: [0.78571429 0.85714286 0.78571429 0.64285714 0.35714286 0.42857143 0.28571429 0.57142857 0.53846154 0.57142857] mean value: 0.5824175824175823 key: train_recall value: [0.752 0.752 0.664 0.72 0.728 0.768 0.76 0.736 0.77777778 0.76 ] mean value: 0.7417777777777778 key: test_accuracy value: [0.67857143 0.57142857 0.67857143 0.67857143 0.39285714 0.42857143 0.46428571 0.64285714 0.55555556 0.55555556] mean value: 0.5646825396825397 key: train_accuracy value: [0.748 0.732 0.7 0.728 0.736 0.728 0.72 0.712 0.75697211 0.73705179] mean value: 0.7298023904382471 key: test_roc_auc value: [0.67857143 0.57142857 0.67857143 0.67857143 0.39285714 0.42857143 0.46428571 0.64285714 0.55494505 0.55494505] mean value: 0.5645604395604396 key: train_roc_auc value: [0.748 0.732 0.7 0.728 0.736 0.728 0.72 0.712 0.75688889 0.73714286] mean value: 0.7298031746031747 key: test_jcc value: [0.55 0.5 0.55 0.5 0.22727273 0.27272727 0.21052632 0.44444444 0.36842105 0.4 ] mean value: 0.40233918128654966 key: train_jcc value: [0.59872611 0.58385093 0.52531646 0.56962025 0.57961783 0.58536585 0.57575758 0.56097561 0.6163522 0.59006211] mean value: 0.5785644941813679 MCC on Blind test: 0.06 MCC on Training: 0.13 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.06327343 0.06530857 0.09730339 0.05098176 0.07526469 0.0654285 0.06463885 0.06701899 0.08650851 0.07277727] mean value: 0.07085039615631103 key: score_time value: [0.02955723 0.01657557 0.02868676 0.02863407 0.02792621 0.02376008 0.02413678 0.0219276 0.02406621 0.02365518] mean value: 0.024892568588256836 key: test_mcc value: [ 0.07312724 0.2981424 0.22941573 0.4330127 0.21483446 0.28867513 -0.08247861 -0.07161149 0.03314968 0.27857101] mean value: 0.16948382661047132 key: train_mcc value: [0.79291396 0.71282164 0.71236482 0.70402253 0.75202407 0.728 0.73602355 0.75202407 0.77688889 0.71354172] mean value: 0.7380625251809052 key: test_fscore value: [0.58064516 0.6875 0.66666667 0.73333333 0.62068966 0.66666667 0.28571429 0.48275862 0.48 0.58333333] mean value: 0.5787307722866678 key: train_fscore value: [0.8984375 0.859375 0.85826772 0.85258964 0.87550201 0.864 0.8685259 0.87649402 0.88888889 0.85826772] mean value: 0.8700348391744873 key: test_precision value: [0.52941176 0.61111111 0.57894737 0.6875 0.6 0.625 0.42857143 0.46666667 0.5 0.7 ] mean value: 0.5727208339476142 key: train_precision value: [0.8778626 0.83969466 0.84496124 0.84920635 0.87903226 0.864 0.86507937 0.87301587 0.88888889 0.84496124] mean value: 0.8626702466783543 key: test_recall value: [0.64285714 0.78571429 0.78571429 0.78571429 0.64285714 0.71428571 0.21428571 0.5 0.46153846 0.5 ] mean value: 0.6032967032967033 key: train_recall value: [0.92 0.88 0.872 0.856 0.872 0.864 0.872 0.88 0.88888889 0.872 ] mean value: 0.877688888888889 key: test_accuracy value: [0.53571429 0.64285714 0.60714286 0.71428571 0.60714286 0.64285714 0.46428571 0.46428571 0.51851852 0.62962963] mean value: 0.5826719576719577 key: train_accuracy value: [0.896 0.856 0.856 0.852 0.876 0.864 0.868 0.876 0.88844622 0.85657371] mean value: 0.8689019920318725 key: test_roc_auc value: [0.53571429 0.64285714 0.60714286 0.71428571 0.60714286 0.64285714 0.46428571 0.46428571 0.51648352 0.63461538] mean value: 0.582967032967033 key: train_roc_auc value: [0.896 0.856 0.856 0.852 0.876 0.864 0.868 0.876 0.88844444 0.85663492] mean value: 0.8689079365079365 key: test_jcc value: [0.40909091 0.52380952 0.5 0.57894737 0.45 0.5 0.16666667 0.31818182 0.31578947 0.41176471] mean value: 0.4174250465736534 key: train_jcc value: [0.81560284 0.75342466 0.75172414 0.74305556 0.77857143 0.76056338 0.76760563 0.78014184 0.8 0.75172414] mean value: 0.7702413612458872 MCC on Blind test: 0.11 MCC on Training: 0.17 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.07235575 0.07136869 0.04726791 0.03604126 0.03612018 0.03632832 0.03577828 0.03548861 0.03592539 0.03777575] mean value: 0.04444501399993896 key: score_time value: [0.01465654 0.02264142 0.01212549 0.01192808 0.0121398 0.01215363 0.01190066 0.01221561 0.01215649 0.0122335 ] mean value: 0.013415122032165527 key: test_mcc value: [0.07161149 0.53530338 0.36563621 0.57735027 0.28571429 0.28571429 0.31622777 0.22941573 0.33516484 0.25824176] mean value: 0.326038001241101 key: train_mcc value: [0.584 0.51333229 0.56016135 0.5280169 0.58467393 0.56807272 0.55207067 0.56001792 0.59666376 0.57788786] mean value: 0.562489739289276 key: test_fscore value: [0.55172414 0.78787879 0.70967742 0.76923077 0.64285714 0.64285714 0.54545455 0.66666667 0.66666667 0.64285714] mean value: 0.6625870421754738 key: train_fscore value: [0.792 0.76447876 0.7826087 0.76494024 0.796875 0.78571429 0.77777778 0.78087649 0.80754717 0.79051383] mean value: 0.7843332260494147 key: test_precision value: [0.53333333 0.68421053 0.64705882 0.83333333 0.64285714 0.64285714 0.75 0.57894737 0.64285714 0.64285714] mean value: 0.6598311956361493 key: train_precision value: [0.792 0.73880597 0.7734375 0.76190476 0.77862595 0.77952756 0.77165354 0.77777778 0.76978417 0.78125 ] mean value: 0.7724767239054341 key: test_recall value: [0.57142857 0.92857143 0.78571429 0.71428571 0.64285714 0.64285714 0.42857143 0.78571429 0.69230769 0.64285714] mean value: 0.6835164835164835 key: train_recall value: [0.792 0.792 0.792 0.768 0.816 0.792 0.784 0.784 0.84920635 0.8 ] mean value: 0.7969206349206349 key: test_accuracy value: [0.53571429 0.75 0.67857143 0.78571429 0.64285714 0.64285714 0.64285714 0.60714286 0.66666667 0.62962963] mean value: 0.6582010582010582 key: train_accuracy value: [0.792 0.756 0.78 0.764 0.792 0.784 0.776 0.78 0.79681275 0.78884462] mean value: 0.7809657370517928 key: test_roc_auc value: [0.53571429 0.75 0.67857143 0.78571429 0.64285714 0.64285714 0.64285714 0.60714286 0.66758242 0.62912088] mean value: 0.6582417582417582 key: train_roc_auc value: [0.792 0.756 0.78 0.764 0.792 0.784 0.776 0.78 0.79660317 0.78888889] mean value: 0.7809492063492064 key: test_jcc value: [0.38095238 0.65 0.55 0.625 0.47368421 0.47368421 0.375 0.5 0.5 0.47368421] mean value: 0.5002005012531329 key: train_jcc value: [0.65562914 0.61875 0.64285714 0.61935484 0.66233766 0.64705882 0.63636364 0.64052288 0.67721519 0.65359477] mean value: 0.6453684079802621 MCC on Blind test: 0.37 MCC on Training: 0.33 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.47657967 0.49272037 0.58378768 0.56772757 0.49551463 0.47991943 0.60773325 0.48119974 0.50890255 0.498106 ] mean value: 0.5192190885543824 key: score_time value: [0.01231408 0.0123179 0.01213169 0.01208472 0.01208472 0.01208329 0.01211023 0.01213408 0.01193833 0.01200604] mean value: 0.012120509147644043 key: test_mcc value: [0.14285714 0.47434165 0.2981424 0.65814518 0.28571429 0.14285714 0.21483446 0.35805744 0.25701934 0.40659341] mean value: 0.3238562444847083 key: train_mcc value: [0.552 0.49601587 0.568 0.49614291 0.55313398 0.54443572 0.43212447 0.52006657 0.47492806 0.54631523] mean value: 0.5183162822915595 key: test_fscore value: [0.57142857 0.76470588 0.6875 0.8 0.64285714 0.57142857 0.59259259 0.66666667 0.58333333 0.71428571] mean value: 0.6594798474945535 key: train_fscore value: [0.776 0.74900398 0.784 0.75098814 0.78294574 0.77647059 0.71936759 0.76190476 0.74615385 0.77647059] mean value: 0.7623305236252346 key: test_precision value: [0.57142857 0.65 0.61111111 0.90909091 0.64285714 0.57142857 0.61538462 0.69230769 0.63636364 0.71428571] mean value: 0.6614257964257966 key: train_precision value: [0.776 0.74603175 0.784 0.7421875 0.7593985 0.76153846 0.7109375 0.75590551 0.7238806 0.76153846] mean value: 0.7521418274175219 key: test_recall value: [0.57142857 0.92857143 0.78571429 0.71428571 0.64285714 0.57142857 0.57142857 0.64285714 0.53846154 0.71428571] mean value: 0.6681318681318682 key: train_recall value: [0.776 0.752 0.784 0.76 0.808 0.792 0.728 0.768 0.76984127 0.792 ] mean value: 0.772984126984127 key: test_accuracy value: [0.57142857 0.71428571 0.64285714 0.82142857 0.64285714 0.57142857 0.60714286 0.67857143 0.62962963 0.7037037 ] mean value: 0.6583333333333334 key: train_accuracy value: [0.776 0.748 0.784 0.748 0.776 0.772 0.716 0.76 0.73705179 0.77290837] mean value: 0.758996015936255 key: test_roc_auc value: [0.57142857 0.71428571 0.64285714 0.82142857 0.64285714 0.57142857 0.60714286 0.67857143 0.62637363 0.7032967 ] mean value: 0.657967032967033 key: train_roc_auc value: [0.776 0.748 0.784 0.748 0.776 0.772 0.716 0.76 0.73692063 0.77298413] mean value: 0.7589904761904762 key: test_jcc value: [0.4 0.61904762 0.52380952 0.66666667 0.47368421 0.4 0.42105263 0.5 0.41176471 0.55555556] mean value: 0.49715809130669814 key: train_jcc value: [0.63398693 0.59872611 0.64473684 0.60126582 0.6433121 0.63461538 0.5617284 0.61538462 0.59509202 0.63461538] mean value: 0.6163463613772148 MCC on Blind test: 0.44 MCC on Training: 0.32 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.17859507 1.45861363 1.39832067 1.16997719 1.38419437 0.99715471 1.3383863 1.23490596 0.79626298 1.19329453] mean value: 1.2149705410003662 key: score_time value: [0.01475716 0.01423025 0.01323438 0.01557779 0.01231813 0.01241636 0.01247382 0.01227498 0.0123117 0.01255322] mean value: 0.013214778900146485 key: test_mcc value: [0.28867513 0.72168784 0.38235956 0.5118907 0.21483446 0.28571429 0.09325048 0.14433757 0.33516484 0.10989011] mean value: 0.3087804965524271 key: train_mcc value: [0.90504321 0.86469203 0.86535795 0.86469203 0.88138309 0.85610959 0.89625816 0.88138309 0.68096337 0.9204293 ] mean value: 0.8616311825332025 key: test_fscore value: [0.66666667 0.86666667 0.72727273 0.77419355 0.62068966 0.64285714 0.31578947 0.6 0.66666667 0.57142857] mean value: 0.6452231118802163 key: train_fscore value: [0.953125 0.93061224 0.93004115 0.93061224 0.9382716 0.92741935 0.94736842 0.9382716 0.84870849 0.95967742] mean value: 0.9304107534266887 key: test_precision value: [0.625 0.8125 0.63157895 0.70588235 0.6 0.64285714 0.6 0.5625 0.64285714 0.57142857] mean value: 0.6394604157452455 key: train_precision value: [0.93129771 0.95 0.95762712 0.95 0.96610169 0.93495935 0.95901639 0.96610169 0.79310345 0.96747967] mean value: 0.937568708450697 key: test_recall value: [0.71428571 0.92857143 0.85714286 0.85714286 0.64285714 0.64285714 0.21428571 0.64285714 0.69230769 0.57142857] mean value: 0.6763736263736264 key: train_recall value: [0.976 0.912 0.904 0.912 0.912 0.92 0.936 0.912 0.91269841 0.952 ] mean value: 0.9248698412698413 key: test_accuracy value: [0.64285714 0.85714286 0.67857143 0.75 0.60714286 0.64285714 0.53571429 0.57142857 0.66666667 0.55555556] mean value: 0.6507936507936509 key: train_accuracy value: [0.952 0.932 0.932 0.932 0.94 0.928 0.948 0.94 0.83665339 0.96015936] mean value: 0.9300812749003983 key: test_roc_auc value: [0.64285714 0.85714286 0.67857143 0.75 0.60714286 0.64285714 0.53571429 0.57142857 0.66758242 0.55494505] mean value: 0.650824175824176 key: train_roc_auc value: [0.952 0.932 0.932 0.932 0.94 0.928 0.948 0.94 0.83634921 0.96012698] mean value: 0.9300476190476189 key: test_jcc value: [0.5 0.76470588 0.57142857 0.63157895 0.45 0.47368421 0.1875 0.42857143 0.5 0.4 ] mean value: 0.4907469040247678 key: train_jcc value: [0.91044776 0.87022901 0.86923077 0.87022901 0.88372093 0.86466165 0.9 0.88372093 0.73717949 0.92248062] mean value: 0.8711900167626956 MCC on Blind test: 0.16 MCC on Training: 0.31 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01363063 0.01304746 0.01075482 0.00936985 0.00926352 0.00948238 0.00976658 0.00981617 0.01031184 0.00965762] mean value: 0.010510087013244629 key: score_time value: [0.0138998 0.00947094 0.00961757 0.00957799 0.00933671 0.00974154 0.00891876 0.00941706 0.00997543 0.01011586] mean value: 0.010007166862487793 key: test_mcc value: [0.07161149 0.34815531 0.21483446 0.4472136 0.36563621 0.4330127 0.14433757 0.35805744 0.25824176 0.10482848] mean value: 0.27459290172139844 key: train_mcc value: [0.30621273 0.28365431 0.29009627 0.29009627 0.32376195 0.29009627 0.33072045 0.29070095 0.31697116 0.29326324] mean value: 0.3015573596357617 key: test_fscore value: [0.55172414 0.72222222 0.62068966 0.75 0.70967742 0.73333333 0.53846154 0.66666667 0.61538462 0.625 ] mean value: 0.6533159588526661 key: train_fscore value: [0.67169811 0.66666667 0.66415094 0.66415094 0.68401487 0.66415094 0.68421053 0.66666667 0.67910448 0.66415094] mean value: 0.6708965093941991 key: test_precision value: [0.53333333 0.59090909 0.6 0.66666667 0.64705882 0.6875 0.58333333 0.69230769 0.61538462 0.55555556] mean value: 0.6172049111019697 key: train_precision value: [0.63571429 0.62068966 0.62857143 0.62857143 0.63888889 0.62857143 0.64539007 0.62676056 0.64084507 0.62857143] mean value: 0.6322574248786106 key: test_recall value: [0.57142857 0.92857143 0.64285714 0.85714286 0.78571429 0.78571429 0.5 0.64285714 0.61538462 0.71428571] mean value: 0.7043956043956044 key: train_recall value: [0.712 0.72 0.704 0.704 0.736 0.704 0.728 0.712 0.72222222 0.704 ] mean value: 0.7146222222222222 key: test_accuracy value: [0.53571429 0.64285714 0.60714286 0.71428571 0.67857143 0.71428571 0.57142857 0.67857143 0.62962963 0.55555556] mean value: 0.6328042328042327 key: train_accuracy value: [0.652 0.64 0.644 0.644 0.66 0.644 0.664 0.644 0.65737052 0.64541833] mean value: 0.6494788844621515 key: test_roc_auc value: [0.53571429 0.64285714 0.60714286 0.71428571 0.67857143 0.71428571 0.57142857 0.67857143 0.62912088 0.54945055] mean value: 0.6321428571428571 key: train_roc_auc value: [0.652 0.64 0.644 0.644 0.66 0.644 0.664 0.644 0.65711111 0.64565079] mean value: 0.6494761904761905 key: test_jcc value: [0.38095238 0.56521739 0.45 0.6 0.55 0.57894737 0.36842105 0.5 0.44444444 0.45454545] mean value: 0.4892528092299259 key: train_jcc value: [0.50568182 0.5 0.49717514 0.49717514 0.51977401 0.49717514 0.52 0.5 0.51412429 0.49717514] mean value: 0.5048280688238316 MCC on Blind test: 0.22 MCC on Training: 0.27 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00923967 0.00933647 0.00943494 0.01048541 0.01057506 0.01045704 0.0105207 0.01061487 0.01054645 0.01066804] mean value: 0.010187864303588867 key: score_time value: [0.00880885 0.00864577 0.0099647 0.00977373 0.01003504 0.00999022 0.00999308 0.00996065 0.00994682 0.00992823] mean value: 0.00970470905303955 key: test_mcc value: [-0.22941573 0.6882472 0.21483446 0.42857143 0.2981424 0.35805744 0.07312724 0.2981424 0.5110477 0.43206933] mean value: 0.3072823859744318 key: train_mcc value: [0.47068992 0.42668461 0.47697114 0.44411739 0.46945483 0.46835153 0.47521831 0.45445923 0.44956331 0.44009 ] mean value: 0.45756002684043945 key: test_fscore value: [0.26086957 0.84848485 0.59259259 0.71428571 0.58333333 0.68965517 0.48 0.58333333 0.66666667 0.66666667] mean value: 0.608588789299434 key: train_fscore value: [0.70742358 0.69491525 0.71551724 0.67873303 0.70995671 0.71244635 0.69955157 0.69868996 0.69565217 0.68995633] mean value: 0.700284220159425 key: test_precision value: [0.33333333 0.73684211 0.61538462 0.71428571 0.7 0.66666667 0.54545455 0.7 0.875 0.8 ] mean value: 0.6686966980388033 key: train_precision value: [0.77884615 0.73873874 0.77570093 0.78125 0.77358491 0.76851852 0.79591837 0.76923077 0.76923077 0.75961538] mean value: 0.7710634541767089 key: test_recall value: [0.21428571 1. 0.57142857 0.71428571 0.5 0.71428571 0.42857143 0.5 0.53846154 0.57142857] mean value: 0.5752747252747252 key: train_recall value: [0.648 0.656 0.664 0.6 0.656 0.664 0.624 0.64 0.63492063 0.632 ] mean value: 0.6418920634920635 key: test_accuracy value: [0.39285714 0.82142857 0.60714286 0.71428571 0.64285714 0.67857143 0.53571429 0.64285714 0.74074074 0.7037037 ] mean value: 0.6480158730158729 key: train_accuracy value: [0.732 0.712 0.736 0.716 0.732 0.732 0.732 0.724 0.72111554 0.71713147] mean value: 0.7254247011952192 key: test_roc_auc value: [0.39285714 0.82142857 0.60714286 0.71428571 0.64285714 0.67857143 0.53571429 0.64285714 0.73351648 0.70879121] mean value: 0.6478021978021978 key: train_roc_auc value: [0.732 0.712 0.736 0.716 0.732 0.732 0.732 0.724 0.72146032 0.71679365] mean value: 0.7254253968253968 key: test_jcc value: [0.15 0.73684211 0.42105263 0.55555556 0.41176471 0.52631579 0.31578947 0.41176471 0.5 0.5 ] mean value: 0.45290849673202616 key: train_jcc value: [0.5472973 0.53246753 0.55704698 0.51369863 0.55033557 0.55333333 0.53793103 0.53691275 0.53333333 0.52666667] mean value: 0.5389023129731331 MCC on Blind test: 0.24 MCC on Training: 0.31 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01170444 0.01446462 0.01428366 0.01441145 0.01465797 0.01477575 0.01524448 0.01561737 0.01750588 0.01623178] mean value: 0.014889740943908691 key: score_time value: [0.00989652 0.01208615 0.01212263 0.01209188 0.01218152 0.01214814 0.01236701 0.01217055 0.01231909 0.01233387] mean value: 0.011971735954284668 key: test_mcc value: [ 0.07161149 0.4662524 0.34641016 0.38235956 -0.07647191 0.36563621 0. 0.15811388 -0.01981072 0.36689969] mean value: 0.20610007648295223 key: train_mcc value: [0.52437882 0.29622057 0.35196145 0.51201638 0.4533395 0.52089361 0.15681251 0.49614291 0.28301111 0.55488987] mean value: 0.4149666740934486 key: test_fscore value: [0.51851852 0.52631579 0.35294118 0.60869565 0.34782609 0.70967742 0.66666667 0.64705882 0.58823529 0.60869565] mean value: 0.5574631079435702 key: train_fscore value: [0.7264574 0.38509317 0.38461538 0.75697211 0.55367232 0.77655678 0.67750678 0.75098814 0.70422535 0.74889868] mean value: 0.6464986103802143 key: test_precision value: [0.53846154 1. 1. 0.77777778 0.44444444 0.64705882 0.5 0.55 0.47619048 0.77777778] mean value: 0.6711710838181426 key: train_precision value: [0.82653061 0.86111111 0.96774194 0.75396825 0.94230769 0.71621622 0.51229508 0.7421875 0.54585153 0.83333333] mean value: 0.7701543265016868 key: test_recall value: [0.5 0.35714286 0.21428571 0.5 0.28571429 0.78571429 1. 0.78571429 0.76923077 0.5 ] mean value: 0.5697802197802198 key: train_recall value: [0.648 0.248 0.24 0.76 0.392 0.848 1. 0.76 0.99206349 0.68 ] mean value: 0.6568063492063492 key: test_accuracy value: [0.53571429 0.67857143 0.60714286 0.67857143 0.46428571 0.67857143 0.5 0.57142857 0.48148148 0.66666667] mean value: 0.5862433862433863 key: train_accuracy value: [0.756 0.604 0.616 0.756 0.684 0.756 0.524 0.748 0.58167331 0.77290837] mean value: 0.6798581673306773 key: test_roc_auc value: [0.53571429 0.67857143 0.60714286 0.67857143 0.46428571 0.67857143 0.5 0.57142857 0.49175824 0.67307692] mean value: 0.5879120879120879 key: train_roc_auc value: [0.756 0.604 0.616 0.756 0.684 0.756 0.524 0.748 0.58003175 0.77253968] mean value: 0.6796571428571427 key: test_jcc value: [0.35 0.35714286 0.21428571 0.4375 0.21052632 0.55 0.5 0.47826087 0.41666667 0.4375 ] mean value: 0.3951882423449929 key: train_jcc value: [0.57042254 0.23846154 0.23809524 0.60897436 0.3828125 0.63473054 0.51229508 0.60126582 0.54347826 0.59859155] mean value: 0.49291274245819217 MCC on Blind test: 0.03 MCC on Training: 0.21 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02380443 0.03806591 0.02902269 0.03493381 0.05793786 0.02395225 0.02376032 0.02424216 0.02383685 0.02343082] mean value: 0.030298709869384766 key: score_time value: [0.01335096 0.02851439 0.013587 0.0181818 0.02188611 0.01255488 0.01263213 0.01325417 0.01258922 0.01320601] mean value: 0.01597566604614258 key: test_mcc value: [-0.07161149 0.15811388 0.21938173 0.21483446 0.28867513 0.21483446 -0.07161149 0.14433757 -0.10989011 -0.03846154] mean value: 0.09486026134029023 key: train_mcc value: [0.90690676 0.89362249 0.94548368 0.968496 0.97603123 0.85096294 0.96076892 0.92295821 0.99206349 0.96092777] mean value: 0.9378221493192764 key: test_fscore value: [0.44444444 0.64705882 0.56 0.59259259 0.61538462 0.62068966 0.44444444 0.6 0.44444444 0.5 ] mean value: 0.5469059020012368 key: train_fscore value: [0.95 0.9469697 0.97119342 0.98425197 0.98804781 0.92592593 0.98039216 0.96153846 0.99601594 0.98039216] mean value: 0.9684727527321293 key: test_precision value: [0.46153846 0.55 0.63636364 0.61538462 0.66666667 0.6 0.46153846 0.5625 0.42857143 0.5 ] mean value: 0.548256327006327 key: train_precision value: [0.99130435 0.89928058 1. 0.96899225 0.98412698 0.86206897 0.96153846 0.92592593 1. 0.96153846] mean value: 0.9554775970074745 key: test_recall value: [0.42857143 0.78571429 0.5 0.57142857 0.57142857 0.64285714 0.42857143 0.64285714 0.46153846 0.5 ] mean value: 0.5532967032967033 key: train_recall value: [0.912 1. 0.944 1. 0.992 1. 1. 1. 0.99206349 1. ] mean value: 0.9840063492063493 key: test_accuracy value: [0.46428571 0.57142857 0.60714286 0.60714286 0.64285714 0.60714286 0.46428571 0.57142857 0.44444444 0.48148148] mean value: 0.5461640211640212 key: train_accuracy value: [0.952 0.944 0.972 0.984 0.988 0.92 0.98 0.96 0.99601594 0.98007968] mean value: 0.9676095617529882 key: test_roc_auc value: [0.46428571 0.57142857 0.60714286 0.60714286 0.64285714 0.60714286 0.46428571 0.57142857 0.44505495 0.48076923] mean value: 0.5461538461538462 key: train_roc_auc value: [0.952 0.944 0.972 0.984 0.988 0.92 0.98 0.96 0.99603175 0.98015873] mean value: 0.9676190476190477 key: test_jcc value: [0.28571429 0.47826087 0.38888889 0.42105263 0.44444444 0.45 0.28571429 0.42857143 0.28571429 0.33333333] mean value: 0.3801694453525117 key: train_jcc value: [0.9047619 0.89928058 0.944 0.96899225 0.97637795 0.86206897 0.96153846 0.92592593 0.99206349 0.96153846] mean value: 0.9396547987702977 MCC on Blind test: -0.01 MCC on Training: 0.09 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.63691115 0.62956643 0.58926058 0.61616993 0.61175299 0.65925908 0.68647099 0.63639235 0.62525988 0.6312077 ] mean value: 0.6322251081466674 key: score_time value: [0.17998695 0.14733434 0.18602657 0.13863182 0.19838929 0.14227605 0.16029763 0.16619754 0.17320061 0.16561246] mean value: 0.16579532623291016 key: test_mcc value: [0.1490712 0.59628479 0.4472136 0.64450339 0.35805744 0.35805744 0.4472136 0.5118907 0.40659341 0.52414242] mean value: 0.4443027966018125 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.625 0.8125 0.75 0.82758621 0.68965517 0.68965517 0.66666667 0.77419355 0.69230769 0.69565217] mean value: 0.7223216632998637 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.55555556 0.72222222 0.66666667 0.8 0.66666667 0.66666667 0.8 0.70588235 0.69230769 0.88888889] mean value: 0.7164856711915535 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.71428571 0.92857143 0.85714286 0.85714286 0.71428571 0.71428571 0.57142857 0.85714286 0.69230769 0.57142857] mean value: 0.7478021978021977 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.57142857 0.78571429 0.71428571 0.82142857 0.67857143 0.67857143 0.71428571 0.75 0.7037037 0.74074074] mean value: 0.7158730158730158 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.57142857 0.78571429 0.71428571 0.82142857 0.67857143 0.67857143 0.71428571 0.75 0.7032967 0.74725275] mean value: 0.7164835164835164 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.45454545 0.68421053 0.6 0.70588235 0.52631579 0.52631579 0.5 0.63157895 0.52941176 0.53333333] mean value: 0.5691593958157426 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.34 MCC on Training: 0.44 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.93086481 0.94526696 0.9617064 0.99515414 0.91366696 0.90987396 0.91088629 0.87749577 0.95121098 0.9392221 ] mean value: 0.933534836769104 key: score_time value: [0.24210334 0.20107675 0.20042157 0.22303629 0.18216991 0.23975468 0.23343253 0.21548176 0.18491888 0.21895623] mean value: 0.21413519382476806 key: test_mcc value: [0.14433757 0.59628479 0.4472136 0.72168784 0.5118907 0.28571429 0.5118907 0.57735027 0.26519742 0.34641737] mean value: 0.44079845307264015 key: train_mcc value: [0.87211164 0.83223972 0.84010754 0.80810344 0.84043041 0.8326664 0.84010754 0.83202663 0.82479204 0.8167619 ] mean value: 0.8339347261412211 key: test_fscore value: [0.6 0.8125 0.75 0.86666667 0.77419355 0.64285714 0.72 0.8 0.64285714 0.64 ] mean value: 0.7249074500768049 key: train_fscore value: [0.93650794 0.91699605 0.91935484 0.9047619 0.91869919 0.91764706 0.92063492 0.91633466 0.91338583 0.90836653] mean value: 0.9172688915851446 key: test_precision value: [0.5625 0.72222222 0.66666667 0.8125 0.70588235 0.64285714 0.81818182 0.75 0.6 0.72727273] mean value: 0.7008082930141754 key: train_precision value: [0.92913386 0.90625 0.92682927 0.8976378 0.9338843 0.9 0.91338583 0.91269841 0.90625 0.9047619 ] mean value: 0.9130831363588623 key: test_recall value: [0.64285714 0.92857143 0.85714286 0.92857143 0.85714286 0.64285714 0.64285714 0.85714286 0.69230769 0.57142857] mean value: 0.7620879120879122 key: train_recall value: [0.944 0.928 0.912 0.912 0.904 0.936 0.928 0.92 0.92063492 0.912 ] mean value: 0.9216634920634922 key: test_accuracy value: [0.57142857 0.78571429 0.71428571 0.85714286 0.75 0.64285714 0.75 0.78571429 0.62962963 0.66666667] mean value: 0.7153439153439154 key: train_accuracy value: [0.936 0.916 0.92 0.904 0.92 0.916 0.92 0.916 0.9123506 0.90836653] mean value: 0.9168717131474103 key: test_roc_auc value: [0.57142857 0.78571429 0.71428571 0.85714286 0.75 0.64285714 0.75 0.78571429 0.63186813 0.67032967] mean value: 0.7159340659340659 key: train_roc_auc value: [0.936 0.916 0.92 0.904 0.92 0.916 0.92 0.916 0.91231746 0.90838095] mean value: 0.9168698412698412 key: test_jcc value: [0.42857143 0.68421053 0.6 0.76470588 0.63157895 0.47368421 0.5625 0.66666667 0.47368421 0.47058824] mean value: 0.5756190107621996 key: train_jcc value: [0.88059701 0.84671533 0.85074627 0.82608696 0.84962406 0.84782609 0.85294118 0.84558824 0.84057971 0.83211679] mean value: 0.8472821625908681 MCC on Blind test: 0.36 MCC on Training: 0.44 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02518368 0.02483606 0.01727009 0.03318739 0.01711464 0.01720667 0.01741982 0.03403592 0.03433204 0.03445721] mean value: 0.025504350662231445 key: score_time value: [0.01281404 0.01421499 0.01452446 0.01404595 0.01446962 0.01448607 0.0146203 0.02833962 0.02898526 0.02907801] mean value: 0.01855783462524414 key: test_mcc value: [0. 0.53530338 0.1490712 0.4330127 0.28571429 0.35805744 0.09325048 0. 0.33516484 0.18681319] mean value: 0.23763875049595723 key: train_mcc value: [0.64002048 0.56829104 0.59201894 0.58407477 0.6 0.62498074 0.59201894 0.59201894 0.6186856 0.62721305] mean value: 0.6039322510348655 key: test_fscore value: [0.5 0.78787879 0.625 0.73333333 0.64285714 0.66666667 0.31578947 0.5 0.66666667 0.59259259] mean value: 0.60307846636794 key: train_fscore value: [0.82071713 0.78740157 0.79681275 0.79365079 0.8 0.81712062 0.79518072 0.79681275 0.81538462 0.81853282] mean value: 0.8041613777313108 key: test_precision value: [0.5 0.68421053 0.55555556 0.6875 0.64285714 0.69230769 0.6 0.5 0.64285714 0.61538462] mean value: 0.612067267527794 key: train_precision value: [0.81746032 0.7751938 0.79365079 0.78740157 0.8 0.79545455 0.7983871 0.79365079 0.79104478 0.79104478] mean value: 0.7943288472482211 key: test_recall value: [0.5 0.92857143 0.71428571 0.78571429 0.64285714 0.64285714 0.21428571 0.5 0.69230769 0.57142857] mean value: 0.6192307692307693 key: train_recall value: [0.824 0.8 0.8 0.8 0.8 0.84 0.792 0.8 0.84126984 0.848 ] mean value: 0.8145269841269842 key: test_accuracy value: [0.5 0.75 0.57142857 0.71428571 0.64285714 0.67857143 0.53571429 0.5 0.66666667 0.59259259] mean value: 0.6152116402116402 key: train_accuracy value: [0.82 0.784 0.796 0.792 0.8 0.812 0.796 0.796 0.80876494 0.812749 ] mean value: 0.8017513944223108 key: test_roc_auc value: [0.5 0.75 0.57142857 0.71428571 0.64285714 0.67857143 0.53571429 0.5 0.66758242 0.59340659] mean value: 0.6153846153846153 key: train_roc_auc value: [0.82 0.784 0.796 0.792 0.8 0.812 0.796 0.796 0.80863492 0.81288889] mean value: 0.801752380952381 key: test_jcc value: [0.33333333 0.65 0.45454545 0.57894737 0.47368421 0.5 0.1875 0.33333333 0.5 0.42105263] mean value: 0.44323963317384363 key: train_jcc value: [0.69594595 0.64935065 0.66225166 0.65789474 0.66666667 0.69078947 0.66 0.66225166 0.68831169 0.69281046] mean value: 0.6726272929575885 MCC on Blind test: 0.22 MCC on Training: 0.24 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.10959864 0.10714197 0.10356522 0.13085938 0.15054131 0.12836862 0.08568263 0.07235026 0.04970574 0.0825038 ] mean value: 0.1020317554473877 key: score_time value: [0.02261543 0.02406764 0.02002716 0.02046847 0.03379059 0.02086997 0.02123046 0.01255202 0.01242304 0.02138186] mean value: 0.02094266414642334 key: test_mcc value: [0.14285714 0.57735027 0.2981424 0.64450339 0.28571429 0.28571429 0.38235956 0.22941573 0.33516484 0.33149677] mean value: 0.35127186646630326 key: train_mcc value: [0.54401741 0.49614291 0.56001792 0.51201638 0.5769053 0.552637 0.53606862 0.54415674 0.53941077 0.53817331] mean value: 0.5399546365954189 key: test_fscore value: [0.57142857 0.8 0.6875 0.81481481 0.64285714 0.64285714 0.60869565 0.66666667 0.66666667 0.68965517] mean value: 0.6791141829878711 key: train_fscore value: [0.77290837 0.75098814 0.78087649 0.75697211 0.79377432 0.78125 0.76984127 0.77470356 0.77862595 0.77165354] mean value: 0.7731593758129274 key: test_precision value: [0.57142857 0.66666667 0.61111111 0.84615385 0.64285714 0.64285714 0.77777778 0.57894737 0.64285714 0.66666667] mean value: 0.6647323436797122 key: train_precision value: [0.76984127 0.7421875 0.77777778 0.75396825 0.77272727 0.76335878 0.76377953 0.765625 0.75 0.75968992] mean value: 0.7618955302980204 key: test_recall value: [0.57142857 1. 0.78571429 0.78571429 0.64285714 0.64285714 0.5 0.78571429 0.69230769 0.71428571] mean value: 0.712087912087912 key: train_recall value: [0.776 0.76 0.784 0.76 0.816 0.8 0.776 0.784 0.80952381 0.784 ] mean value: 0.784952380952381 key: test_accuracy value: [0.57142857 0.75 0.64285714 0.82142857 0.64285714 0.64285714 0.67857143 0.60714286 0.66666667 0.66666667] mean value: 0.6690476190476191 key: train_accuracy value: [0.772 0.748 0.78 0.756 0.788 0.776 0.768 0.772 0.7689243 0.7689243] mean value: 0.769784860557769 key: test_roc_auc value: [0.57142857 0.75 0.64285714 0.82142857 0.64285714 0.64285714 0.67857143 0.60714286 0.66758242 0.66483516] mean value: 0.668956043956044 key: train_roc_auc value: [0.772 0.748 0.78 0.756 0.788 0.776 0.768 0.772 0.7687619 0.76898413] mean value: 0.7697746031746032 key: test_jcc value: [0.4 0.66666667 0.52380952 0.6875 0.47368421 0.47368421 0.4375 0.5 0.5 0.52631579] mean value: 0.5189160401002506 key: train_jcc value: [0.62987013 0.60126582 0.64052288 0.60897436 0.65806452 0.64102564 0.62580645 0.63225806 0.6375 0.62820513] mean value: 0.6303492988935127 MCC on Blind test: 0.44 MCC on Training: 0.35 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03808546 0.01277423 0.01192927 0.01432085 0.0138979 0.01354623 0.01247215 0.01250887 0.01386213 0.013309 ] mean value: 0.01567060947418213 key: score_time value: [0.01188254 0.00985026 0.00942659 0.0096693 0.00974226 0.00945902 0.01065063 0.00983071 0.01027036 0.01089048] mean value: 0.010167217254638672 key: test_mcc value: [0.14433757 0.41239305 0.21938173 0.72168784 0.28867513 0.28867513 0.36563621 0.14433757 0.18681319 0.25824176] mean value: 0.3030179174049282 key: train_mcc value: [0.60848698 0.57601843 0.58467393 0.53606862 0.6244998 0.58467393 0.584 0.6 0.57310335 0.58565079] mean value: 0.5857175849098157 key: test_fscore value: [0.6 0.74285714 0.64516129 0.86666667 0.66666667 0.66666667 0.64 0.53846154 0.59259259 0.64285714] mean value: 0.6601929707090998 key: train_fscore value: [0.8 0.78884462 0.78688525 0.76612903 0.81568627 0.796875 0.792 0.8 0.79699248 0.792 ] mean value: 0.7935412655386459 key: test_precision value: [0.5625 0.61904762 0.58823529 0.8125 0.625 0.625 0.72727273 0.58333333 0.57142857 0.64285714] mean value: 0.6357174688057041 key: train_precision value: [0.81666667 0.78571429 0.80672269 0.77235772 0.8 0.77862595 0.792 0.8 0.75714286 0.792 ] mean value: 0.7901230176375148 key: test_recall value: [0.64285714 0.92857143 0.71428571 0.92857143 0.71428571 0.71428571 0.57142857 0.5 0.61538462 0.64285714] mean value: 0.6972527472527473 key: train_recall value: [0.784 0.792 0.768 0.76 0.832 0.816 0.792 0.8 0.84126984 0.792 ] mean value: 0.7977269841269841 key: test_accuracy value: [0.57142857 0.67857143 0.60714286 0.85714286 0.64285714 0.64285714 0.67857143 0.57142857 0.59259259 0.62962963] mean value: 0.6472222222222223 key: train_accuracy value: [0.804 0.788 0.792 0.768 0.812 0.792 0.792 0.8 0.78486056 0.79282869] mean value: 0.7925689243027889 key: test_roc_auc value: [0.57142857 0.67857143 0.60714286 0.85714286 0.64285714 0.64285714 0.67857143 0.57142857 0.59340659 0.62912088] mean value: 0.6472527472527473 key: train_roc_auc value: [0.804 0.788 0.792 0.768 0.812 0.792 0.792 0.8 0.78463492 0.7928254 ] mean value: 0.7925460317460319 key: test_jcc value: [0.42857143 0.59090909 0.47619048 0.76470588 0.5 0.5 0.47058824 0.36842105 0.42105263 0.47368421] mean value: 0.49941230080548965 key: train_jcc value: [0.66666667 0.65131579 0.64864865 0.62091503 0.68874172 0.66233766 0.65562914 0.66666667 0.6625 0.65562914] mean value: 0.6579050466473066 MCC on Blind test: 0.26 MCC on Training: 0.3 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01241875 0.01550078 0.01668119 0.0186255 0.01705098 0.01737761 0.02096796 0.01588893 0.01940417 0.01595926] mean value: 0.01698751449584961 key: score_time value: [0.00885892 0.01122165 0.01162767 0.01217222 0.01199245 0.01203942 0.01212955 0.01196456 0.01201367 0.01200461] mean value: 0.011602473258972169 key: test_mcc value: [0.34641016 0.2773501 0.2773501 0.36563621 0.15811388 0.17407766 0. 0.09325048 0.25701934 0.25824176] mean value: 0.22074496876863892 key: train_mcc value: [0.26148818 0.39674602 0.32587527 0.55377492 0.43356607 0.52175886 0.58627206 0.4466845 0.66756084 0.54829712] mean value: 0.47420238569307377 key: test_fscore value: [0.71794872 0.7 0.7 0.64 0.64705882 0.4 0.125 0.64864865 0.58333333 0.64285714] mean value: 0.5804846666317255 key: train_fscore value: [0.69637883 0.73313783 0.71225071 0.76666667 0.74772036 0.67661692 0.74178404 0.75308642 0.82644628 0.78832117] mean value: 0.7442409225264214 key: test_precision value: [0.56 0.53846154 0.53846154 0.72727273 0.55 0.66666667 0.5 0.52173913 0.63636364 0.64285714] mean value: 0.5881822380518034 key: train_precision value: [0.53418803 0.5787037 0.55309735 0.8 0.60294118 0.89473684 0.89772727 0.61306533 0.86206897 0.72483221] mean value: 0.7061360881243113 key: test_recall value: [1. 1. 1. 0.57142857 0.78571429 0.28571429 0.07142857 0.85714286 0.53846154 0.64285714] mean value: 0.6752747252747253 key: train_recall value: [1. 1. 1. 0.736 0.984 0.544 0.632 0.976 0.79365079 0.864 ] mean value: 0.8529650793650794 key: test_accuracy value: [0.60714286 0.57142857 0.57142857 0.67857143 0.57142857 0.57142857 0.5 0.53571429 0.62962963 0.62962963] mean value: 0.5866402116402116 key: train_accuracy value: [0.564 0.636 0.596 0.776 0.668 0.74 0.78 0.68 0.83266932 0.7689243 ] mean value: 0.7041593625498008 key: test_roc_auc value: [0.60714286 0.57142857 0.57142857 0.67857143 0.57142857 0.57142857 0.5 0.53571429 0.62637363 0.62912088] mean value: 0.5862637362637363 key: train_roc_auc value: [0.564 0.636 0.596 0.776 0.668 0.74 0.78 0.68 0.8328254 0.76930159] mean value: 0.7042126984126984 key: test_jcc value: [0.56 0.53846154 0.53846154 0.47058824 0.47826087 0.25 0.06666667 0.48 0.41176471 0.47368421] mean value: 0.4267887764857747 key: train_jcc value: [0.53418803 0.5787037 0.55309735 0.62162162 0.59708738 0.5112782 0.58955224 0.6039604 0.70422535 0.65060241] mean value: 0.5944316675372405 MCC on Blind test: 0.27 MCC on Training: 0.22 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.11272168 0.0763216 0.07966208 0.08366203 0.09074378 0.07549524 0.07483959 0.07829881 0.07256794 0.07145262] mean value: 0.0815765380859375 key: score_time value: [0.01099873 0.01155496 0.01182389 0.01084709 0.01080203 0.01086473 0.01084137 0.01072812 0.01077342 0.01059651] mean value: 0.010983085632324219 key: test_mcc value: [-0.07161149 0.53530338 0.5118907 0.78772636 0.5118907 0.42857143 0.28571429 0.4472136 0.40787852 0.27857101] mean value: 0.4123148488432841 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.78787879 0.77419355 0.88888889 0.77419355 0.71428571 0.64285714 0.75 0.66666667 0.58333333] mean value: 0.7026742075129172 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.46153846 0.68421053 0.70588235 0.92307692 0.70588235 0.71428571 0.64285714 0.66666667 0.72727273 0.7 ] mean value: 0.693167286789578 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.42857143 0.92857143 0.85714286 0.85714286 0.85714286 0.71428571 0.64285714 0.85714286 0.61538462 0.5 ] mean value: 0.7258241758241757 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.46428571 0.75 0.75 0.89285714 0.75 0.71428571 0.64285714 0.71428571 0.7037037 0.62962963] mean value: 0.7011904761904763 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.46428571 0.75 0.75 0.89285714 0.75 0.71428571 0.64285714 0.71428571 0.70054945 0.63461538] mean value: 0.7013736263736264 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.65 0.63157895 0.8 0.63157895 0.55555556 0.47368421 0.6 0.5 0.41176471] mean value: 0.5539876652415352 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.36 MCC on Training: 0.41 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... @??B@?Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... blA0.4Z=oX>TsKy/V1pO|Yht;DW6k@i2%nqd5:-Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.15764785 0.15233874 0.15911317 0.16889334 0.15824723 0.15371776 0.14956284 0.15227222 0.1519618 0.15367222] mean value: 0.15574271678924562 key: score_time value: [0.0152235 0.01572585 0.01761246 0.01639104 0.01561093 0.01534081 0.01508999 0.01672125 0.01529789 0.01579165] mean value: 0.015880537033081055 key: test_mcc value: [0.55629391 0.40201513 0.60302269 0.67131711 0.65743826 0.45514956 0.4 0.56803756 0.50251891 0.61237244] mean value: 0.5428165562870759 key: train_mcc value: [0.91679401 0.87288252 0.88400204 0.85011808 0.85032812 0.88888889 0.88910845 0.90668963 0.9002223 0.86688074] mean value: 0.882591478373277 key: test_fscore value: [0.75675676 0.68421053 0.78947368 0.8 0.8372093 0.7027027 0.7 0.8 0.73684211 0.81818182] mean value: 0.7625376895756333 key: train_fscore value: [0.95798319 0.93484419 0.94050992 0.92436975 0.92394366 0.94444444 0.94505495 0.95156695 0.9494382 0.93258427] mean value: 0.9404739523773479 key: test_precision value: [0.82352941 0.72222222 0.83333333 0.93333333 0.7826087 0.76470588 0.7 0.72 0.77777778 0.75 ] mean value: 0.7807510656436487 key: train_precision value: [0.96610169 0.95375723 0.95953757 0.93220339 0.93714286 0.94444444 0.93478261 0.97660819 0.96022727 0.94318182] mean value: 0.9507987070760173 key: test_recall value: [0.7 0.65 0.75 0.7 0.9 0.65 0.7 0.9 0.7 0.9 ] mean value: 0.7550000000000001 key: train_recall value: [0.95 0.91666667 0.92222222 0.91666667 0.91111111 0.94444444 0.95555556 0.92777778 0.93888889 0.92222222] mean value: 0.9305555555555556 key: test_accuracy value: [0.775 0.7 0.8 0.825 0.825 0.725 0.7 0.775 0.75 0.8 ] mean value: 0.7675 key: train_accuracy value: [0.95833333 0.93611111 0.94166667 0.925 0.925 0.94444444 0.94444444 0.95277778 0.95 0.93333333] mean value: 0.9411111111111111 key: test_roc_auc value: [0.775 0.7 0.8 0.825 0.825 0.725 0.7 0.775 0.75 0.8 ] mean value: 0.7675 key: train_roc_auc value: [0.95833333 0.93611111 0.94166667 0.925 0.925 0.94444444 0.94444444 0.95277778 0.95 0.93333333] mean value: 0.9411111111111111 key: test_jcc value: [0.60869565 0.52 0.65217391 0.66666667 0.72 0.54166667 0.53846154 0.66666667 0.58333333 0.69230769] mean value: 0.6189972129319956 key: train_jcc value: [0.91935484 0.87765957 0.88770053 0.859375 0.85863874 0.89473684 0.89583333 0.9076087 0.90374332 0.87368421] mean value: 0.8878335088517726 MCC on Blind test: 0.35 MCC on Training: 0.54 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.18847132 0.24218154 0.23754025 0.21985197 0.24364138 0.23536777 0.2421608 0.22732234 0.22379589 0.2150588 ] mean value: 0.22753920555114746 key: score_time value: [0.04884648 0.076684 0.03782344 0.06962705 0.04514408 0.05583453 0.04175115 0.03939033 0.03690195 0.04173112] mean value: 0.049373412132263185 key: test_mcc value: [0.65743826 0.464758 0.65081403 0.71443451 0.70352647 0.45514956 0.65081403 0.60302269 0.65743826 0.70352647] mean value: 0.6260922274596256 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.81081081 0.68571429 0.82051282 0.83333333 0.84210526 0.7027027 0.82926829 0.78947368 0.81081081 0.85714286] mean value: 0.7981874861078969 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88235294 0.8 0.84210526 0.9375 0.88888889 0.76470588 0.80952381 0.83333333 0.88235294 0.81818182] mean value: 0.8458944877791627 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.6 0.8 0.75 0.8 0.65 0.85 0.75 0.75 0.9 ] mean value: 0.7600000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.825 0.725 0.825 0.85 0.85 0.725 0.825 0.8 0.825 0.85 ] mean value: 0.8099999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.825 0.725 0.825 0.85 0.85 0.725 0.825 0.8 0.825 0.85 ] mean value: 0.8099999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68181818 0.52173913 0.69565217 0.71428571 0.72727273 0.54166667 0.70833333 0.65217391 0.68181818 0.75 ] mean value: 0.667476002258611 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.63 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03693748 0.02189779 0.02172542 0.02443624 0.02246094 0.02256846 0.02367616 0.02367377 0.02298236 0.02196765] mean value: 0.02423262596130371 key: score_time value: [0.00866437 0.00865269 0.00874186 0.00855374 0.00876784 0.0087285 0.00872731 0.00876069 0.00865126 0.00861645] mean value: 0.008686470985412597 key: test_mcc value: [0.65081403 0.50251891 0.4 0.40201513 0.50251891 0.3 0.35043832 0.4 0.55068879 0.61237244] mean value: 0.46713665174561436 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82926829 0.73684211 0.7 0.68421053 0.73684211 0.65 0.68292683 0.7 0.76923077 0.81818182] mean value: 0.7307502446205911 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.80952381 0.77777778 0.7 0.72222222 0.77777778 0.65 0.66666667 0.7 0.78947368 0.75 ] mean value: 0.7343441938178781 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.85 0.7 0.7 0.65 0.7 0.65 0.7 0.7 0.75 0.9 ] mean value: 0.7300000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.825 0.75 0.7 0.7 0.75 0.65 0.675 0.7 0.775 0.8 ] mean value: 0.7325 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.825 0.75 0.7 0.7 0.75 0.65 0.675 0.7 0.775 0.8 ] mean value: 0.7325 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.70833333 0.58333333 0.53846154 0.52 0.58333333 0.48148148 0.51851852 0.53846154 0.625 0.69230769] mean value: 0.5789230769230769 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.29 MCC on Training: 0.47 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01242256 0.00941133 0.01010966 0.00934935 0.01056123 0.01024294 0.01063728 0.01041222 0.00977182 0.01043153] mean value: 0.010334992408752441 key: score_time value: [0.01210141 0.00852966 0.00846219 0.00913668 0.0088594 0.00855231 0.00883675 0.00901842 0.0090394 0.00891423] mean value: 0.009145045280456543 key: test_mcc value: [0.25286087 0.10206207 0.35400522 0.55629391 0.45056356 0.05057217 0.56803756 0.30151134 0.48038446 0.5 ] mean value: 0.3616291162760345 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.59459459 0.5 0.64864865 0.75675676 0.73170732 0.48648649 0.74285714 0.66666667 0.66666667 0.75 ] mean value: 0.6544384279750133 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.64705882 0.5625 0.70588235 0.82352941 0.71428571 0.52941176 0.86666667 0.63636364 0.84615385 0.75 ] mean value: 0.7081852216411039 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.55 0.45 0.6 0.7 0.75 0.45 0.65 0.7 0.55 0.75] mean value: 0.615 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.625 0.55 0.675 0.775 0.725 0.525 0.775 0.65 0.725 0.75 ] mean value: 0.6775 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.625 0.55 0.675 0.775 0.725 0.525 0.775 0.65 0.725 0.75 ] mean value: 0.6775 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42307692 0.33333333 0.48 0.60869565 0.57692308 0.32142857 0.59090909 0.5 0.5 0.6 ] mean value: 0.49343666478449083 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.36 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11375928 0.11290932 0.11381221 0.11277127 0.11330462 0.11460733 0.11503673 0.1153512 0.11568785 0.11687946] mean value: 0.11441192626953126 key: score_time value: [0.01750302 0.01753736 0.01759648 0.01781797 0.01730275 0.01753926 0.01776409 0.01794052 0.01789689 0.01806712] mean value: 0.017696547508239745 key: test_mcc value: [0.61237244 0.51031036 0.65081403 0.464758 0.8 0.45056356 0.65743826 0.50251891 0.35400522 0.58713656] mean value: 0.5589917330258323 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77777778 0.72222222 0.82051282 0.68571429 0.9 0.73170732 0.8372093 0.76190476 0.64864865 0.80851064] mean value: 0.7694207774477141 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.8125 0.84210526 0.8 0.9 0.71428571 0.7826087 0.72727273 0.70588235 0.7037037 ] mean value: 0.7863358457013391 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.7 0.65 0.8 0.6 0.9 0.75 0.9 0.8 0.6 0.95] mean value: 0.7649999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.75 0.825 0.725 0.9 0.725 0.825 0.75 0.675 0.775] mean value: 0.775 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.75 0.825 0.725 0.9 0.725 0.825 0.75 0.675 0.775] mean value: 0.775 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.63636364 0.56521739 0.69565217 0.52173913 0.81818182 0.57692308 0.72 0.61538462 0.48 0.67857143] mean value: 0.6308033271076751 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.2 MCC on Training: 0.56 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.58466887 0.58668137 0.57328677 0.56745672 0.58909225 0.57865119 0.5803473 0.57368517 0.57621217 0.58946824] mean value: 0.579955005645752 key: score_time value: [0.0099957 0.00967789 0.00979185 0.01029539 0.00993347 0.01039004 0.01009941 0.01023817 0.00916696 0.00914812] mean value: 0.009873700141906739 key: test_mcc value: [0.70352647 0.56803756 0.60302269 0.70352647 0.70352647 0.55068879 0.65081403 0.55629391 0.61237244 0.55068879] mean value: 0.6202497615632169 key: train_mcc value: [1. 1. 1. 1. 1. 0.99445979 1. 0.99445979 0.99445979 1. ] mean value: 0.9983379373493732 key: test_fscore value: [0.84210526 0.74285714 0.78947368 0.84210526 0.84210526 0.76923077 0.82926829 0.79069767 0.77777778 0.7804878 ] mean value: 0.8006108935529481 key: train_fscore value: [1. 1. 1. 1. 1. 0.99721448 1. 0.99721448 0.99721448 1. ] mean value: 0.9991643454038996 key: test_precision value: [0.88888889 0.86666667 0.83333333 0.88888889 0.88888889 0.78947368 0.80952381 0.73913043 0.875 0.76190476] mean value: 0.8341699357088374 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.65 0.75 0.8 0.8 0.75 0.85 0.85 0.7 0.8 ] mean value: 0.775 key: train_recall value: [1. 1. 1. 1. 1. 0.99444444 1. 0.99444444 0.99444444 1. ] mean value: 0.9983333333333334 key: test_accuracy value: [0.85 0.775 0.8 0.85 0.85 0.775 0.825 0.775 0.8 0.775] mean value: 0.8074999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 0.99722222 1. 0.99722222 0.99722222 1. ] mean value: 0.9991666666666668 key: test_roc_auc value: [0.85 0.775 0.8 0.85 0.85 0.775 0.825 0.775 0.8 0.775] mean value: 0.8075000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 0.99722222 1. 0.99722222 0.99722222 1. ] mean value: 0.9991666666666668 key: test_jcc value: [0.72727273 0.59090909 0.65217391 0.72727273 0.72727273 0.625 0.70833333 0.65384615 0.63636364 0.64 ] mean value: 0.6688444309313876 key: train_jcc value: [1. 1. 1. 1. 1. 0.99444444 1. 0.99444444 0.99444444 1. ] mean value: 0.9983333333333334 MCC on Blind test: 0.45 MCC on Training: 0.62 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01139641 0.00988293 0.01090026 0.01062179 0.0107367 0.0103364 0.00980043 0.01008844 0.01031709 0.01081729] mean value: 0.010489773750305176 key: score_time value: [0.0097487 0.00890708 0.00887775 0.00864553 0.0091145 0.0092392 0.00934124 0.00955558 0.00923324 0.00883961] mean value: 0.009150242805480957 key: test_mcc value: [0.35400522 0.20100756 0.25819889 0.25031309 0.45056356 0.4 0.35043832 0.20412415 0.30618622 0.26688026] mean value: 0.30417172544932597 key: train_mcc value: [0.32863599 0.37291922 0.30702591 0.33952347 0.36812387 0.3393133 0.34497723 0.35663591 0.40022241 0.35026496] mean value: 0.3507642250925763 key: test_fscore value: [0.69767442 0.57894737 0.66666667 0.63414634 0.71794872 0.7 0.66666667 0.63636364 0.68181818 0.68085106] mean value: 0.6661083061782775 key: train_fscore value: [0.67560322 0.69541779 0.69099757 0.67924528 0.69680851 0.67750678 0.68108108 0.68983957 0.70491803 0.68119891] mean value: 0.6872616738692704 key: test_precision value: [0.65217391 0.61111111 0.6 0.61904762 0.73684211 0.7 0.68421053 0.58333333 0.625 0.59259259] mean value: 0.6404311200707082 key: train_precision value: [0.65284974 0.67539267 0.61471861 0.65968586 0.66836735 0.66137566 0.66315789 0.66494845 0.69354839 0.6684492 ] mean value: 0.6622493831299934 key: test_recall value: [0.75 0.55 0.75 0.65 0.7 0.7 0.65 0.7 0.75 0.8 ] mean value: 0.7 key: train_recall value: [0.7 0.71666667 0.78888889 0.7 0.72777778 0.69444444 0.7 0.71666667 0.71666667 0.69444444] mean value: 0.7155555555555555 key: test_accuracy value: [0.675 0.6 0.625 0.625 0.725 0.7 0.675 0.6 0.65 0.625] mean value: 0.65 key: train_accuracy value: [0.66388889 0.68611111 0.64722222 0.66944444 0.68333333 0.66944444 0.67222222 0.67777778 0.7 0.675 ] mean value: 0.6744444444444445 key: test_roc_auc value: [0.675 0.6 0.625 0.625 0.725 0.7 0.675 0.6 0.65 0.625] mean value: 0.65 key: train_roc_auc value: [0.66388889 0.68611111 0.64722222 0.66944444 0.68333333 0.66944444 0.67222222 0.67777778 0.7 0.675 ] mean value: 0.6744444444444444 key: test_jcc value: [0.53571429 0.40740741 0.5 0.46428571 0.56 0.53846154 0.5 0.46666667 0.51724138 0.51612903] mean value: 0.5005906024104021 key: train_jcc value: [0.51012146 0.53305785 0.52788104 0.51428571 0.53469388 0.51229508 0.51639344 0.52653061 0.5443038 0.51652893] mean value: 0.5236091801381727 MCC on Blind test: 0.24 MCC on Training: 0.3 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.0636878 0.08921671 0.12308002 0.10774374 0.11540103 0.11087155 0.12070131 0.12148762 0.12078977 0.14447999] mean value: 0.11174595355987549 key: score_time value: [0.01371312 0.0225718 0.02200818 0.02782011 0.02207804 0.02229714 0.0224061 0.02223897 0.0220449 0.02771449] mean value: 0.022489285469055174 key: test_mcc value: [0.54554473 0. 0.60302269 0.41931393 0.40201513 0.35043832 0.6 0.45056356 0.40824829 0.25819889] mean value: 0.4037345534663799 key: train_mcc value: [0.90668963 0.90624109 0.92227915 0.91133616 0.90050042 0.92813592 0.93901932 0.90590512 0.90568135 0.9 ] mean value: 0.9125788166695756 key: test_fscore value: [0.6875 0.44444444 0.78947368 0.64705882 0.68421053 0.68292683 0.8 0.71794872 0.66666667 0.66666667] mean value: 0.6786896359050516 key: train_fscore value: [0.95156695 0.95184136 0.96089385 0.95505618 0.94915254 0.96338028 0.96918768 0.95211268 0.95238095 0.95 ] mean value: 0.9555572473434546 key: test_precision value: [0.91666667 0.5 0.83333333 0.78571429 0.72222222 0.66666667 0.8 0.73684211 0.75 0.6 ] mean value: 0.7311445279866332 key: train_precision value: [0.97660819 0.97109827 0.96629213 0.96590909 0.96551724 0.97714286 0.97740113 0.96571429 0.96045198 0.95 ] mean value: 0.9676135170352094 key: test_recall value: [0.55 0.4 0.75 0.55 0.65 0.7 0.8 0.7 0.6 0.75] mean value: 0.6449999999999999 key: train_recall value: [0.92777778 0.93333333 0.95555556 0.94444444 0.93333333 0.95 0.96111111 0.93888889 0.94444444 0.95 ] mean value: 0.9438888888888888 key: test_accuracy value: [0.75 0.5 0.8 0.7 0.7 0.675 0.8 0.725 0.7 0.625] mean value: 0.6975 key: train_accuracy value: [0.95277778 0.95277778 0.96111111 0.95555556 0.95 0.96388889 0.96944444 0.95277778 0.95277778 0.95 ] mean value: 0.956111111111111 key: test_roc_auc value: [0.75 0.5 0.8 0.7 0.7 0.675 0.8 0.725 0.7 0.625] mean value: 0.6975 key: train_roc_auc value: [0.95277778 0.95277778 0.96111111 0.95555556 0.95 0.96388889 0.96944444 0.95277778 0.95277778 0.95 ] mean value: 0.956111111111111 key: test_jcc value: [0.52380952 0.28571429 0.65217391 0.47826087 0.52 0.51851852 0.66666667 0.56 0.5 0.5 ] mean value: 0.5205143777317691 key: train_jcc value: [0.9076087 0.90810811 0.92473118 0.91397849 0.90322581 0.92934783 0.94021739 0.90860215 0.90909091 0.9047619 ] mean value: 0.9149672469413002 MCC on Blind test: 0.16 MCC on Training: 0.4 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02024055 0.00988913 0.00877857 0.01014876 0.00999212 0.00957346 0.00963378 0.00987172 0.00981402 0.010144 ] mean value: 0.010808610916137695 key: score_time value: [0.02568817 0.01129723 0.01123953 0.01206899 0.01156902 0.01231432 0.01168394 0.01303697 0.01248503 0.01356196] mean value: 0.013494515419006347 key: test_mcc value: [ 0.21821789 -0.15171652 0.40201513 0.10206207 0.10050378 0.2 0.45514956 0.05057217 -0.05006262 0.10482848] mean value: 0.14315699529185016 key: train_mcc value: [0.52251259 0.52302999 0.50003087 0.54444444 0.54444444 0.52222222 0.51114266 0.48888889 0.51123736 0.52225446] mean value: 0.5190207929152265 key: test_fscore value: [0.5 0.37837838 0.68421053 0.5 0.57142857 0.6 0.74418605 0.55813953 0.46153846 0.60869565] mean value: 0.5606577171230462 key: train_fscore value: [0.75706215 0.75428571 0.74860335 0.77222222 0.77222222 0.76111111 0.75418994 0.74444444 0.75280899 0.75977654] mean value: 0.7576726682344649 key: test_precision value: [0.66666667 0.41176471 0.72222222 0.5625 0.54545455 0.6 0.69565217 0.52173913 0.47368421 0.53846154] mean value: 0.5738145193561468 key: train_precision value: [0.77011494 0.77647059 0.75280899 0.77222222 0.77222222 0.76111111 0.75842697 0.74444444 0.76136364 0.76404494] mean value: 0.7633230066004071 key: test_recall value: [0.4 0.35 0.65 0.45 0.6 0.6 0.8 0.6 0.45 0.7 ] mean value: 0.5599999999999999 key: train_recall value: [0.74444444 0.73333333 0.74444444 0.77222222 0.77222222 0.76111111 0.75 0.74444444 0.74444444 0.75555556] mean value: 0.7522222222222222 key: test_accuracy value: [0.6 0.425 0.7 0.55 0.55 0.6 0.725 0.525 0.475 0.55 ] mean value: 0.57 key: train_accuracy value: [0.76111111 0.76111111 0.75 0.77222222 0.77222222 0.76111111 0.75555556 0.74444444 0.75555556 0.76111111] mean value: 0.7594444444444444 key: test_roc_auc value: [0.6 0.425 0.7 0.55 0.55 0.6 0.725 0.525 0.475 0.55 ] mean value: 0.57 key: train_roc_auc value: [0.76111111 0.76111111 0.75 0.77222222 0.77222222 0.76111111 0.75555556 0.74444444 0.75555556 0.76111111] mean value: 0.7594444444444444 key: test_jcc value: [0.33333333 0.23333333 0.52 0.33333333 0.4 0.42857143 0.59259259 0.38709677 0.3 0.4375 ] mean value: 0.3965760795357569 key: train_jcc value: [0.60909091 0.60550459 0.59821429 0.62895928 0.62895928 0.61434978 0.60538117 0.59292035 0.6036036 0.61261261] mean value: 0.609959584589991 MCC on Blind test: 0.13 MCC on Training: 0.14 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02986217 0.04133296 0.04973912 0.06988502 0.06855345 0.06958771 0.07420182 0.06967735 0.0714922 0.06431341] mean value: 0.06086452007293701 key: score_time value: [0.01213598 0.01236892 0.02372622 0.03108072 0.02218246 0.01627469 0.02237892 0.01776695 0.02495909 0.02131605] mean value: 0.020419001579284668 key: test_mcc value: [0.45514956 0.25286087 0.45056356 0.26688026 0.25031309 0.30151134 0.35043832 0.35043832 0.15171652 0.30618622] mean value: 0.31360580604894933 key: train_mcc value: [0.76206429 0.73917412 0.75010419 0.77796989 0.75597566 0.7578513 0.75555556 0.73944825 0.75001157 0.70556644] mean value: 0.7493721270766465 key: test_fscore value: [0.7027027 0.59459459 0.73170732 0.54545455 0.61538462 0.66666667 0.68292683 0.66666667 0.60465116 0.68181818] mean value: 0.6492573282420134 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.87749288 0.86760563 0.87394958 0.88764045 0.87570621 0.87283237 0.87777778 0.86685552 0.87465181 0.85236769] mean value: 0.8726879925661633 key: test_precision value: [0.76470588 0.64705882 0.71428571 0.69230769 0.63157895 0.63636364 0.66666667 0.68421053 0.56521739 0.625 ] mean value: 0.662739528049462 key: train_precision value: [0.9005848 0.88 0.88135593 0.89772727 0.8908046 0.90963855 0.87777778 0.88439306 0.87709497 0.8547486 ] mean value: 0.8854125568950904 key: test_recall value: [0.65 0.55 0.75 0.45 0.6 0.7 0.7 0.65 0.65 0.75] mean value: 0.6450000000000001 key: train_recall value: [0.85555556 0.85555556 0.86666667 0.87777778 0.86111111 0.83888889 0.87777778 0.85 0.87222222 0.85 ] mean value: 0.8605555555555556 key: test_accuracy value: [0.725 0.625 0.725 0.625 0.625 0.65 0.675 0.675 0.575 0.65 ] mean value: 0.655 key: train_accuracy value: [0.88055556 0.86944444 0.875 0.88888889 0.87777778 0.87777778 0.87777778 0.86944444 0.875 0.85277778] mean value: 0.8744444444444444 key: test_roc_auc value: [0.725 0.625 0.725 0.625 0.625 0.65 0.675 0.675 0.575 0.65 ] mean value: 0.655 key: train_roc_auc value: [0.88055556 0.86944444 0.875 0.88888889 0.87777778 0.87777778 0.87777778 0.86944444 0.875 0.85277778] mean value: 0.8744444444444444 key: test_jcc value: [0.54166667 0.42307692 0.57692308 0.375 0.44444444 0.5 0.51851852 0.5 0.43333333 0.51724138] mean value: 0.4830204342273309 key: train_jcc value: [0.78172589 0.76616915 0.7761194 0.7979798 0.77889447 0.77435897 0.78217822 0.765 0.77722772 0.74271845] mean value: 0.7742372077435387 MCC on Blind test: 0.15 MCC on Training: 0.31 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.06712818 0.03658915 0.03660822 0.03656101 0.03723001 0.04999566 0.03760552 0.03825212 0.03968191 0.04618239] mean value: 0.04258341789245605 key: score_time value: [0.01342082 0.01266599 0.01333094 0.01329613 0.01344419 0.01322675 0.01261997 0.01274204 0.01273513 0.01350021] mean value: 0.01309821605682373 key: test_mcc value: [0.45514956 0.25286087 0.55068879 0.41931393 0.50251891 0.51031036 0.4 0.55629391 0.25031309 0.41931393] mean value: 0.4316763362562218 key: train_mcc value: [0.6001482 0.5951889 0.62825303 0.63395978 0.62856397 0.6001482 0.59467392 0.61111111 0.61881909 0.60003704] mean value: 0.6110903232368619 key: test_fscore value: [0.74418605 0.59459459 0.7804878 0.64705882 0.73684211 0.77272727 0.7 0.79069767 0.63414634 0.73913043] mean value: 0.713987109816874 key: train_fscore value: [0.8021978 0.80216802 0.81743869 0.82065217 0.81842818 0.8021978 0.8 0.80555556 0.816 0.80110497] mean value: 0.8085743204300047 key: test_precision value: [0.69565217 0.64705882 0.76190476 0.78571429 0.77777778 0.70833333 0.7 0.73913043 0.61904762 0.65384615] mean value: 0.7088465363848996 key: train_precision value: [0.79347826 0.78306878 0.80213904 0.80319149 0.7989418 0.79347826 0.78918919 0.80555556 0.78461538 0.7967033 ] mean value: 0.7950361056607995 key: test_recall value: [0.8 0.55 0.8 0.55 0.7 0.85 0.7 0.85 0.65 0.85] mean value: 0.73 key: train_recall value: [0.81111111 0.82222222 0.83333333 0.83888889 0.83888889 0.81111111 0.81111111 0.80555556 0.85 0.80555556] mean value: 0.8227777777777778 key: test_accuracy value: [0.725 0.625 0.775 0.7 0.75 0.75 0.7 0.775 0.625 0.7 ] mean value: 0.7125000000000001 key: train_accuracy value: [0.8 0.79722222 0.81388889 0.81666667 0.81388889 0.8 0.79722222 0.80555556 0.80833333 0.8 ] mean value: 0.8052777777777779 key: test_roc_auc value: [0.725 0.625 0.775 0.7 0.75 0.75 0.7 0.775 0.625 0.7 ] mean value: 0.7125 key: train_roc_auc value: [0.8 0.79722222 0.81388889 0.81666667 0.81388889 0.8 0.79722222 0.80555556 0.80833333 0.8 ] mean value: 0.8052777777777779 key: test_jcc value: [0.59259259 0.42307692 0.64 0.47826087 0.58333333 0.62962963 0.53846154 0.65384615 0.46428571 0.5862069 ] mean value: 0.5589693651342827 key: train_jcc value: [0.66972477 0.66968326 0.69124424 0.69585253 0.69266055 0.66972477 0.66666667 0.6744186 0.68918919 0.66820276] mean value: 0.6787367349339196 MCC on Blind test: 0.46 MCC on Training: 0.43 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.5212841 0.55255389 0.63335919 0.50119567 0.52335238 0.51176476 0.61742282 0.51516509 0.54698634 0.52821016] mean value: 0.5451294422149658 key: score_time value: [0.01203442 0.01534677 0.01470399 0.01195908 0.0147593 0.01461744 0.01473427 0.01463246 0.01470113 0.01469851] mean value: 0.014218735694885253 key: test_mcc value: [0.50251891 0.45056356 0.5 0.20965697 0.45056356 0.464758 0.35043832 0.35043832 0.25031309 0.40824829] mean value: 0.39374990119732506 key: train_mcc value: [0.65001003 0.80044482 0.67306408 0.65620398 0.70565357 0.64448423 0.6500903 0.77821024 0.66703735 0.73351447] mean value: 0.695871305203189 key: test_fscore value: [0.76190476 0.71794872 0.75 0.52941176 0.71794872 0.75555556 0.68292683 0.66666667 0.63414634 0.72727273] mean value: 0.6943782082734737 key: train_fscore value: [0.82548476 0.89830508 0.8401084 0.83152174 0.85399449 0.82122905 0.82644628 0.88700565 0.83606557 0.86516854] mean value: 0.8485329573946186 key: test_precision value: [0.72727273 0.73684211 0.75 0.64285714 0.73684211 0.68 0.66666667 0.68421053 0.61904762 0.66666667] mean value: 0.6910405559352928 key: train_precision value: [0.82320442 0.9137931 0.82010582 0.81382979 0.84699454 0.8258427 0.81967213 0.90229885 0.82258065 0.875 ] mean value: 0.8463321989709524 key: test_recall value: [0.8 0.7 0.75 0.45 0.7 0.85 0.7 0.65 0.65 0.8 ] mean value: 0.705 key: train_recall value: [0.82777778 0.88333333 0.86111111 0.85 0.86111111 0.81666667 0.83333333 0.87222222 0.85 0.85555556] mean value: 0.8511111111111112 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.75 0.725 0.75 0.6 0.725 0.725 0.675 0.675 0.625 0.7 ] mean value: 0.6950000000000001 key: train_accuracy value: [0.825 0.9 0.83611111 0.82777778 0.85277778 0.82222222 0.825 0.88888889 0.83333333 0.86666667] mean value: 0.8477777777777776 key: test_roc_auc value: [0.75 0.725 0.75 0.6 0.725 0.725 0.675 0.675 0.625 0.7 ] mean value: 0.6950000000000001 key: train_roc_auc value: [0.825 0.9 0.83611111 0.82777778 0.85277778 0.82222222 0.825 0.88888889 0.83333333 0.86666667] mean value: 0.8477777777777777 key: test_jcc value: [0.61538462 0.56 0.6 0.36 0.56 0.60714286 0.51851852 0.5 0.46428571 0.57142857] mean value: 0.5356760276760277 key: train_jcc value: [0.70283019 0.81538462 0.72429907 0.71162791 0.74519231 0.69668246 0.70422535 0.79695431 0.71830986 0.76237624] mean value: 0.7377882312220629 MCC on Blind test: 0.44 MCC on Training: 0.39 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.50233197 1.41186833 1.56785917 1.61239552 1.38103843 1.52702236 1.61019516 1.39374781 1.54995918 1.51594973] mean value: 1.5072367668151856 key: score_time value: [0.01240253 0.01243758 0.01233935 0.01530075 0.01234269 0.0151813 0.01486135 0.01565146 0.01522994 0.01228261] mean value: 0.01380295753479004 key: test_mcc value: [0.60302269 0.41931393 0.7 0.48038446 0.65081403 0.55068879 0.35043832 0.45514956 0.30151134 0.40824829] mean value: 0.4919571424380392 key: train_mcc value: [0.8849874 0.92813592 0.87864601 0.90568135 0.90089021 0.88894376 0.91702052 0.88400204 0.89512157 0.91679401] mean value: 0.9000222799589072 key: test_fscore value: [0.78947368 0.64705882 0.85 0.66666667 0.82051282 0.7804878 0.66666667 0.7027027 0.63157895 0.72727273] mean value: 0.7282420843807992 key: train_fscore value: [0.93982808 0.96338028 0.9375 0.95238095 0.94886364 0.94413408 0.95774648 0.94050992 0.94617564 0.95867769] mean value: 0.9489196746107831 key: test_precision value: [0.83333333 0.78571429 0.85 0.84615385 0.84210526 0.76190476 0.68421053 0.76470588 0.66666667 0.66666667] mean value: 0.7701461232266186 key: train_precision value: [0.9704142 0.97714286 0.95930233 0.96045198 0.97093023 0.9494382 0.97142857 0.95953757 0.96531792 0.95081967] mean value: 0.9634783531003344 key: test_recall value: [0.75 0.55 0.85 0.55 0.8 0.8 0.65 0.65 0.6 0.8 ] mean value: 0.7 key: train_recall value: [0.91111111 0.95 0.91666667 0.94444444 0.92777778 0.93888889 0.94444444 0.92222222 0.92777778 0.96666667] mean value: 0.9349999999999999 key: test_accuracy value: [0.8 0.7 0.85 0.725 0.825 0.775 0.675 0.725 0.65 0.7 ] mean value: 0.7425 key: train_accuracy value: [0.94166667 0.96388889 0.93888889 0.95277778 0.95 0.94444444 0.95833333 0.94166667 0.94722222 0.95833333] mean value: 0.9497222222222224 key: test_roc_auc value: [0.8 0.7 0.85 0.725 0.825 0.775 0.675 0.725 0.65 0.7 ] mean value: 0.7424999999999999 key: train_roc_auc value: [0.94166667 0.96388889 0.93888889 0.95277778 0.95 0.94444444 0.95833333 0.94166667 0.94722222 0.95833333] mean value: 0.9497222222222224 key: test_jcc value: [0.65217391 0.47826087 0.73913043 0.5 0.69565217 0.64 0.5 0.54166667 0.46153846 0.57142857] mean value: 0.5779851090938047 key: train_jcc value: [0.88648649 0.92934783 0.88235294 0.90909091 0.9027027 0.89417989 0.91891892 0.88770053 0.89784946 0.92063492] mean value: 0.9029264596402209 MCC on Blind test: 0.24 MCC on Training: 0.49 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01391649 0.01248479 0.00997925 0.01060843 0.00953817 0.00967121 0.00953913 0.00975847 0.00945473 0.00954247] mean value: 0.01044931411743164 key: score_time value: [0.01271582 0.00987029 0.009166 0.01010704 0.00900269 0.00937533 0.00887442 0.00892925 0.00882363 0.00878596] mean value: 0.009565043449401855 key: test_mcc value: [0.30151134 0.10050378 0.40201513 0.15171652 0.45514956 0.40201513 0.35400522 0.05163978 0.15491933 0.1796053 ] mean value: 0.2553081093241488 key: train_mcc value: [0.29244883 0.33152869 0.31304952 0.30905217 0.27098325 0.27447212 0.26290647 0.31019038 0.2983419 0.32563721] mean value: 0.2988610537616342 key: test_fscore value: [0.66666667 0.52631579 0.71428571 0.60465116 0.7027027 0.71428571 0.69767442 0.57777778 0.62222222 0.66666667] mean value: 0.6493248835476496 key: train_fscore value: [0.67010309 0.6873385 0.67368421 0.67700258 0.66326531 0.65796345 0.65091864 0.68030691 0.67352185 0.68393782] mean value: 0.671804235645417 key: test_precision value: [0.63636364 0.55555556 0.68181818 0.56521739 0.76470588 0.68181818 0.65217391 0.52 0.56 0.5483871 ] mean value: 0.6166039839030516 key: train_precision value: [0.625 0.64251208 0.64 0.63285024 0.61320755 0.62068966 0.61691542 0.63033175 0.62679426 0.6407767 ] mean value: 0.628907765502521 key: test_recall value: [0.7 0.5 0.75 0.65 0.65 0.75 0.75 0.65 0.7 0.85] mean value: 0.695 key: train_recall value: [0.72222222 0.73888889 0.71111111 0.72777778 0.72222222 0.7 0.68888889 0.73888889 0.72777778 0.73333333] mean value: 0.7211111111111111 key: test_accuracy value: [0.65 0.55 0.7 0.575 0.725 0.7 0.675 0.525 0.575 0.575] mean value: 0.625 key: train_accuracy value: [0.64444444 0.66388889 0.65555556 0.65277778 0.63333333 0.63611111 0.63055556 0.65277778 0.64722222 0.66111111] mean value: 0.6477777777777778 key: test_roc_auc value: [0.65 0.55 0.7 0.575 0.725 0.7 0.675 0.525 0.575 0.575] mean value: 0.625 key: train_roc_auc value: [0.64444444 0.66388889 0.65555556 0.65277778 0.63333333 0.63611111 0.63055556 0.65277778 0.64722222 0.66111111] mean value: 0.6477777777777778 key: test_jcc value: [0.5 0.35714286 0.55555556 0.43333333 0.54166667 0.55555556 0.53571429 0.40625 0.4516129 0.5 ] mean value: 0.48368311571940603 key: train_jcc value: [0.50387597 0.52362205 0.50793651 0.51171875 0.49618321 0.49027237 0.48249027 0.51550388 0.50775194 0.51968504] mean value: 0.5059039979517684 MCC on Blind test: 0.21 MCC on Training: 0.26 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01023173 0.00992513 0.01066232 0.00962949 0.00974727 0.0098052 0.0100441 0.01044488 0.00972342 0.01015401] mean value: 0.010036754608154296 key: score_time value: [0.00943971 0.00946641 0.00895858 0.00907207 0.00885415 0.00974631 0.00918221 0.0087471 0.00908518 0.00913858] mean value: 0.00916903018951416 key: test_mcc value: [0.41931393 0.36147845 0.27994626 0.31448545 0.48038446 0.40201513 0.35043832 0.35043832 0.3 0.30618622] mean value: 0.3564686536246601 key: train_mcc value: [0.44190661 0.45775864 0.43932629 0.41546816 0.43344012 0.44742872 0.43660668 0.42889782 0.43603326 0.44251627] mean value: 0.43793825667096326 key: test_fscore value: [0.64705882 0.62857143 0.51612903 0.58823529 0.66666667 0.68421053 0.68292683 0.68292683 0.65 0.68181818] mean value: 0.6428543611813776 key: train_fscore value: [0.7020649 0.69724771 0.67711599 0.68263473 0.68882175 0.68923077 0.69822485 0.70317003 0.7 0.70029674] mean value: 0.6938807459468042 key: test_precision value: [0.78571429 0.73333333 0.72727273 0.71428571 0.84615385 0.72222222 0.66666667 0.66666667 0.65 0.625 ] mean value: 0.7137315462315462 key: train_precision value: [0.74842767 0.7755102 0.77697842 0.74025974 0.75496689 0.77241379 0.74683544 0.73053892 0.74375 0.75159236] mean value: 0.7541273436965764 key: test_recall value: [0.55 0.55 0.4 0.5 0.55 0.65 0.7 0.7 0.65 0.75] mean value: 0.6 key: train_recall value: [0.66111111 0.63333333 0.6 0.63333333 0.63333333 0.62222222 0.65555556 0.67777778 0.66111111 0.65555556] mean value: 0.6433333333333334 key: test_accuracy value: [0.7 0.675 0.625 0.65 0.725 0.7 0.675 0.675 0.65 0.65 ] mean value: 0.6725000000000001 key: train_accuracy value: [0.71944444 0.725 0.71388889 0.70555556 0.71388889 0.71944444 0.71666667 0.71388889 0.71666667 0.71944444] mean value: 0.7163888888888889 key: test_roc_auc value: [0.7 0.675 0.625 0.65 0.725 0.7 0.675 0.675 0.65 0.65 ] mean value: 0.6725000000000001 key: train_roc_auc value: [0.71944444 0.725 0.71388889 0.70555556 0.71388889 0.71944444 0.71666667 0.71388889 0.71666667 0.71944444] mean value: 0.7163888888888889 key: test_jcc value: [0.47826087 0.45833333 0.34782609 0.41666667 0.5 0.52 0.51851852 0.51851852 0.48148148 0.51724138] mean value: 0.4756846854350603 key: train_jcc value: [0.54090909 0.53521127 0.51184834 0.51818182 0.52534562 0.5258216 0.53636364 0.54222222 0.53846154 0.53881279] mean value: 0.5313177918728241 MCC on Blind test: 0.24 MCC on Training: 0.36 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01282263 0.0153296 0.01739407 0.01774645 0.01536393 0.01602697 0.01743293 0.01649237 0.01844716 0.01539874] mean value: 0.016245484352111816 key: score_time value: [0.00911331 0.01191974 0.0119946 0.0121069 0.01196671 0.01198554 0.01205373 0.01195002 0.01227689 0.01202059] mean value: 0.011738801002502441 key: test_mcc value: [0.54554473 0.28005602 0.55068879 0.26688026 0.22941573 0.25286087 0.16796775 0.28005602 0.10482848 0.32897585] mean value: 0.30072744943184126 key: train_mcc value: [0.47326627 0.45827033 0.57906602 0.49461052 0.34229069 0.55144005 0.45506157 0.23604761 0.57551331 0.4108907 ] mean value: 0.457645707496826 key: test_fscore value: [0.79166667 0.38461538 0.76923077 0.68085106 0.18181818 0.65116279 0.4516129 0.38461538 0.60869565 0.44444444] mean value: 0.5348713241318013 key: train_fscore value: [0.76430206 0.55686275 0.7816092 0.77200903 0.41880342 0.76657061 0.57794677 0.19095477 0.80387409 0.44827586] mean value: 0.608120854934185 key: test_precision value: [0.67857143 0.83333333 0.78947368 0.59259259 1. 0.60869565 0.63636364 0.83333333 0.53846154 0.85714286] mean value: 0.7367968056183158 key: train_precision value: [0.64980545 0.94666667 0.80952381 0.65019011 0.90740741 0.79640719 0.91566265 1. 0.71244635 1. ] mean value: 0.8388109633299624 key: test_recall value: [0.95 0.25 0.75 0.8 0.1 0.7 0.35 0.25 0.7 0.3 ] mean value: 0.515 key: train_recall value: [0.92777778 0.39444444 0.75555556 0.95 0.27222222 0.73888889 0.42222222 0.10555556 0.92222222 0.28888889] mean value: 0.5777777777777777 key: test_accuracy value: [0.75 0.6 0.775 0.625 0.55 0.625 0.575 0.6 0.55 0.625] mean value: 0.6275 key: train_accuracy value: [0.71388889 0.68611111 0.78888889 0.71944444 0.62222222 0.775 0.69166667 0.55277778 0.775 0.64444444] mean value: 0.6969444444444445 key: test_roc_auc value: [0.75 0.6 0.775 0.625 0.55 0.625 0.575 0.6 0.55 0.625] mean value: 0.6275 key: train_roc_auc value: [0.71388889 0.68611111 0.78888889 0.71944444 0.62222222 0.775 0.69166667 0.55277778 0.775 0.64444444] mean value: 0.6969444444444445 key: test_jcc value: [0.65517241 0.23809524 0.625 0.51612903 0.1 0.48275862 0.29166667 0.23809524 0.4375 0.28571429] mean value: 0.3870131495312252 key: train_jcc value: [0.61851852 0.38586957 0.64150943 0.62867647 0.26486486 0.62149533 0.40641711 0.10555556 0.67206478 0.28888889] mean value: 0.46338605143259226 MCC on Blind test: 0.3 MCC on Training: 0.3 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02913284 0.02746439 0.02936268 0.02987266 0.03059578 0.02934861 0.02993393 0.03252649 0.03724647 0.04961872] mean value: 0.03251025676727295 key: score_time value: [0.01252866 0.01456547 0.01305914 0.01303244 0.01299238 0.01307678 0.01251817 0.01251388 0.01309037 0.01328564] mean value: 0.013066291809082031 key: test_mcc value: [0.54554473 0.15171652 0.45056356 0.20100756 0.40201513 0.16796775 0.40824829 0.40824829 0.35043832 0.10050378] mean value: 0.3186253930627978 key: train_mcc value: [0.84848869 0.83553169 0.83587914 0.80836728 0.90871632 0.94006471 0.65032676 0.76785254 0.84465303 0.69828598] mean value: 0.8138166134893426 key: test_fscore value: [0.6875 0.54054054 0.73170732 0.61904762 0.71428571 0.65306122 0.66666667 0.72727273 0.68292683 0.52631579] mean value: 0.654932442811821 key: train_fscore value: [0.91291291 0.90243902 0.91948052 0.90666667 0.95072464 0.9701897 0.7456446 0.88648649 0.92134831 0.79194631] mean value: 0.8907839172149717 key: test_precision value: [0.91666667 0.58823529 0.71428571 0.59090909 0.68181818 0.55172414 0.75 0.66666667 0.66666667 0.55555556] mean value: 0.6682527974617224 key: train_precision value: [0.99346405 1. 0.86341463 0.87179487 0.99393939 0.94708995 1. 0.86315789 0.93181818 1. ] mean value: 0.9464678975813159 key: test_recall value: [0.55 0.5 0.75 0.65 0.75 0.8 0.6 0.8 0.7 0.5 ] mean value: 0.66 key: train_recall value: [0.84444444 0.82222222 0.98333333 0.94444444 0.91111111 0.99444444 0.59444444 0.91111111 0.91111111 0.65555556] mean value: 0.8572222222222221 key: test_accuracy value: [0.75 0.575 0.725 0.6 0.7 0.575 0.7 0.7 0.675 0.55 ] mean value: 0.6549999999999999 key: train_accuracy value: [0.91944444 0.91111111 0.91388889 0.90277778 0.95277778 0.96944444 0.79722222 0.88333333 0.92222222 0.82777778] mean value: 0.9 key: test_roc_auc value: [0.75 0.575 0.725 0.6 0.7 0.575 0.7 0.7 0.675 0.55 ] mean value: 0.655 key: train_roc_auc value: [0.91944444 0.91111111 0.91388889 0.90277778 0.95277778 0.96944444 0.79722222 0.88333333 0.92222222 0.82777778] mean value: 0.9 key: test_jcc value: [0.52380952 0.37037037 0.57692308 0.44827586 0.55555556 0.48484848 0.5 0.57142857 0.51851852 0.35714286] mean value: 0.49068728206659235 key: train_jcc value: [0.83977901 0.82222222 0.85096154 0.82926829 0.90607735 0.94210526 0.59444444 0.7961165 0.85416667 0.65555556] mean value: 0.8090696841636777 MCC on Blind test: 0.06 MCC on Training: 0.32 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.65748262 0.66617322 0.66881251 0.64454508 0.66701698 0.67392683 0.62155128 0.71644044 0.66209722 0.62296462] mean value: 0.6601010799407959 key: score_time value: [0.13449264 0.1659081 0.14223075 0.18241429 0.18937325 0.20226336 0.19072938 0.18482995 0.15786266 0.18756723] mean value: 0.17376716136932374 key: test_mcc value: [0.65081403 0.60302269 0.75093926 0.73379939 0.8 0.55629391 0.55629391 0.70352647 0.60302269 0.56803756] mean value: 0.6525749902575708 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82051282 0.78947368 0.87804878 0.82352941 0.9 0.79069767 0.79069767 0.85714286 0.78947368 0.8 ] mean value: 0.8239576587166451 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84210526 0.83333333 0.85714286 1. 0.9 0.73913043 0.73913043 0.81818182 0.83333333 0.72 ] mean value: 0.8282357474714453 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.75 0.9 0.7 0.9 0.85 0.85 0.9 0.75 0.9 ] mean value: 0.8300000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.825 0.8 0.875 0.85 0.9 0.775 0.775 0.85 0.8 0.775] mean value: 0.8225 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.825 0.8 0.875 0.85 0.9 0.775 0.775 0.85 0.8 0.775] mean value: 0.8225 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.69565217 0.65217391 0.7826087 0.7 0.81818182 0.65384615 0.65384615 0.75 0.65217391 0.66666667] mean value: 0.7025149488192967 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.43 MCC on Training: 0.65 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.01592159 0.99271607 1.01006341 0.95987391 1.05893207 0.9285593 0.97082615 0.95707583 1.00332904 0.95562291] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( 0.9852920293807983 key: score_time value: [0.16004181 0.19368076 0.23659921 0.21456766 0.21858096 0.20587206 0.21522927 0.1965394 0.21183658 0.24990153] mean value: 0.21028492450714112 key: test_mcc value: [0.7 0.5 0.55629391 0.67131711 0.8 0.56803756 0.60302269 0.61237244 0.35043832 0.48038446] mean value: 0.5841866490244778 key: train_mcc value: [0.87234339 0.87799459 0.88334697 0.87783197 0.87255892 0.87826584 0.88334697 0.87799459 0.8611244 0.87777778] mean value: 0.876258541276983 key: test_fscore value: [0.85 0.75 0.79069767 0.8 0.9 0.8 0.80952381 0.81818182 0.66666667 0.76595745] mean value: 0.795102741559941 key: train_fscore value: [0.93557423 0.93820225 0.94182825 0.93854749 0.93521127 0.93785311 0.94150418 0.93820225 0.93074792 0.93888889] mean value: 0.9376559829504872 key: test_precision value: [0.85 0.75 0.73913043 0.93333333 0.9 0.72 0.77272727 0.75 0.68421053 0.66666667] mean value: 0.7766068233825671 key: train_precision value: [0.94350282 0.94886364 0.93922652 0.94382022 0.94857143 0.95402299 0.94413408 0.94886364 0.9281768 0.93888889] mean value: 0.9438071021400611 key: test_recall value: [0.85 0.75 0.85 0.7 0.9 0.9 0.85 0.9 0.65 0.9 ] mean value: 0.825 key: train_recall value: [0.92777778 0.92777778 0.94444444 0.93333333 0.92222222 0.92222222 0.93888889 0.92777778 0.93333333 0.93888889] mean value: 0.9316666666666666 key: test_accuracy value: [0.85 0.75 0.775 0.825 0.9 0.775 0.8 0.8 0.675 0.725] mean value: 0.7875 key: train_accuracy value: [0.93611111 0.93888889 0.94166667 0.93888889 0.93611111 0.93888889 0.94166667 0.93888889 0.93055556 0.93888889] mean value: 0.9380555555555554 key: test_roc_auc value: [0.85 0.75 0.775 0.825 0.9 0.775 0.8 0.8 0.675 0.725] mean value: 0.7874999999999999 key: train_roc_auc value: [0.93611111 0.93888889 0.94166667 0.93888889 0.93611111 0.93888889 0.94166667 0.93888889 0.93055556 0.93888889] mean value: 0.9380555555555554 key: test_jcc value: [0.73913043 0.6 0.65384615 0.66666667 0.81818182 0.66666667 0.68 0.69230769 0.5 0.62068966] mean value: 0.663748908762402 key: train_jcc value: [0.87894737 0.88359788 0.89005236 0.88421053 0.87830688 0.88297872 0.88947368 0.88359788 0.87046632 0.88481675] mean value: 0.8826448379045436 MCC on Blind test: 0.46 MCC on Training: 0.58 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03630614 0.0351007 0.01518106 0.01517034 0.01455164 0.01526999 0.01950336 0.03802061 0.03630972 0.0367949 ] mean value: 0.02622084617614746 key: score_time value: [0.02343774 0.01197529 0.01214242 0.01196218 0.01198125 0.01206446 0.02081728 0.01211619 0.02085519 0.02345276] mean value: 0.016080474853515624 key: test_mcc value: [0.4 0.2 0.5 0.32732684 0.45056356 0.36147845 0.40201513 0.55629391 0.25031309 0.43643578] mean value: 0.38844267427927376 key: train_mcc value: [0.65001003 0.67231561 0.67273112 0.68905905 0.67248172 0.67273112 0.6722326 0.70038921 0.69471252 0.66120295] mean value: 0.6757865912428997 key: test_fscore value: [0.7 0.6 0.75 0.5625 0.71794872 0.71111111 0.68421053 0.79069767 0.63414634 0.75 ] mean value: 0.6900614371257637 key: train_fscore value: [0.82548476 0.83746556 0.83923706 0.84615385 0.83835616 0.83286119 0.8365651 0.84745763 0.84931507 0.82913165] mean value: 0.8382028032066812 key: test_precision value: [0.7 0.6 0.75 0.75 0.73684211 0.64 0.72222222 0.73913043 0.61904762 0.64285714] mean value: 0.6900099524172751 key: train_precision value: [0.82320442 0.83060109 0.82352941 0.83695652 0.82702703 0.84971098 0.83425414 0.86206897 0.83783784 0.83615819] mean value: 0.8361348595067384 key: test_recall value: [0.7 0.6 0.75 0.45 0.7 0.8 0.65 0.85 0.65 0.9 ] mean value: 0.7050000000000001 key: train_recall value: [0.82777778 0.84444444 0.85555556 0.85555556 0.85 0.81666667 0.83888889 0.83333333 0.86111111 0.82222222] mean value: 0.8405555555555555 key: test_accuracy value: [0.7 0.6 0.75 0.65 0.725 0.675 0.7 0.775 0.625 0.7 ] mean value: 0.69 key: train_accuracy value: [0.825 0.83611111 0.83611111 0.84444444 0.83611111 0.83611111 0.83611111 0.85 0.84722222 0.83055556] mean value: 0.8377777777777778 key: test_roc_auc value: [0.7 0.6 0.75 0.65 0.725 0.675 0.7 0.775 0.625 0.7 ] mean value: 0.69 key: train_roc_auc value: [0.825 0.83611111 0.83611111 0.84444444 0.83611111 0.83611111 0.83611111 0.85 0.84722222 0.83055556] mean value: 0.8377777777777778 key: test_jcc value: [0.53846154 0.42857143 0.6 0.39130435 0.56 0.55172414 0.52 0.65384615 0.46428571 0.6 ] mean value: 0.5308193320921956 key: train_jcc value: [0.70283019 0.72037915 0.72300469 0.73333333 0.72169811 0.71359223 0.71904762 0.73529412 0.73809524 0.70813397] mean value: 0.7215408656066729 MCC on Blind test: 0.3 MCC on Training: 0.39 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.12880254 0.12195826 0.12721825 0.12827802 0.11396861 0.15150261 0.14985538 0.16404557 0.12269855 0.13099289] mean value: 0.13393206596374513 key: score_time value: [0.01617551 0.02146268 0.02245426 0.02336526 0.02313924 0.02070665 0.02402425 0.02267718 0.0211575 0.02493477] mean value: 0.022009730339050293 key: test_mcc value: [0.4 0.2 0.5 0.51031036 0.45056356 0.30618622 0.40201513 0.55629391 0.25031309 0.43643578] mean value: 0.40121180427204495 key: train_mcc value: [0.65001003 0.67231561 0.75028952 0.60668948 0.67248172 0.73446765 0.6722326 0.70038921 0.58661049 0.66120295] mean value: 0.6706689245434767 key: test_fscore value: [0.7 0.6 0.75 0.72222222 0.71794872 0.68181818 0.68421053 0.79069767 0.63414634 0.75 ] mean value: 0.7031043664186931 key: train_fscore value: [0.82548476 0.83746556 0.87323944 0.80862534 0.83835616 0.86285714 0.8365651 0.84745763 0.80211082 0.82913165] mean value: 0.8361293604743443 key: test_precision value: [0.7 0.6 0.75 0.8125 0.73684211 0.625 0.72222222 0.73913043 0.61904762 0.64285714] mean value: 0.6947599524172751 key: train_precision value: [0.82320442 0.83060109 0.88571429 0.78534031 0.82702703 0.88823529 0.83425414 0.86206897 0.7638191 0.83615819] mean value: 0.8336422830512195 key: test_recall value: [0.7 0.6 0.75 0.65 0.7 0.75 0.65 0.85 0.65 0.9 ] mean value: 0.7200000000000001 key: train_recall value: [0.82777778 0.84444444 0.86111111 0.83333333 0.85 0.83888889 0.83888889 0.83333333 0.84444444 0.82222222] mean value: 0.8394444444444444 key: test_accuracy value: [0.7 0.6 0.75 0.75 0.725 0.65 0.7 0.775 0.625 0.7 ] mean value: 0.6975 key: train_accuracy value: [0.825 0.83611111 0.875 0.80277778 0.83611111 0.86666667 0.83611111 0.85 0.79166667 0.83055556] mean value: 0.835 key: test_roc_auc value: [0.7 0.6 0.75 0.75 0.725 0.65 0.7 0.775 0.625 0.7 ] mean value: 0.6975 key: train_roc_auc value: [0.825 0.83611111 0.875 0.80277778 0.83611111 0.86666667 0.83611111 0.85 0.79166667 0.83055556] mean value: 0.835 key: test_jcc value: [0.53846154 0.42857143 0.6 0.56521739 0.56 0.51724138 0.52 0.65384615 0.46428571 0.6 ] mean value: 0.5447623605779527 key: train_jcc value: [0.70283019 0.72037915 0.775 0.67873303 0.72169811 0.75879397 0.71904762 0.73529412 0.66960352 0.70813397] mean value: 0.7189513682545297 MCC on Blind test: 0.3 MCC on Training: 0.4 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02376771 0.01650381 0.01568365 0.01558352 0.01602173 0.01611924 0.01578999 0.01567674 0.01638484 0.01623201] mean value: 0.016776323318481445 key: score_time value: [0.01567316 0.01140404 0.01099873 0.01142168 0.01125193 0.0109601 0.01087904 0.01091695 0.01099563 0.01208329] mean value: 0.011658453941345214 key: test_mcc value: [0.35043832 0.20412415 0.45056356 0.35400522 0.50251891 0.48038446 0.30151134 0.40824829 0.20412415 0.375 ] mean value: 0.36309183896620933 key: train_mcc value: [0.58442564 0.62778747 0.60971925 0.58214224 0.6171335 0.58903435 0.57421951 0.57792049 0.60578932 0.60133779] mean value: 0.5969509562625129 key: test_fscore value: [0.66666667 0.55555556 0.73170732 0.64864865 0.73684211 0.76595745 0.66666667 0.72727273 0.63636364 0.73076923] mean value: 0.6866450001087971 key: train_fscore value: [0.79784367 0.81440443 0.81364829 0.80104712 0.8119891 0.7967033 0.79466667 0.78651685 0.80547945 0.80645161] mean value: 0.8028750495361268 key: test_precision value: [0.68421053 0.625 0.71428571 0.70588235 0.77777778 0.66666667 0.63636364 0.66666667 0.58333333 0.59375 ] mean value: 0.6653936674350761 key: train_precision value: [0.77486911 0.8121547 0.77114428 0.75742574 0.79679144 0.78804348 0.76410256 0.79545455 0.79459459 0.78125 ] mean value: 0.7835830453524305 key: test_recall value: [0.65 0.5 0.75 0.6 0.7 0.9 0.7 0.8 0.7 0.95] mean value: 0.725 key: train_recall value: [0.82222222 0.81666667 0.86111111 0.85 0.82777778 0.80555556 0.82777778 0.77777778 0.81666667 0.83333333] mean value: 0.8238888888888889 key: test_accuracy value: [0.675 0.6 0.725 0.675 0.75 0.725 0.65 0.7 0.6 0.65 ] mean value: 0.675 key: train_accuracy value: [0.79166667 0.81388889 0.80277778 0.78888889 0.80833333 0.79444444 0.78611111 0.78888889 0.80277778 0.8 ] mean value: 0.7977777777777778 key: test_roc_auc value: [0.675 0.6 0.725 0.675 0.75 0.725 0.65 0.7 0.6 0.65 ] mean value: 0.675 key: train_roc_auc value: [0.79166667 0.81388889 0.80277778 0.78888889 0.80833333 0.79444444 0.78611111 0.78888889 0.80277778 0.8 ] mean value: 0.7977777777777778 key: test_jcc value: [0.5 0.38461538 0.57692308 0.48 0.58333333 0.62068966 0.5 0.57142857 0.46666667 0.57575758] mean value: 0.5259414263897023 key: train_jcc value: [0.66367713 0.68691589 0.68584071 0.66812227 0.68348624 0.66210046 0.65929204 0.64814815 0.67431193 0.67567568] mean value: 0.6707570477582943 MCC on Blind test: 0.31 MCC on Training: 0.36 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01848388 0.02353001 0.02304053 0.02098775 0.0257237 0.0263145 0.02219963 0.02335763 0.0252955 0.02253652] mean value: 0.023146963119506835 key: score_time value: [0.00875473 0.0136764 0.01403379 0.01399541 0.01399732 0.01392794 0.01392269 0.01399899 0.0139482 0.01391339] mean value: 0.013416886329650879 key: test_mcc value: [0.16666667 0.06579517 0.25 0.32732684 0.50251891 0.48038446 0.25819889 0.21821789 0.16666667 0.27994626] mean value: 0.27157217426892466 key: train_mcc value: [0.25378236 0.49501719 0.45245173 0.66234906 0.66161159 0.56853834 0.59625678 0.50812445 0.32660252 0.54916259] mean value: 0.5073896615278827 key: test_fscore value: [0.67857143 0.64150943 0.69230769 0.5625 0.73684211 0.76595745 0.57142857 0.5 0.67857143 0.51612903] mean value: 0.6343817139171118 key: train_fscore value: [0.69685039 0.77241379 0.75663717 0.8252149 0.82719547 0.79691517 0.74342105 0.61654135 0.71574642 0.7090301 ] mean value: 0.7459965816793886 key: test_precision value: [0.52777778 0.51515152 0.5625 0.75 0.77777778 0.66666667 0.66666667 0.66666667 0.52777778 0.72727273] mean value: 0.6388257575757577 key: train_precision value: [0.53963415 0.65882353 0.62867647 0.85207101 0.84393064 0.74162679 0.91129032 0.95348837 0.56634304 0.8907563 ] mean value: 0.758664062162102 key: test_recall value: [0.95 0.85 0.9 0.45 0.7 0.9 0.5 0.4 0.95 0.4 ] mean value: 0.7000000000000001 key: train_recall value: [0.98333333 0.93333333 0.95 0.8 0.81111111 0.86111111 0.62777778 0.45555556 0.97222222 0.58888889] mean value: 0.7983333333333333 key: test_accuracy value: [0.55 0.525 0.6 0.65 0.75 0.725 0.625 0.6 0.55 0.625] mean value: 0.62 key: train_accuracy value: [0.57222222 0.725 0.69444444 0.83055556 0.83055556 0.78055556 0.78333333 0.71666667 0.61388889 0.75833333] mean value: 0.7305555555555555/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' key: test_roc_auc value: [0.55 0.525 0.6 0.65 0.75 0.725 0.625 0.6 0.55 0.625] mean value: 0.62 key: train_roc_auc value: [0.57222222 0.725 0.69444444 0.83055556 0.83055556 0.78055556 0.78333333 0.71666667 0.61388889 0.75833333] mean value: 0.7305555555555556 key: test_jcc value: [0.51351351 0.47222222 0.52941176 0.39130435 0.58333333 0.62068966 0.4 0.33333333 0.51351351 0.34782609] mean value: 0.470514777057682 key: train_jcc value: [0.5347432 0.62921348 0.60854093 0.70243902 0.70531401 0.66239316 0.59162304 0.44565217 0.55732484 0.5492228 ] mean value: 0.5986466656529182 MCC on Blind test: 0.3 MCC on Training: 0.27 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10538673 0.09183884 0.09605789 0.08989596 0.0981648 0.08816004 0.09013271 0.08940768 0.09605694 0.09259391] mean value: 0.09376955032348633 key: score_time value: [0.01095104 0.01118755 0.01131535 0.01089764 0.01093531 0.01101398 0.0110116 0.01100492 0.01122355 0.01098967] mean value: 0.011053061485290528 key: test_mcc value: [0.6 0.51031036 0.65081403 0.61237244 0.70352647 0.50251891 0.60302269 0.60302269 0.71443451 0.65081403] mean value: 0.6150836116946066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.72222222 0.82051282 0.77777778 0.84210526 0.73684211 0.80952381 0.80952381 0.83333333 0.82926829] mean value: 0.7981109433997753 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.8125 0.84210526 0.875 0.88888889 0.77777778 0.77272727 0.77272727 0.9375 0.80952381] mean value: 0.8288750284802916 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.65 0.8 0.7 0.8 0.7 0.85 0.85 0.75 0.85] mean value: 0.775 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.75 0.825 0.8 0.85 0.75 0.8 0.8 0.85 0.825] mean value: 0.8049999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.75 0.825 0.8 0.85 0.75 0.8 0.8 0.85 0.825] mean value: 0.8049999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.56521739 0.69565217 0.63636364 0.72727273 0.58333333 0.68 0.68 0.71428571 0.70833333] mean value: 0.6657124976472802 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.43 MCC on Training: 0.62 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (424, 173) Data type to split: actual Split type: sl target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 173 No.of cols dropped: 2 No. of columns for x_features: 171 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: sl Total no. of input features: 171 --------No. of numerical features: 165 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 69 Train data size: (63, 171) y_train numbers: Counter({0: 32, 1: 31}) Test data size: (6, 171) y_test_numbers: Counter({1: 3, 0: 3}) y_train ratio: 1.032258064516129 y_test ratio: 1.0 ------------------------------------------------------------- Simple Random OverSampling Counter({1: 32, 0: 32}) (64, 171) Simple Random UnderSampling Counter({0: 31, 1: 31}) (62, 171) Simple Combined Over and UnderSampling Counter({0: 32, 1: 32}) (64, 171) SMOTE_NC OverSampling Counter({1: 32, 0: 32}) (64, 171) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (64, 171) y_train numbers: 64 y_train ratio: 1.0 y_test ratio: 1.0 ================================ Resampling: Random underampling ================================ Train data size: (62, 171) y_train numbers: 62 y_train ratio: 1.0 y_test ratio: 1.0 ================================ Resampling:Combined (over+under) ================================ Train data size: (64, 171) y_train numbers: 64 y_train ratio: 1.0 y_test ratio: 1.0 ============================== Resampling: Smote NC ============================== Train data size: (64, 171) y_train numbers: 64 y_train ratio: 1.0 y_test ratio: 1.0 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... 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Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... 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Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... sK?x\(?VUUUUU?t /C?əə?袋.? R`?@Iݗ??9}?m۶m?Hx?<[c?|N?V??FWh?$dm[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... @pff??!J@S`E?zGz?D@Xz?@ Q1?l[B?%M@K@?@0!? Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.94004796 0.94146341 0.9468599 0.93779904 0.94174757 0.95192308 0.93857494 0.96153846 0.92822967 0.95169082] mean value: 0.9439874858888505 key: test_precision value: [0.86363636 0.85 0.72727273 0.75 0.625 0.64 0.76 0.625 0.79310345 0.7826087 ] mean value: 0.7416621234837127 key: train_precision value: [0.93333333 0.95544554 0.95145631 0.93333333 0.95098039 0.95192308 0.95979899 0.96153846 0.92380952 0.95631068] mean value: 0.9477929650915184 key: test_recall value: [0.79166667 0.73913043 0.69565217 0.7826087 0.65217391 0.69565217 0.82608696 0.65217391 1. 0.7826087 ] mean value: 0.7617753623188406 key: train_recall value: [0.9468599 0.92788462 0.94230769 0.94230769 0.93269231 0.95192308 0.91826923 0.96153846 0.93269231 0.94711538] mean value: 0.9403590672612412 key: test_accuracy value: [0.8 0.76923077 0.66666667 0.71794872 0.56410256 0.58974359 0.74358974 0.56410256 0.84615385 0.74358974] mean value: 0.7005128205128205 key: train_accuracy value: [0.92877493 0.93181818 0.9375 0.92613636 0.93181818 0.94318182 0.92897727 0.95454545 0.91477273 0.94318182] mean value: 0.9340706746956746 key: test_roc_auc value: [0.80208333 0.77581522 0.66032609 0.70380435 0.54483696 0.56657609 0.72554348 0.54483696 0.8125 0.73505435] mean value: 0.6871376811594202 key: train_roc_auc value: [0.92481884 0.93269231 0.93643162 0.92254274 0.93162393 0.94123932 0.93135684 0.95299145 0.9107906 0.94230769] mean value: 0.9326795336306205 key: test_jcc value: [0.7037037 0.65384615 0.55172414 0.62068966 0.46875 0.5 0.65517241 0.46875 0.79310345 0.64285714] mean value: 0.6058596655579415 key: train_jcc value: [0.88687783 0.88940092 0.89908257 0.88288288 0.88990826 0.90825688 0.88425926 0.92592593 0.86607143 0.9078341 ] mean value: 0.8940500054157287 MCC on Blind test: 0.03 MCC on Training: 0.38 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.19111824 0.24648404 0.21344709 0.24599147 0.22884607 0.29986 0.23360419 0.23803163 0.23730445 0.20066762] mean value: 0.2335354804992676 key: score_time value: [0.04443455 0.04328513 0.0480032 0.04027772 0.063869 0.0643115 0.07259798 0.0580914 0.04141188 0.04560566] mean value: 0.0521888017654419 key: test_mcc value: [0.74325613 0.52831916 0.37805005 0.62641145 0.02786391 0.18430245 0.78804348 0.30496347 0.73720978 0.57608696] mean value: 0.4894506828074053 key: train_mcc value: [1. 1. 1. 1. 1. 1. 0.99414733 1. 1. 1. ] mean value: 0.9994147329356592 key: test_fscore value: [0.89361702 0.8 0.72727273 0.85106383 0.625 0.69387755 0.91304348 0.72340426 0.89795918 0.82608696] mean value: 0.7951325003132192 key: train_fscore value: [1. 1. 1. 1. 1. 1. 0.99759036 1. 1. 1. ] mean value: 0.9997590361445784 key: test_precision value: [0.91304348 0.81818182 0.76190476 0.83333333 0.6 0.65384615 0.91304348 0.70833333 0.84615385 0.82608696] mean value: 0.7873927159796725 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.7826087 0.69565217 0.86956522 0.65217391 0.73913043 0.91304348 0.73913043 0.95652174 0.82608696] mean value: 0.804891304347826 key: train_recall value: [1. 1. 1. 1. 1. 1. 0.99519231 1. 1. 1. ] mean value: 0.9995192307692307 key: test_accuracy value: [0.875 0.76923077 0.69230769 0.82051282 0.53846154 0.61538462 0.8974359 0.66666667 0.87179487 0.79487179] mean value: 0.7541666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 0.99715909 1. 1. 1. ] mean value: 0.999715909090909 key: test_roc_auc value: [0.875 0.76630435 0.69157609 0.80978261 0.51358696 0.58831522 0.89402174 0.65081522 0.85326087 0.78804348] mean value: 0.7430706521739131 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 0.99759615 1. 1. 1. ] mean value: 0.9997596153846153 key: test_jcc value: [0.80769231 0.66666667 0.57142857 0.74074074 0.45454545 0.53125 0.84 0.56666667 0.81481481 0.7037037 ] mean value: 0.6697508926258926 key: train_jcc value: [1. 1. 1. 1. 1. 1. 0.99519231 1. 1. 1. ] mean value: 0.9995192307692307 MCC on Blind test: 0.37 MCC on Training: 0.49 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0286479 0.0240624 0.02461648 0.02377725 0.02171755 0.0247345 0.0257659 0.02476501 0.02333546 0.0282321 ] mean value: 0.024965453147888183 key: score_time value: [0.00967383 0.00964189 0.00917435 0.00869751 0.00894713 0.00952673 0.01006126 0.00956106 0.0097549 0.00967956] mean value: 0.009471821784973144 key: test_mcc value: [ 0.52704628 0.48261709 0.00269551 0.36413043 0.06434895 0.09066482 0.4701087 -0.04021809 0.33426762 0.21294497] mean value: 0.2508606256194377 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.81632653 0.77272727 0.57777778 0.73913043 0.59090909 0.63829787 0.7826087 0.54545455 0.69767442 0.66666667] mean value: 0.6827573305527459 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.80952381 0.59090909 0.73913043 0.61904762 0.625 0.7826087 0.57142857 0.75 0.68181818] mean value: 0.6969466403162056 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.83333333 0.73913043 0.56521739 0.73913043 0.56521739 0.65217391 0.7826087 0.52173913 0.65217391 0.65217391] mean value: 0.6702898550724639 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.775 0.74358974 0.51282051 0.69230769 0.53846154 0.56410256 0.74358974 0.48717949 0.66666667 0.61538462] mean value: 0.6339102564102564 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76041667 0.74456522 0.5013587 0.68206522 0.5326087 0.54483696 0.73505435 0.47961957 0.66983696 0.60733696] mean value: 0.625769927536232 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68965517 0.62962963 0.40625 0.5862069 0.41935484 0.46875 0.64285714 0.375 0.53571429 0.5 ] mean value: 0.5253417965876254 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.32 MCC on Training: 0.25 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00920367 0.00929785 0.00923419 0.00920153 0.0090704 0.00924778 0.00910735 0.00921988 0.00932622 0.00920844] mean value: 0.00921173095703125 key: score_time value: [0.00849652 0.00839877 0.00833964 0.00838542 0.00828671 0.00833893 0.00833654 0.00831294 0.00839829 0.00843906] mean value: 0.008373284339904785 key: test_mcc value: [ 0.49236596 0.4121128 0.12163341 0.29488391 0.02786391 0.09066482 0.27348302 -0.04021809 0.19781414 0.4121128 ] mean value: 0.22827166702930662 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.7826087 0.76595745 0.68 0.73469388 0.625 0.63829787 0.68181818 0.54545455 0.68085106 0.76595745] mean value: 0.6900639130263155 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.81818182 0.75 0.62962963 0.69230769 0.6 0.625 0.71428571 0.57142857 0.66666667 0.75 ] mean value: 0.6817500092500093 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.7826087 0.73913043 0.7826087 0.65217391 0.65217391 0.65217391 0.52173913 0.69565217 0.7826087 ] mean value: 0.701086956521739 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.71794872 0.58974359 0.66666667 0.53846154 0.56410256 0.64102564 0.48717949 0.61538462 0.71794872] mean value: 0.6288461538461539 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.70380435 0.55706522 0.64130435 0.51358696 0.54483696 0.63858696 0.47961957 0.59782609 0.70380435] mean value: 0.6130434782608695 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.64285714 0.62068966 0.51515152 0.58064516 0.45454545 0.46875 0.51724138 0.375 0.51612903 0.62068966] mean value: 0.5311698995757672 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.5 MCC on Training: 0.23 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.12057471 0.11632943 0.11691785 0.11592793 0.11875892 0.12105036 0.12288928 0.11772704 0.11852765 0.11895132] mean value: 0.11876544952392579 key: score_time value: [0.01733565 0.01755285 0.01733565 0.01735711 0.01737857 0.01906371 0.01785064 0.01819873 0.01758432 0.01749492] mean value: 0.0177152156829834 key: test_mcc value: [0.6875 0.4121128 0.40546538 0.37805005 0.10713839 0.07372098 0.40546538 0.24520241 0.4701087 0.51604685] mean value: 0.3700810936626047 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.875 0.76595745 0.7755102 0.72727273 0.69230769 0.65306122 0.7755102 0.70833333 0.7826087 0.81632653] mean value: 0.7571888058639744 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.75 0.73076923 0.76190476 0.62068966 0.61538462 0.73076923 0.68 0.7826087 0.76923077] mean value: 0.7316356958883196 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.7826087 0.82608696 0.69565217 0.7826087 0.69565217 0.82608696 0.73913043 0.7826087 0.86956522] mean value: 0.7875 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85 0.71794872 0.71794872 0.69230769 0.58974359 0.56410256 0.71794872 0.64102564 0.74358974 0.76923077] mean value: 0.7003846153846154 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.84375 0.70380435 0.69429348 0.69157609 0.54755435 0.53532609 0.69429348 0.61956522 0.73505435 0.74728261] mean value: 0.68125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77777778 0.62068966 0.63333333 0.57142857 0.52941176 0.48484848 0.63333333 0.5483871 0.64285714 0.68965517] mean value: 0.6131722332644927 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.37 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.57867289 0.5750947 0.56384039 0.56862473 0.61694503 0.61854935 0.56697202 0.57754683 0.56954551 0.56595469] mean value: 0.5801746129989624 key: score_time value: [0.00955367 0.00908279 0.00907636 0.01082039 0.01023364 0.00958657 0.00940728 0.00955629 0.00933576 0.0092113 ] mean value: 0.009586405754089356 key: test_mcc value: [0.74325613 0.57608696 0.27348302 0.46254092 0.15217391 0.18430245 0.78804348 0.36413043 0.57341464 0.58718413] mean value: 0.47046160501780304 key: train_mcc value: [0.99412336 0.99413485 1. 1. 0.99414733 0.98824786 0.99414733 1. 1. 0.99413485] mean value: 0.9958935573042595 key: test_fscore value: [0.89361702 0.82608696 0.68181818 0.79166667 0.65217391 0.69387755 0.91304348 0.73913043 0.84 0.81818182] mean value: 0.7849596021572366 key: train_fscore value: [0.99759036 0.99760192 1. 1. 0.99759036 0.99519231 0.99759036 1. 1. 0.99760192] mean value: 0.9983167228960113 key: test_precision value: [0.91304348 0.82608696 0.71428571 0.76 0.65217391 0.65384615 0.91304348 0.73913043 0.77777778 0.85714286] mean value: 0.7806530763922067 key: train_precision value: [0.99519231 0.99521531 1. 1. 1. 0.99519231 1. 1. 1. 0.99521531] mean value: 0.9980815237394184 key: test_recall value: [0.875 0.82608696 0.65217391 0.82608696 0.65217391 0.73913043 0.91304348 0.73913043 0.91304348 0.7826087 ] mean value: 0.7918478260869565 key: train_recall value: [1. 1. 1. 1. 0.99519231 0.99519231 0.99519231 1. 1. 1. ] mean value: 0.9985576923076923 key: test_accuracy value: [0.875 0.79487179 0.64102564 0.74358974 0.58974359 0.61538462 0.8974359 0.69230769 0.79487179 0.79487179] mean value: 0.7439102564102564 key: train_accuracy value: [0.997151 0.99715909 1. 1. 0.99715909 0.99431818 0.99715909 1. 1. 0.99715909] mean value: 0.9980105542605543 key: test_roc_auc value: [0.875 0.78804348 0.63858696 0.72554348 0.57608696 0.58831522 0.89402174 0.68206522 0.76902174 0.79755435] mean value: 0.7334239130434783 key: train_roc_auc value: [0.99652778 0.99652778 1. 1. 0.99759615 0.99412393 0.99759615 1. 1. 0.99652778] mean value: 0.9978899572649574 key: test_jcc value: [0.80769231 0.7037037 0.51724138 0.65517241 0.48387097 0.53125 0.84 0.5862069 0.72413793 0.69230769] mean value: 0.6541583292135295 key: train_jcc value: [0.99519231 0.99521531 1. 1. 0.99519231 0.99043062 0.99519231 1. 1. 0.99521531] mean value: 0.9966438167096061 MCC on Blind test: 0.37 MCC on Training: 0.47 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01003385 0.01086712 0.01110387 0.01083755 0.01111317 0.0100615 0.01065922 0.01072502 0.01090646 0.01052737] mean value: 0.010683512687683106 key: score_time value: [0.00936055 0.00999904 0.0099411 0.00994611 0.00989676 0.00974417 0.00971031 0.0093317 0.00974822 0.00905418] mean value: 0.009673213958740235 key: test_mcc value: [ 0.49236596 0.54285061 0.519803 0.36413043 -0.27173913 -0.01648451 0.3180697 0.16891598 0.29488391 0.63344389] mean value: 0.3046239848181219 key: train_mcc value: [0.33971221 0.30619379 0.35132431 0.33617045 0.34961023 0.38283133 0.35605999 0.3813381 0.37766739 0.34953525] mean value: 0.3530443037416988 key: test_fscore value: [0.7826087 0.79069767 0.82352941 0.73913043 0.47826087 0.59574468 0.71111111 0.63636364 0.73469388 0.84444444] mean value: 0.7136584836504587 key: train_fscore value: [0.715 0.70904645 0.71536524 0.72058824 0.75892857 0.74019608 0.72592593 0.74146341 0.73 0.71679198] mean value: 0.7273305899726445 key: test_precision value: [0.81818182 0.85 0.75 0.73913043 0.47826087 0.58333333 0.72727273 0.66666667 0.69230769 0.86363636] mean value: 0.7168789905746428 key: train_precision value: [0.74093264 0.72139303 0.75132275 0.735 0.70833333 0.755 0.74619289 0.75247525 0.76041667 0.7486911 ] mean value: 0.7419757669037876 key: test_recall value: [0.75 0.73913043 0.91304348 0.73913043 0.47826087 0.60869565 0.69565217 0.60869565 0.7826087 0.82608696] mean value: 0.7141304347826087 key: train_recall value: [0.69082126 0.69711538 0.68269231 0.70673077 0.81730769 0.72596154 0.70673077 0.73076923 0.70192308 0.6875 ] mean value: 0.7147552025269416 key: test_accuracy value: [0.75 0.76923077 0.76923077 0.69230769 0.38461538 0.51282051 0.66666667 0.58974359 0.66666667 0.82051282] mean value: 0.6621794871794873 key: train_accuracy value: [0.67521368 0.66193182 0.67897727 0.67613636 0.69318182 0.69886364 0.68465909 0.69886364 0.69318182 0.67897727] mean value: 0.6839986402486402 key: test_roc_auc value: [0.75 0.77581522 0.73777174 0.68206522 0.36413043 0.49184783 0.66032609 0.58559783 0.64130435 0.81929348] mean value: 0.6508152173913044 key: train_roc_auc value: [0.67179952 0.65411325 0.67815171 0.66933761 0.66559829 0.69284188 0.67975427 0.6917735 0.69123932 0.67708333] mean value: 0.6771692679301374 key: test_jcc value: [0.64285714 0.65384615 0.7 0.5862069 0.31428571 0.42424242 0.55172414 0.46666667 0.58064516 0.73076923] mean value: 0.5651243528440414 key: train_jcc value: [0.55642023 0.54924242 0.55686275 0.56321839 0.61151079 0.58754864 0.56976744 0.58914729 0.57480315 0.55859375] mean value: 0.5717114851395768 MCC on Blind test: 0.5 MCC on Training: 0.3 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.10089827 0.08711076 0.1035831 0.10246706 0.10298562 0.10321021 0.11418509 0.09717679 0.12284422 0.11086917] mean value: 0.1045330286026001 key: score_time value: [0.02171016 0.02416873 0.02509117 0.02958679 0.02718163 0.0250144 0.0254786 0.02229214 0.02203631 0.02457452] mean value: 0.02471344470977783 key: test_mcc value: [ 0.30506954 0.51604685 0.30496347 0.03806935 -0.03686049 -0.10425721 0.03806935 0.28908807 0.17227757 0.519803 ] mean value: 0.20422694993146778 key: train_mcc value: [0.89495493 0.89575396 0.91805837 0.90707884 0.88320834 0.91846817 0.87086793 0.924188 0.91276525 0.91901409] mean value: 0.904435788279297 key: test_fscore value: [0.74509804 0.81632653 0.72340426 0.67924528 0.6122449 0.6 0.67924528 0.76363636 0.70588235 0.82352941] mean value: 0.7148612417486245 key: train_fscore value: [0.95774648 0.95813953 0.96698113 0.96261682 0.95327103 0.96713615 0.94835681 0.96941176 0.96487119 0.96728972] mean value: 0.9615820632757641 key: test_precision value: [0.7037037 0.76923077 0.70833333 0.6 0.57692308 0.55555556 0.6 0.65625 0.64285714 0.75 ] mean value: 0.6562853581603582 key: train_precision value: [0.93150685 0.92792793 0.94907407 0.93636364 0.92727273 0.94495413 0.9266055 0.94930876 0.94063927 0.94090909] mean value: 0.9374561964056809 key: test_recall value: [0.79166667 0.86956522 0.73913043 0.7826087 0.65217391 0.65217391 0.7826087 0.91304348 0.7826087 0.91304348] mean value: 0.7878623188405797 key: train_recall value: [0.98550725 0.99038462 0.98557692 0.99038462 0.98076923 0.99038462 0.97115385 0.99038462 0.99038462 0.99519231] mean value: 0.9870122630992197 key: test_accuracy value: [0.675 0.76923077 0.66666667 0.56410256 0.51282051 0.48717949 0.56410256 0.66666667 0.61538462 0.76923077] mean value: 0.6290384615384614 key: train_accuracy value: [0.94871795 0.94886364 0.96022727 0.95454545 0.94318182 0.96022727 0.9375 0.96306818 0.95738636 0.96022727] mean value: 0.9533945221445222 key: test_roc_auc value: [0.64583333 0.74728261 0.65081522 0.51630435 0.48233696 0.45108696 0.51630435 0.61277174 0.57880435 0.73777174] mean value: 0.5939311594202898 key: train_roc_auc value: [0.94067029 0.93963675 0.95459402 0.9465812 0.93482906 0.95352564 0.93002137 0.95699786 0.95005342 0.95245726] mean value: 0.9459366871051653 key: test_jcc value: [0.59375 0.68965517 0.56666667 0.51428571 0.44117647 0.42857143 0.51428571 0.61764706 0.54545455 0.7 ] mean value: 0.5611492771089627 key: train_jcc value: [0.91891892 0.91964286 0.93607306 0.92792793 0.91071429 0.93636364 0.90178571 0.94063927 0.9321267 0.93665158] mean value: 0.926084394966345 MCC on Blind test: -0.06 MCC on Training: 0.2 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02291346 0.0105722 0.01012969 0.01012325 0.01000237 0.00976086 0.01001477 0.01019359 0.00991273 0.00980854] mean value: 0.011343145370483398 key: score_time value: [0.02179909 0.01310992 0.01241803 0.01372862 0.01279116 0.01638865 0.01423287 0.01213288 0.0136776 0.01253009] mean value: 0.014280891418457032 key: test_mcc value: [ 0.31622777 0.28552012 0.15217391 0.09066482 -0.08080534 -0.29814384 -0.01224439 0.03806935 0.17227757 0.519803 ] mean value: 0.1183542956152773 key: train_mcc value: [0.43602043 0.46144679 0.49231255 0.46130196 0.5656972 0.47382495 0.43772318 0.48182559 0.46153232 0.42997753] mean value: 0.47016624957030617 key: test_fscore value: [0.73469388 0.75471698 0.65217391 0.63829787 0.58333333 0.5 0.65384615 0.67924528 0.70588235 0.82352941] mean value: 0.6725719178971237 key: train_fscore value: [0.78733032 0.80176211 0.80812641 0.80088496 0.83371298 0.8018018 0.78636364 0.79907621 0.7972973 0.79120879] mean value: 0.8007564521064282 key: test_precision value: [0.72 0.66666667 0.65217391 0.625 0.56 0.48 0.5862069 0.6 0.64285714 0.75 ] mean value: 0.6282904619119012 key: train_precision value: [0.74042553 0.7398374 0.76170213 0.74180328 0.79220779 0.75423729 0.74568966 0.76888889 0.75 0.72874494] mean value: 0.752353690031292 key: test_recall value: [0.75 0.86956522 0.65217391 0.65217391 0.60869565 0.52173913 0.73913043 0.7826087 0.7826087 0.91304348] mean value: 0.7271739130434782 key: train_recall value: [0.84057971 0.875 0.86057692 0.87019231 0.87980769 0.85576923 0.83173077 0.83173077 0.85096154 0.86538462] mean value: 0.8561733556298774 key: test_accuracy value: [0.675 0.66666667 0.58974359 0.56410256 0.48717949 0.38461538 0.53846154 0.56410256 0.61538462 0.76923077] mean value: 0.5854487179487179 key: train_accuracy value: [0.73219373 0.74431818 0.75852273 0.74431818 0.79261364 0.75 0.73295455 0.75284091 0.74431818 0.73011364] mean value: 0.7482193732193732 key: test_roc_auc value: [0.65625 0.62228261 0.57608696 0.54483696 0.46059783 0.35461957 0.49456522 0.51630435 0.57880435 0.73777174] mean value: 0.554211956521739 key: train_roc_auc value: [0.7084843 0.71527778 0.73584402 0.71634615 0.77323718 0.72649573 0.71100427 0.73530983 0.72061966 0.70005342] mean value: 0.7242672333704941 key: test_jcc value: [0.58064516 0.60606061 0.48387097 0.46875 0.41176471 0.33333333 0.48571429 0.51428571 0.54545455 0.7 ] mean value: 0.5129879319763095 key: train_jcc value: [0.64925373 0.66911765 0.6780303 0.66789668 0.71484375 0.66917293 0.64794007 0.66538462 0.66292135 0.65454545] mean value: 0.667910653588107 MCC on Blind test: 0.19 MCC on Training: 0.12 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02930784 0.08795834 0.05480313 0.07572603 0.04138994 0.03490448 0.03409052 0.07017255 0.06605673 0.06678724] mean value: 0.056119680404663086 key: score_time value: [0.01244783 0.03076887 0.0243547 0.02196693 0.0131824 0.01321602 0.01292729 0.0237751 0.01311994 0.02430892] mean value: 0.019006800651550294 key: test_mcc value: [ 0.67445327 0.4701087 0.51926212 0.46254092 -0.03686049 0.07372098 0.57608696 0.02786391 0.35387166 0.46254092] mean value: 0.3583588945934485 key: train_mcc value: [0.66823306 0.66227149 0.6981977 0.69222299 0.71739451 0.71043296 0.67441705 0.73653544 0.68046699 0.70474489] mean value: 0.6944917078361071 key: test_fscore value: [0.8372093 0.7826087 0.80851064 0.79166667 0.6122449 0.65306122 0.82608696 0.625 0.75 0.79166667] mean value: 0.747805504857968 key: train_fscore value: [0.86729858 0.86774942 0.88111888 0.87906977 0.88516746 0.88683603 0.87119438 0.89104116 0.87323944 0.88151659] mean value: 0.8784231704457994 key: test_precision value: [0.94736842 0.7826087 0.79166667 0.76 0.57692308 0.61538462 0.82608696 0.6 0.72 0.76 ] mean value: 0.7380038432200904 key: train_precision value: [0.85116279 0.83856502 0.85520362 0.85135135 0.88095238 0.85333333 0.84931507 0.89756098 0.85321101 0.86915888] mean value: 0.8599814430447659 key: test_recall value: [0.75 0.7826087 0.82608696 0.82608696 0.65217391 0.69565217 0.82608696 0.65217391 0.7826087 0.82608696] mean value: 0.7619565217391304 key: train_recall value: [0.88405797 0.89903846 0.90865385 0.90865385 0.88942308 0.92307692 0.89423077 0.88461538 0.89423077 0.89423077] mean value: 0.8980211817168341 key: test_accuracy value: [0.825 0.74358974 0.76923077 0.74358974 0.51282051 0.56410256 0.79487179 0.53846154 0.69230769 0.74358974] mean value: 0.6927564102564103 key: train_accuracy value: [0.84045584 0.83806818 0.85511364 0.85227273 0.86363636 0.86079545 0.84375 0.87215909 0.84659091 0.85795455] mean value: 0.8530796749546751 key: test_roc_auc value: [0.84375 0.73505435 0.75679348 0.72554348 0.48233696 0.53532609 0.78804348 0.51358696 0.67255435 0.72554348] mean value: 0.6778532608695651 key: train_roc_auc value: [0.83091787 0.82451923 0.84321581 0.83974359 0.85790598 0.84695513 0.83253205 0.86939103 0.83600427 0.84989316] mean value: 0.8431078130806393 key: test_jcc value: [0.72 0.64285714 0.67857143 0.65517241 0.44117647 0.48484848 0.7037037 0.45454545 0.6 0.65517241] mean value: 0.6036047512700656 key: train_jcc value: [0.76569038 0.76639344 0.7875 0.78423237 0.79399142 0.7966805 0.77178423 0.80349345 0.775 0.78813559] mean value: 0.7832901373938685 MCC on Blind test: 0.17 MCC on Training: 0.36 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05470824 0.03854442 0.03696156 0.03821802 0.03855181 0.04292464 0.04360843 0.03746986 0.04387283 0.03793955] mean value: 0.041279935836791994 key: score_time value: [0.0138073 0.01228499 0.01226997 0.0122385 0.01220012 0.012254 0.01315355 0.01311111 0.01298809 0.0120337 ] mean value: 0.01263413429260254 key: test_mcc value: [0.73786479 0.46254092 0.46046933 0.46254092 0.17227757 0.22652118 0.51604685 0.02786391 0.63564481 0.46046933] mean value: 0.41622395935809803 key: train_mcc value: [0.51604685 0.52919506 0.52919506 0.57177214 0.54816561 0.57791971 0.55354226 0.58391402 0.54137655 0.53528809] mean value: 0.5486415359574506 key: test_fscore value: [0.89795918 0.79166667 0.8 0.79166667 0.70588235 0.73076923 0.81632653 0.625 0.8627451 0.8 ] mean value: 0.7822015729368671 key: train_fscore value: [0.81632653 0.82247191 0.82247191 0.83561644 0.82407407 0.83678161 0.82993197 0.84018265 0.8261851 0.82432432] mean value: 0.8278366519558006 key: test_precision value: [0.88 0.76 0.74074074 0.76 0.64285714 0.65517241 0.76923077 0.6 0.78571429 0.74074074] mean value: 0.7334456093076782 key: train_precision value: [0.76923077 0.7721519 0.7721519 0.79565217 0.79464286 0.80176211 0.78540773 0.8 0.7787234 0.77542373] mean value: 0.7845146570683237 key: test_recall value: [0.91666667 0.82608696 0.86956522 0.82608696 0.7826087 0.82608696 0.86956522 0.65217391 0.95652174 0.86956522] mean value: 0.8394927536231883 key: train_recall value: [0.86956522 0.87980769 0.87980769 0.87980769 0.85576923 0.875 0.87980769 0.88461538 0.87980769 0.87980769] mean value: 0.8763795986622073 key: test_accuracy value: [0.875 0.74358974 0.74358974 0.74358974 0.61538462 0.64102564 0.76923077 0.53846154 0.82051282 0.74358974] mean value: 0.7233974358974359 key: train_accuracy value: [0.76923077 0.77556818 0.77556818 0.79545455 0.78409091 0.79829545 0.78693182 0.80113636 0.78125 0.77840909] mean value: 0.7845935314685315 key: test_roc_auc value: [0.86458333 0.72554348 0.71603261 0.72554348 0.57880435 0.60054348 0.74728261 0.51358696 0.79076087 0.71603261] mean value: 0.6978713768115943 key: train_roc_auc value: [0.74728261 0.75240385 0.75240385 0.7767094 0.76816239 0.78125 0.76629274 0.78258547 0.75934829 0.75587607] mean value: 0.7642314659977703 key: test_jcc value: [0.81481481 0.65517241 0.66666667 0.65517241 0.54545455 0.57575758 0.68965517 0.45454545 0.75862069 0.66666667] mean value: 0.6482526413560896 key: train_jcc value: [0.68965517 0.69847328 0.69847328 0.71764706 0.7007874 0.71936759 0.70930233 0.72440945 0.70384615 0.70114943] mean value: 0.7063111140164231 MCC on Blind test: 0.3 MCC on Training: 0.42 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48850012 0.58990645 0.59133458 0.52428436 0.57095885 0.49955797 0.6930635 0.50486517 0.50573516 0.51541924] mean value: 0.5483625411987305 key: score_time value: [0.01209831 0.01216078 0.01248932 0.01206803 0.02162933 0.01200771 0.01207638 0.01207376 0.01201415 0.01218653] mean value: 0.013080430030822755 key: test_mcc value: [0.68473679 0.46254092 0.40396119 0.40546538 0.22652118 0.22652118 0.40924551 0.04619565 0.58466953 0.57341464] mean value: 0.4023271951558807 key: train_mcc value: [0.49785356 0.49888952 0.51074904 0.54301604 0.55354226 0.57817454 0.38753085 0.57786273 0.5169927 0.52357031] mean value: 0.5188181558712357 key: test_fscore value: [0.88 0.79166667 0.78431373 0.7755102 0.73076923 0.73076923 0.79245283 0.60869565 0.84615385 0.84 ] mean value: 0.7780331386293395 key: train_fscore value: [0.8125 0.81415929 0.8161435 0.83002208 0.82993197 0.83972912 0.78481013 0.83826879 0.81879195 0.82222222] mean value: 0.8206579045100307 key: test_precision value: [0.84615385 0.76 0.71428571 0.73076923 0.65517241 0.65517241 0.7 0.60869565 0.75862069 0.77777778] mean value: 0.7206647738401861 key: train_precision value: [0.75518672 0.75409836 0.76470588 0.76734694 0.78540773 0.79148936 0.69924812 0.7965368 0.76569038 0.76446281] mean value: 0.7644173094123847 key: test_recall value: [0.91666667 0.82608696 0.86956522 0.82608696 0.82608696 0.82608696 0.91304348 0.60869565 0.95652174 0.91304348] mean value: 0.8481884057971014 key: train_recall value: [0.87922705 0.88461538 0.875 0.90384615 0.87980769 0.89423077 0.89423077 0.88461538 0.87980769 0.88942308] mean value: 0.886480397621702 key: test_accuracy value: [0.85 0.74358974 0.71794872 0.71794872 0.64102564 0.64102564 0.71794872 0.53846154 0.79487179 0.79487179] mean value: 0.7157692307692307 key: train_accuracy value: [0.76068376 0.76136364 0.76704545 0.78125 0.78693182 0.79829545 0.71022727 0.79829545 0.76988636 0.77272727] mean value: 0.7706706487956487 key: test_roc_auc value: [0.83333333 0.72554348 0.68478261 0.69429348 0.60054348 0.60054348 0.67527174 0.52309783 0.75951087 0.76902174] mean value: 0.6865942028985508 key: train_roc_auc value: [0.73475242 0.73397436 0.74305556 0.75400641 0.76629274 0.7769765 0.66933761 0.77911325 0.7454594 0.74679487] mean value: 0.7449763099219622 key: test_jcc value: [0.78571429 0.65517241 0.64516129 0.63333333 0.57575758 0.57575758 0.65625 0.4375 0.73333333 0.72413793] mean value: 0.6422117739046271 key: train_jcc value: [0.68421053 0.68656716 0.68939394 0.70943396 0.70930233 0.72373541 0.64583333 0.72156863 0.69318182 0.69811321] mean value: 0.6961340312807993 MCC on Blind test: 0.3 MCC on Training: 0.4 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.55565834 1.47472739 1.56859636 1.56633496 1.41458726 1.52225161 1.58799076 1.37980938 1.86666608 1.80326724] mean value: 1.573988938331604 key: score_time value: [0.01608443 0.01618004 0.0161171 0.01581478 0.01520157 0.01573634 0.01520061 0.01601124 0.01517057 0.01772451] mean value: 0.01592411994934082 key: test_mcc value: [ 0.57774666 0.42319443 0.40546538 0.40546538 -0.01648451 0.18430245 0.3180697 0.07372098 0.35387166 0.4701087 ] mean value: 0.3195460823096499 key: train_mcc value: [0.894307 0.91196875 0.88227604 0.85272397 0.84198304 0.88227604 0.89545409 0.87642571 0.85860834 0.90001557] mean value: 0.879603855539942 key: test_fscore value: [0.84 0.75555556 0.7755102 0.7755102 0.59574468 0.69387755 0.71111111 0.65306122 0.75 0.7826087 ] mean value: 0.7332979226843374 key: train_fscore value: [0.95631068 0.96385542 0.95260664 0.94089835 0.9346247 0.95260664 0.95609756 0.95035461 0.94285714 0.95923261] mean value: 0.9509444341601506 key: test_precision value: [0.80769231 0.77272727 0.73076923 0.73076923 0.58333333 0.65384615 0.72727273 0.61538462 0.72 0.7826087 ] mean value: 0.7124403567447045 key: train_precision value: [0.96097561 0.96618357 0.93925234 0.9255814 0.94146341 0.93925234 0.97029703 0.93488372 0.93396226 0.9569378 ] mean value: 0.9468789481342714 key: test_recall value: [0.875 0.73913043 0.82608696 0.82608696 0.60869565 0.73913043 0.69565217 0.69565217 0.7826087 0.7826087 ] mean value: 0.7570652173913043 key: train_recall value: [0.95169082 0.96153846 0.96634615 0.95673077 0.92788462 0.96634615 0.94230769 0.96634615 0.95192308 0.96153846] mean value: 0.9552652359717577 key: test_accuracy value: [0.8 0.71794872 0.71794872 0.71794872 0.51282051 0.61538462 0.66666667 0.56410256 0.69230769 0.74358974] mean value: 0.6748717948717949 key: train_accuracy value: [0.94871795 0.95738636 0.94318182 0.92897727 0.92329545 0.94318182 0.94886364 0.94034091 0.93181818 0.95170455] mean value: 0.9417467948717949 key: test_roc_auc value: [0.78125 0.71331522 0.69429348 0.69429348 0.49184783 0.58831522 0.66032609 0.53532609 0.67255435 0.73505435] mean value: 0.6566576086956522 key: train_roc_auc value: [0.94806763 0.95646368 0.93803419 0.92280983 0.92227564 0.93803419 0.95032051 0.93456197 0.92735043 0.94951923] mean value: 0.9387437290969899 key: test_jcc value: [0.72413793 0.60714286 0.63333333 0.63333333 0.42424242 0.53125 0.55172414 0.48484848 0.6 0.64285714] mean value: 0.5832869644723093 key: train_jcc value: [0.91627907 0.93023256 0.90950226 0.88839286 0.87727273 0.90950226 0.91588785 0.90540541 0.89189189 0.92165899] mean value: 0.9066025871149141 MCC on Blind test: 0.17 MCC on Training: 0.32 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01370645 0.01334119 0.01010346 0.01066375 0.01070833 0.00998354 0.01058173 0.00891519 0.00897789 0.00881314] mean value: 0.010579466819763184 key: score_time value: [0.01211429 0.01067185 0.00960422 0.00930238 0.00963569 0.0084281 0.00931978 0.00837064 0.00830555 0.0082984 ] mean value: 0.009405088424682618 key: test_mcc value: [ 0.47916667 0.51604685 0.35087798 0.18430245 -0.12363384 -0.10425721 0.23457872 0.02786391 0.28811938 0.62641145] mean value: 0.247947634668017 key: train_mcc value: [0.25022258 0.26155892 0.25608328 0.28943552 0.30363583 0.29605659 0.28588998 0.29605659 0.27487631 0.23796051] mean value: 0.2751776100764225 key: test_fscore value: [0.79166667 0.81632653 0.77777778 0.69387755 0.55319149 0.6 0.72 0.625 0.74509804 0.85106383] mean value: 0.7174001884441721 key: train_fscore value: [0.72072072 0.71981777 0.71689498 0.73423423 0.73636364 0.73589165 0.72892938 0.73589165 0.72311213 0.71875 ] mean value: 0.7270606144964644 key: test_precision value: [0.79166667 0.76923077 0.67741935 0.65384615 0.54166667 0.55555556 0.66666667 0.6 0.67857143 0.83333333] mean value: 0.676795659537595 key: train_precision value: [0.67510549 0.68398268 0.6826087 0.69067797 0.69827586 0.69361702 0.69264069 0.69361702 0.68995633 0.67083333] mean value: 0.6871315093442533 key: test_recall value: [0.79166667 0.86956522 0.91304348 0.73913043 0.56521739 0.65217391 0.7826087 0.65217391 0.82608696 0.86956522] mean value: 0.7661231884057972 key: train_recall value: [0.77294686 0.75961538 0.75480769 0.78365385 0.77884615 0.78365385 0.76923077 0.78365385 0.75961538 0.77403846] mean value: 0.7720062244518766 key: test_accuracy value: [0.75 0.76923077 0.69230769 0.61538462 0.46153846 0.48717949 0.64102564 0.53846154 0.66666667 0.82051282] mean value: 0.6442307692307692 key: train_accuracy value: [0.64672365 0.65056818 0.64772727 0.66477273 0.67045455 0.66761364 0.66193182 0.66761364 0.65625 0.64204545] mean value: 0.657570091945092 key: test_roc_auc value: [0.73958333 0.74728261 0.64402174 0.58831522 0.4388587 0.45108696 0.61005435 0.51358696 0.63179348 0.80978261] mean value: 0.6174365942028986 key: train_roc_auc value: [0.61911232 0.62633547 0.62393162 0.6383547 0.64636752 0.64182692 0.63808761 0.64182692 0.63327991 0.61271368] mean value: 0.6321836677814939 key: test_jcc value: [0.65517241 0.68965517 0.63636364 0.53125 0.38235294 0.42857143 0.5625 0.45454545 0.59375 0.74074074] mean value: 0.5674901787604627 key: train_jcc value: [0.56338028 0.56227758 0.55871886 0.58007117 0.58273381 0.58214286 0.5734767 0.58214286 0.56630824 0.56097561] mean value: 0.5712227980576514 MCC on Blind test: 0.3 MCC on Training: 0.25 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0104425 0.01059461 0.01125097 0.01062703 0.01126981 0.0109067 0.01115656 0.0110414 0.01114416 0.01153994] mean value: 0.010997366905212403 key: score_time value: [0.01002336 0.00873208 0.00914764 0.0096221 0.00969219 0.00967264 0.00968671 0.00940061 0.00973582 0.00983477] mean value: 0.009554791450500488 key: test_mcc value: [ 0.47916667 0.57608696 0.4121128 0.36413043 -0.18947459 -0.25815217 0.35387166 -0.06434895 0.19781414 0.63344389] mean value: 0.2504650840035721 key: train_mcc value: [0.36325932 0.36107589 0.3859576 0.3396775 0.38262017 0.4013277 0.35445486 0.40715749 0.35796919 0.3394312 ] mean value: 0.36929309097229734 key: test_fscore value: [0.79166667 0.82608696 0.76595745 0.73913043 0.54166667 0.35897436 0.75 0.48780488 0.68085106 0.84444444] mean value: 0.6786582916743563 key: train_fscore value: [0.73218673 0.72906404 0.73762376 0.72506083 0.73316708 0.76235294 0.72727273 0.75662651 0.73170732 0.71782178] mean value: 0.7352883717241393 key: test_precision value: [0.79166667 0.82608696 0.75 0.73913043 0.52 0.4375 0.72 0.55555556 0.66666667 0.86363636] mean value: 0.68702426438296 key: train_precision value: [0.745 0.74747475 0.76020408 0.73399015 0.76165803 0.74654378 0.74371859 0.75845411 0.74257426 0.73979592] mean value: 0.7479413661818685 key: test_recall value: [0.79166667 0.82608696 0.7826087 0.73913043 0.56521739 0.30434783 0.7826087 0.43478261 0.69565217 0.82608696] mean value: 0.6748188405797101 key: train_recall value: [0.71980676 0.71153846 0.71634615 0.71634615 0.70673077 0.77884615 0.71153846 0.75480769 0.72115385 0.69711538] mean value: 0.72342298402081 key: test_accuracy value: [0.75 0.79487179 0.71794872 0.69230769 0.43589744 0.35897436 0.69230769 0.46153846 0.61538462 0.82051282] mean value: 0.6339743589743588 key: train_accuracy value: [0.68945869 0.6875 0.69886364 0.67897727 0.69602273 0.71306818 0.68465909 0.71306818 0.6875 0.67613636] mean value: 0.6925254144004144 key: test_roc_auc value: [0.73958333 0.78804348 0.70380435 0.68206522 0.4076087 0.37092391 0.67255435 0.4673913 0.59782609 0.81929348] mean value: 0.6249094202898551 key: train_roc_auc value: [0.68282005 0.68215812 0.69497863 0.67067308 0.69364316 0.69845085 0.6786859 0.70379274 0.68002137 0.67147436] mean value: 0.6856698253437383 key: test_jcc value: [0.65517241 0.7037037 0.62068966 0.5862069 0.37142857 0.21875 0.6 0.32258065 0.51612903 0.73076923] mean value: 0.5325430148838103 key: train_jcc value: [0.57751938 0.57364341 0.58431373 0.56870229 0.57874016 0.61596958 0.57142857 0.60852713 0.57692308 0.55984556] mean value: 0.5815612885473724 MCC on Blind test: 0.39 MCC on Training: 0.25 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.011585 0.01471496 0.01525688 0.01548576 0.01779652 0.01736188 0.01714301 0.0174458 0.01837444 0.01717544] mean value: 0.01623396873474121 key: score_time value: [0.00993705 0.01202393 0.01207542 0.01211286 0.01217437 0.01226163 0.01210308 0.01199889 0.01246643 0.01236367] mean value: 0.011951732635498046 key: test_mcc value: [ 0.46291005 0.3180697 0.35087798 0.21294497 0.22227711 0.09066482 0.19449665 -0.11014178 0.42370171 0.56305327] mean value: 0.27288544813868587 key: train_mcc value: [0.3388472 0.43713027 0.38685298 0.38651842 0.47667116 0.51458736 0.21483446 0.44411559 0.31384168 0.41025641] mean value: 0.39236555481318136 key: test_fscore value: [0.81355932 0.71111111 0.77777778 0.66666667 0.75 0.63829787 0.75409836 0.68965517 0.51612903 0.7804878 ] mean value: 0.7097783120135523 key: train_fscore value: [0.77992278 0.76811594 0.791423 0.78297872 0.81451613 0.80660377 0.75836431 0.80566802 0.38461538 0.68181818] mean value: 0.7374026244818059 key: test_precision value: [0.68571429 0.72727273 0.67741935 0.68181818 0.63636364 0.625 0.60526316 0.57142857 1. 0.88888889] mean value: 0.7099168804219739 key: train_precision value: [0.64951768 0.77184466 0.66557377 0.70229008 0.70138889 0.79166667 0.61818182 0.6958042 0.96153846 0.83333333] mean value: 0.739113955632268 key: test_recall value: [1. 0.69565217 0.91304348 0.65217391 0.91304348 0.65217391 1. 0.86956522 0.34782609 0.69565217] mean value: 0.7739130434782607 key: train_recall value: [0.97584541 0.76442308 0.97596154 0.88461538 0.97115385 0.82211538 0.98076923 0.95673077 0.24038462 0.57692308] mean value: 0.8148922333704942 key: test_accuracy value: [0.725 0.66666667 0.69230769 0.61538462 0.64102564 0.56410256 0.61538462 0.53846154 0.61538462 0.76923077] mean value: 0.6442948717948718 key: train_accuracy value: [0.67521368 0.72727273 0.69602273 0.71022727 0.73863636 0.76704545 0.63068182 0.72727273 0.54545455 0.68181818] mean value: 0.6899645493395493 key: test_roc_auc value: [0.65625 0.66032609 0.64402174 0.60733696 0.58152174 0.54483696 0.53125 0.46603261 0.67391304 0.78532609] mean value: 0.6150815217391303 key: train_roc_auc value: [0.60945048 0.71901709 0.6338141 0.67147436 0.68696581 0.75480769 0.55288462 0.67628205 0.61324786 0.70512821] mean value: 0.6623072277963582 key: test_jcc value: [0.68571429 0.55172414 0.63636364 0.5 0.6 0.46875 0.60526316 0.52631579 0.34782609 0.64 ] mean value: 0.5561957094333898 key: train_jcc value: [0.63924051 0.62352941 0.65483871 0.64335664 0.68707483 0.67588933 0.61077844 0.67457627 0.23809524 0.51724138] mean value: 0.5964620760828893 MCC on Blind test: 0.43 MCC on Training: 0.27 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02360845 0.0267694 0.02752304 0.02772975 0.02685571 0.02799797 0.02684093 0.02720046 0.03485942 0.02709675] mean value: 0.027648186683654784 key: score_time value: [0.01267385 0.01251411 0.01868725 0.01322317 0.01277518 0.01319027 0.01325345 0.01255679 0.0134871 0.01273227] mean value: 0.013509345054626466 key: test_mcc value: [ 0.08574929 -0.04514469 0.15048232 0.34610933 -0.11014178 0.27875205 0.23350057 0.04241879 0.15048232 -0.11014178] mean value: 0.10220664060020068 key: train_mcc value: [0.28682149 0.31777256 0.26226526 0.26226526 0.28708143 0.27074003 0.26226526 0.30272346 0.29498259 0.27900593] mean value: 0.28259232807332313 key: test_fscore value: [0.72413793 0.71186441 0.74576271 0.77966102 0.68965517 0.76666667 0.75862069 0.73333333 0.74576271 0.68965517] mean value: 0.7345119812974867 key: train_fscore value: [0.76808905 0.77467412 0.76470588 0.76470588 0.7689464 0.76611418 0.76470588 0.77179963 0.77037037 0.76752768] mean value: 0.7681639066950603 key: test_precision value: [0.61764706 0.58333333 0.61111111 0.63888889 0.57142857 0.62162162 0.62857143 0.59459459 0.61111111 0.57142857] mean value: 0.6049736290912762 key: train_precision value: [0.62349398 0.63221884 0.61904762 0.61904762 0.62462462 0.62089552 0.61904762 0.62839879 0.62650602 0.62275449] mean value: 0.6236035131699094 key: test_recall value: [0.875 0.91304348 0.95652174 1. 0.86956522 1. 0.95652174 0.95652174 0.95652174 0.86956522] mean value: 0.9353260869565219 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.56410256 0.61538462 0.66666667 0.53846154 0.64102564 0.64102564 0.58974359 0.61538462 0.53846154] mean value: 0.601025641025641 key: train_accuracy value: [0.64387464 0.65625 0.63636364 0.63636364 0.64488636 0.63920455 0.63636364 0.65056818 0.64772727 0.64204545] mean value: 0.6433647371147371 key: test_roc_auc value: [0.53125 0.48777174 0.54076087 0.59375 0.46603261 0.5625 0.57201087 0.50951087 0.54076087 0.46603261] mean value: 0.5270380434782609 key: train_roc_auc value: [0.56597222 0.57986111 0.55555556 0.55555556 0.56597222 0.55902778 0.55555556 0.57291667 0.56944444 0.5625 ] mean value: 0.5642361111111112 key: test_jcc value: [0.56756757 0.55263158 0.59459459 0.63888889 0.52631579 0.62162162 0.61111111 0.57894737 0.59459459 0.52631579] mean value: 0.5812588904694168 key: train_jcc value: [0.62349398 0.63221884 0.61904762 0.61904762 0.62462462 0.62089552 0.61904762 0.62839879 0.62650602 0.62275449] mean value: 0.6236035131699094 MCC on Blind test: -0.15 MCC on Training: 0.1 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.62003708 0.63112044 0.64581919 0.71403694 0.62803435 0.67773151 0.64243579 0.64349508 0.63946152 0.65098095] mean value: 0.6493152856826783 key: score_time value: [0.14536524 0.18122482 0.17330027 0.18778086 0.1533854 0.16320586 0.18459392 0.24731398 0.21334291 0.14648986] mean value: 0.17960031032562257 key: test_mcc value: [ 0.73786479 0.57121017 0.46046933 0.57341464 0.22652118 -0.01224439 0.63344389 0.22652118 0.68206522 0.57341464] mean value: 0.46726806395764486 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89795918 0.83333333 0.8 0.84 0.73076923 0.65384615 0.84444444 0.73076923 0.86956522 0.84 ] mean value: 0.8040686794227166 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88 0.8 0.74074074 0.77777778 0.65517241 0.5862069 0.86363636 0.65517241 0.86956522 0.77777778] mean value: 0.7606049601461895 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.91666667 0.86956522 0.86956522 0.91304348 0.82608696 0.73913043 0.82608696 0.82608696 0.86956522 0.91304348] mean value: 0.8568840579710144 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.79487179 0.74358974 0.79487179 0.64102564 0.53846154 0.82051282 0.64102564 0.84615385 0.79487179] mean value: 0.7490384615384615 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.86458333 0.77853261 0.71603261 0.76902174 0.60054348 0.49456522 0.81929348 0.60054348 0.84103261 0.76902174] mean value: 0.7253170289855072 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.81481481 0.71428571 0.66666667 0.72413793 0.57575758 0.48571429 0.73076923 0.57575758 0.76923077 0.72413793] mean value: 0.6781272495065599 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.45 MCC on Training: 0.47 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.98313069 1.00086451 0.95221877 0.93742323 0.96730828 0.96432877 0.9655242 0.98007512 1.04809356 0.95873404] mean value: 0.975770115852356 key: score_time value: [0.25847936 0.19192767 0.16687059 0.24523306 0.20921516 0.22552514 0.24136567 0.20772886 0.255867 0.23978019] mean value: 0.2241992712020874 key: test_mcc value: [0.73786479 0.67987942 0.46046933 0.46528674 0.28552012 0.12163341 0.57121017 0.28811938 0.70405231 0.57341464] mean value: 0.4887450303578501 key: train_mcc value: [0.8348636 0.82904059 0.85288887 0.86477206 0.85860834 0.85288887 0.84100568 0.86450182 0.83499081 0.8468576 ] mean value: 0.8480418248287661 key: test_fscore value: [0.89795918 0.875 0.8 0.80769231 0.75471698 0.68 0.83333333 0.74509804 0.88461538 0.84 ] mean value: 0.8118415229662256 key: train_fscore value: [0.93396226 0.93111639 0.94117647 0.94588235 0.94285714 0.94117647 0.93647059 0.94536817 0.93396226 0.93867925] mean value: 0.9390651359365062 key: test_precision value: [0.88 0.84 0.74074074 0.72413793 0.66666667 0.62962963 0.8 0.67857143 0.79310345 0.77777778] mean value: 0.7530627622696588 key: train_precision value: [0.9124424 0.92018779 0.92165899 0.92626728 0.93396226 0.92165899 0.91705069 0.9342723 0.91666667 0.9212963 ] mean value: 0.9225463662024443 key: test_recall value: [0.91666667 0.91304348 0.86956522 0.91304348 0.86956522 0.73913043 0.86956522 0.82608696 1. 0.91304348] mean value: 0.8829710144927535 key: train_recall value: [0.95652174 0.94230769 0.96153846 0.96634615 0.95192308 0.96153846 0.95673077 0.95673077 0.95192308 0.95673077] mean value: 0.9562290969899665 key: test_accuracy value: [0.875 0.84615385 0.74358974 0.74358974 0.66666667 0.58974359 0.79487179 0.66666667 0.84615385 0.79487179] mean value: 0.7567307692307692 key: train_accuracy value: [0.92022792 0.91761364 0.92897727 0.93465909 0.93181818 0.92897727 0.92329545 0.93465909 0.92045455 0.92613636] mean value: 0.9266818829318829 key: test_roc_auc value: [0.86458333 0.83152174 0.71603261 0.70652174 0.62228261 0.55706522 0.77853261 0.63179348 0.8125 0.76902174] mean value: 0.7289855072463769 key: train_roc_auc value: [0.91228865 0.91212607 0.92174145 0.92761752 0.92735043 0.92174145 0.91586538 0.92975427 0.91346154 0.91933761] mean value: 0.9201284373838721 key: test_jcc value: [0.81481481 0.77777778 0.66666667 0.67741935 0.60606061 0.51515152 0.71428571 0.59375 0.79310345 0.72413793] mean value: 0.6883167828906149 key: train_jcc value: [0.87610619 0.87111111 0.88888889 0.89732143 0.89189189 0.88888889 0.88053097 0.8963964 0.87610619 0.88444444] mean value: 0.885168641302491 MCC on Blind test: 0.45 MCC on Training: 0.49 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04122138 0.03764701 0.04018736 0.03957391 0.03750706 0.03657317 0.03675318 0.03727436 0.0381701 0.0518887 ] mean value: 0.03967962265014648 key: score_time value: [0.02406836 0.0207305 0.02535534 0.02122974 0.02152777 0.02303147 0.02045155 0.02168941 0.0220015 0.03030539] mean value: 0.02303910255432129 key: test_mcc value: [0.63192977 0.4121128 0.51604685 0.34752402 0.12163341 0.07372098 0.51926212 0.07372098 0.40396119 0.51604685] mean value: 0.36159589563612315 key: train_mcc value: [0.52840035 0.59675123 0.5960559 0.60819778 0.59032101 0.62033966 0.60208379 0.6202352 0.59601623 0.56575684] mean value: 0.5924157980473695 key: test_fscore value: [0.8627451 0.76595745 0.81632653 0.76 0.68 0.65306122 0.80851064 0.65306122 0.78431373 0.81632653] mean value: 0.7600302418839876 key: train_fscore value: [0.81922197 0.84684685 0.84474886 0.84931507 0.83990719 0.85388128 0.84668192 0.85185185 0.8440367 0.83446712] mean value: 0.8430958804342852 key: test_precision value: [0.81481481 0.75 0.76923077 0.7037037 0.62962963 0.61538462 0.79166667 0.61538462 0.71428571 0.76923077] mean value: 0.7173331298331298 key: train_precision value: [0.77826087 0.79661017 0.80434783 0.80869565 0.81165919 0.81304348 0.80786026 0.82142857 0.80701754 0.78969957] mean value: 0.8038623136515998 key: test_recall value: [0.91666667 0.7826087 0.86956522 0.82608696 0.73913043 0.69565217 0.82608696 0.69565217 0.86956522 0.86956522] mean value: 0.8090579710144927 key: train_recall value: [0.8647343 0.90384615 0.88942308 0.89423077 0.87019231 0.89903846 0.88942308 0.88461538 0.88461538 0.88461538] mean value: 0.886473429951691 key: test_accuracy value: [0.825 0.71794872 0.76923077 0.69230769 0.58974359 0.56410256 0.76923077 0.56410256 0.71794872 0.76923077] mean value: 0.6978846153846154 key: train_accuracy value: [0.77492877 0.80681818 0.80681818 0.8125 0.80397727 0.81818182 0.80965909 0.81818182 0.80681818 0.79261364] mean value: 0.8050496956746956 key: test_roc_auc value: [0.80208333 0.70380435 0.74728261 0.66304348 0.55706522 0.53532609 0.75679348 0.53532609 0.68478261 0.74728261] mean value: 0.6732789855072465 key: train_roc_auc value: [0.75528382 0.78525641 0.78846154 0.79433761 0.78926282 0.80021368 0.79193376 0.8034188 0.78952991 0.7721688 ] mean value: 0.7869867149758454 key: test_jcc value: [0.75862069 0.62068966 0.68965517 0.61290323 0.51515152 0.48484848 0.67857143 0.48484848 0.64516129 0.68965517] mean value: 0.6180105119204118 key: train_jcc value: [0.69379845 0.734375 0.7312253 0.73809524 0.724 0.74501992 0.73412698 0.74193548 0.73015873 0.71595331] mean value: 0.7288688410018732 MCC on Blind test: 0.23 MCC on Training: 0.36 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.11034203 0.08105779 0.05493569 0.1077559 0.10695839 0.12658381 0.04992938 0.08276534 0.12177968 0.11079907] mean value: 0.09529070854187012 key: score_time value: [0.02318954 0.01232338 0.01232219 0.02267122 0.01798582 0.02198195 0.012362 0.01636863 0.01991177 0.02285552] mean value: 0.01819720268249512 key: test_mcc value: [0.68473679 0.46254092 0.40396119 0.46254092 0.22652118 0.22652118 0.62641145 0.13653316 0.58466953 0.519803 ] mean value: 0.4334239303017696 key: train_mcc value: [0.50382261 0.51110435 0.5169927 0.54850223 0.55354226 0.57181053 0.5354446 0.58400029 0.51687518 0.52357031] mean value: 0.5365665078599979 key: test_fscore value: [0.88 0.79166667 0.78431373 0.79166667 0.73076923 0.73076923 0.85106383 0.66666667 0.84615385 0.82352941] mean value: 0.7896599274734444 key: train_fscore value: [0.81348315 0.81777778 0.81879195 0.83111111 0.82993197 0.83636364 0.82511211 0.84090909 0.81797753 0.82222222] mean value: 0.8253680539262301 key: test_precision value: [0.84615385 0.76 0.71428571 0.76 0.65517241 0.65517241 0.83333333 0.64 0.75862069 0.75 ] mean value: 0.7372738411014274 key: train_precision value: [0.7605042 0.76033058 0.76569038 0.77272727 0.78540773 0.79310345 0.77310924 0.79741379 0.76793249 0.76446281] mean value: 0.7740681939256889 key: test_recall value: [0.91666667 0.82608696 0.86956522 0.82608696 0.82608696 0.82608696 0.86956522 0.69565217 0.95652174 0.91304348] mean value: 0.852536231884058 key: train_recall value: [0.87439614 0.88461538 0.87980769 0.89903846 0.87980769 0.88461538 0.88461538 0.88942308 0.875 0.88942308] mean value: 0.8840742289111854 key: test_accuracy value: [0.85 0.74358974 0.71794872 0.74358974 0.64102564 0.64102564 0.82051282 0.58974359 0.79487179 0.76923077] mean value: 0.7311538461538462 key: train_accuracy value: [0.76353276 0.76704545 0.76988636 0.78409091 0.78693182 0.79545455 0.77840909 0.80113636 0.76988636 0.77272727] mean value: 0.7789100945350944 key: test_roc_auc value: [0.83333333 0.72554348 0.68478261 0.72554348 0.60054348 0.60054348 0.80978261 0.56657609 0.75951087 0.73777174] mean value: 0.704393115942029 key: train_roc_auc value: [0.7392814 0.7409188 0.7454594 0.75854701 0.76629274 0.77564103 0.75480769 0.78151709 0.74652778 0.74679487] mean value: 0.7555787811222594 key: test_jcc value: [0.78571429 0.65517241 0.64516129 0.65517241 0.57575758 0.57575758 0.74074074 0.5 0.73333333 0.7 ] mean value: 0.6566809629212299 key: train_jcc value: [0.68560606 0.69172932 0.69318182 0.71102662 0.70930233 0.71875 0.70229008 0.7254902 0.69201521 0.69811321] mean value: 0.7027504832734082 MCC on Blind test: 0.3 MCC on Training: 0.43 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02395463 0.01701307 0.01757646 0.01991796 0.01909781 0.01883769 0.01613355 0.01880169 0.01846266 0.01684713] mean value: 0.01866426467895508 key: score_time value: [0.01566315 0.01194358 0.01225376 0.01203918 0.01225352 0.01122355 0.0110023 0.0122478 0.01186371 0.01196504] mean value: 0.012245559692382812 key: test_mcc value: [0.63192977 0.63564481 0.46046933 0.34590396 0.10713839 0.10713839 0.46528674 0.01741494 0.22178137 0.63564481] mean value: 0.362835252554748 key: train_mcc value: [0.54198461 0.507632 0.50942688 0.52953924 0.58659385 0.60582546 0.54522419 0.56890218 0.53701872 0.53961703] mean value: 0.5471764149379519 key: test_fscore value: [0.8627451 0.8627451 0.8 0.76923077 0.69230769 0.69230769 0.80769231 0.69090909 0.74074074 0.8627451 ] mean value: 0.7781423587305939 key: train_fscore value: [0.83189655 0.81995662 0.82150538 0.82832618 0.84513274 0.85209713 0.83224401 0.83956044 0.82819383 0.83227176] mean value: 0.8331184641065675 key: test_precision value: [0.81481481 0.78571429 0.74074074 0.68965517 0.62068966 0.62068966 0.72413793 0.59375 0.64516129 0.78571429] mean value: 0.7021067831099812 key: train_precision value: [0.75097276 0.74703557 0.74319066 0.74806202 0.78278689 0.7877551 0.76095618 0.77327935 0.76422764 0.74524715] mean value: 0.7603513318128559 key: test_recall value: [0.91666667 0.95652174 0.86956522 0.86956522 0.7826087 0.7826087 0.91304348 0.82608696 0.86956522 0.95652174] mean value: 0.8742753623188406 key: train_recall value: [0.93236715 0.90865385 0.91826923 0.92788462 0.91826923 0.92788462 0.91826923 0.91826923 0.90384615 0.94230769] mean value: 0.9216020995912299 key: test_accuracy value: [0.825 0.82051282 0.74358974 0.69230769 0.58974359 0.58974359 0.74358974 0.56410256 0.64102564 0.82051282] mean value: 0.7030128205128205 key: train_accuracy value: [0.77777778 0.76420455 0.76420455 0.77272727 0.80113636 0.80965909 0.78125 0.79261364 0.77840909 0.77556818] mean value: 0.7817550505050505 key: test_roc_auc value: [0.80208333 0.79076087 0.71603261 0.65353261 0.54755435 0.54755435 0.70652174 0.50679348 0.59103261 0.79076087] mean value: 0.6652626811594204 key: train_roc_auc value: [0.74396135 0.7321047 0.72996795 0.73824786 0.77510684 0.78338675 0.75080128 0.76469017 0.75053419 0.73851496] mean value: 0.7507316053511706 key: test_jcc value: [0.75862069 0.75862069 0.66666667 0.625 0.52941176 0.52941176 0.67741935 0.52777778 0.58823529 0.75862069] mean value: 0.6419784691778083 key: train_jcc value: [0.71217712 0.69485294 0.69708029 0.70695971 0.73180077 0.74230769 0.71268657 0.72348485 0.70676692 0.71272727] mean value: 0.714084412613895 MCC on Blind test: 0.09 MCC on Training: 0.36 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0192275 0.01656246 0.01974559 0.02245927 0.01812124 0.01632333 0.02373052 0.02194309 0.01803041 0.01887774] mean value: 0.01950211524963379 key: score_time value: [0.00894403 0.01115704 0.01120567 0.01184511 0.01217365 0.01176906 0.01179147 0.01185417 0.01177645 0.01633453] mean value: 0.011885118484497071 key: test_mcc value: [ 0.52297636 0.23350057 0.37771739 0.35564338 0.30385418 0.06668313 0.51926212 -0.23350057 0.40532174 0.36860489] mean value: 0.29200632047772407 key: train_mcc value: [0.4857752 0.29707575 0.54376604 0.31378597 0.5314777 0.35262777 0.52936902 0.41824003 0.29707575 0.33679527] mean value: 0.41059884870646685 key: test_fscore value: [0.82352941 0.75862069 0.66666667 0.4137931 0.77192982 0.27586207 0.80851064 0.07407407 0.79310345 0.61111111] mean value: 0.5997201036820663 key: train_fscore value: [0.81449893 0.77212806 0.74787535 0.3671875 0.83018868 0.46931408 0.82326622 0.50896057 0.77212806 0.49477352] mean value: 0.660032097908438 key: test_precision value: [0.77777778 0.62857143 0.8125 1. 0.64705882 0.66666667 0.79166667 0.25 0.65714286 0.84615385] mean value: 0.7077538066508655 key: train_precision value: [0.72900763 0.63467492 0.91034483 0.97916667 0.73605948 0.94202899 0.76987448 1. 0.63467492 0.89873418] mean value: 0.8234566092305686 key: test_recall value: [0.875 0.95652174 0.56521739 0.26086957 0.95652174 0.17391304 0.82608696 0.04347826 1. 0.47826087] mean value: 0.6135869565217391 key: train_recall value: [0.92270531 0.98557692 0.63461538 0.22596154 0.95192308 0.3125 0.88461538 0.34134615 0.98557692 0.34134615] mean value: 0.65861668524712 key: test_accuracy value: [0.775 0.64102564 0.66666667 0.56410256 0.66666667 0.46153846 0.76923077 0.35897436 0.69230769 0.64102564] mean value: 0.6236538461538462 key: train_accuracy value: [0.75213675 0.65625 0.74715909 0.53977273 0.76988636 0.58238636 0.77556818 0.61079545 0.65625 0.58806818] mean value: 0.6678273115773116 key: test_roc_auc value: [0.75 0.57201087 0.6888587 0.63043478 0.60326087 0.52445652 0.75679348 0.42798913 0.625 0.67663043] mean value: 0.6255434782608695 key: train_roc_auc value: [0.71482488 0.58306624 0.7721688 0.60950855 0.72943376 0.64236111 0.75133547 0.67067308 0.58306624 0.6428953 ] mean value: 0.66993334262356 key: test_jcc value: [0.7 0.61111111 0.5 0.26086957 0.62857143 0.16 0.67857143 0.03846154 0.65714286 0.44 ] mean value: 0.4674727929075756 key: train_jcc value: [0.68705036 0.62883436 0.59728507 0.22488038 0.70967742 0.30660377 0.69961977 0.34134615 0.62883436 0.3287037 ] mean value: 0.5152835344369814 MCC on Blind test: 0.37 MCC on Training: 0.29 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.14393139 0.09807372 0.10351205 0.10863495 0.10327053 0.09885406 0.09057808 0.09386349 0.0951519 0.09470105] mean value: 0.10305712223052979 key: score_time value: [0.01131892 0.01092863 0.01100683 0.01209784 0.01085114 0.01087832 0.0113523 0.010885 0.01138663 0.01176763] mean value: 0.01124732494354248 key: test_mcc value: [0.68473679 0.52831916 0.33426762 0.42319443 0.09066482 0.23457872 0.68206522 0.23457872 0.73356078 0.69175116] mean value: 0.46377173956764617 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88 0.8 0.69767442 0.75555556 0.63829787 0.72 0.86956522 0.72 0.89361702 0.86363636] mean value: 0.7838346448804895 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84615385 0.81818182 0.75 0.77272727 0.625 0.66666667 0.86956522 0.66666667 0.875 0.9047619 ] mean value: 0.7794723392549481 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.91666667 0.7826087 0.65217391 0.73913043 0.65217391 0.7826087 0.86956522 0.7826087 0.91304348 0.82608696] mean value: 0.7916666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85 0.76923077 0.66666667 0.71794872 0.56410256 0.64102564 0.84615385 0.64102564 0.87179487 0.84615385] mean value: 0.7414102564102565 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.76630435 0.66983696 0.71331522 0.54483696 0.61005435 0.84103261 0.61005435 0.86277174 0.85054348] mean value: 0.7302083333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.78571429 0.66666667 0.53571429 0.60714286 0.46875 0.5625 0.76923077 0.5625 0.80769231 0.76 ] mean value: 0.6525911172161172 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.46 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... dingBackend)}( nesting_levelKinner_max_num_threadsNubNN}tRsbargs)kwargs} loky_pickler cloudpickleu[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... dingBackend)}( ne1;i;ix_num_threadsNubNN}tRsbargs)kwargs} loky_p;i;i[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... @AFP?4և?J\@BCuJ?ȫ?6T@4T@DEN??@[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.5s remaining: 2.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.5s remaining: 2.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.5s remaining: 3.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.6s remaining: 3.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.6s remaining: 3.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.6s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.6s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.6s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.18655252 0.18357849 0.18973684 0.19232965 0.19326425 0.18887925 0.18397546 0.18180537 0.17860317 0.18103576] mean value: 0.18597607612609862 key: score_time value: [0.01596594 0.017102 0.0167594 0.01628065 0.01669693 0.01845765 0.01524949 0.01575398 0.01522946 0.01649475] mean value: 0.016399025917053223 key: test_mcc value: [0.31876614 0.40398551 0.52623481 0.82608696 0.56736651 0.48566186 0.3927922 0.26726124 0.34815531 0.56736651] mean value: 0.47036770687223467 key: train_mcc value: [0.87507266 0.91808152 0.84135588 0.84158934 0.86066645 0.85105006 0.82726733 0.88498366 0.82692308 0.84158934] mean value: 0.8568579328088273 key: test_fscore value: [0.63636364 0.70833333 0.74418605 0.91304348 0.79166667 0.71428571 0.70833333 0.58536585 0.68085106 0.79166667] mean value: 0.7274095792910172 key: train_fscore value: [0.93658537 0.95883777 0.92048193 0.91970803 0.92978208 0.92493947 0.91219512 0.94146341 0.91346154 0.91970803] mean value: 0.9277162749039466 key: test_precision value: [0.66666667 0.70833333 0.8 0.91304348 0.76 0.78947368 0.68 0.66666667 0.66666667 0.76 ] mean value: 0.7410850495804729 key: train_precision value: [0.95049505 0.96116505 0.92270531 0.93103448 0.93658537 0.93170732 0.92574257 0.95544554 0.91346154 0.93103448] mean value: 0.9359376717775791 key: test_recall value: [0.60869565 0.70833333 0.69565217 0.91304348 0.82608696 0.65217391 0.73913043 0.52173913 0.69565217 0.82608696] mean value: 0.718659420289855 key: train_recall value: [0.92307692 0.95652174 0.91826923 0.90865385 0.92307692 0.91826923 0.89903846 0.92788462 0.91346154 0.90865385] mean value: 0.9196906354515051 key: test_accuracy value: [0.65957447 0.70212766 0.76086957 0.91304348 0.7826087 0.73913043 0.69565217 0.63043478 0.67391304 0.7826087 ] mean value: 0.7339962997224793 key: train_accuracy value: [0.9373494 0.95903614 0.92067308 0.92067308 0.93028846 0.92548077 0.91346154 0.94230769 0.91346154 0.92067308] mean value: 0.9283404772937904 key: test_roc_auc value: [0.65851449 0.70199275 0.76086957 0.91304348 0.7826087 0.73913043 0.69565217 0.63043478 0.67391304 0.7826087 ] mean value: 0.7338768115942029 key: train_roc_auc value: [0.93738387 0.9590301 0.92067308 0.92067308 0.93028846 0.92548077 0.91346154 0.94230769 0.91346154 0.92067308] mean value: 0.928343320327016 key: test_jcc value: [0.46666667 0.5483871 0.59259259 0.84 0.65517241 0.55555556 0.5483871 0.4137931 0.51612903 0.65517241] mean value: 0.5791855971655748 key: train_jcc value: [0.88073394 0.92093023 0.85267857 0.85135135 0.86877828 0.86036036 0.83856502 0.88940092 0.84070796 0.85135135] mean value: 0.8654858001229171 MCC on Blind test: 0.24 MCC on Training: 0.47 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.24306226 0.2893157 0.29926133 0.30903625 0.27538347 0.29948163 0.27903557 0.24341416 0.31551313 0.28881693] mean value: 0.2842320442199707 key: score_time value: [0.04542184 0.07954311 0.08310056 0.058604 0.04283762 0.05492973 0.06714153 0.04820609 0.07758927 0.05979371] mean value: 0.061716747283935544 key: test_mcc value: [0.36699609 0.40398551 0.73913043 0.74194083 0.73913043 0.57396402 0.43852901 0.3927922 0.35082321 0.65217391] mean value: 0.5399465646603201 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.63414634 0.70833333 0.86956522 0.875 0.86956522 0.76190476 0.69767442 0.68181818 0.65116279 0.82608696] mean value: 0.7575257219126366 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.72222222 0.70833333 0.86956522 0.84 0.86956522 0.84210526 0.75 0.71428571 0.7 0.82608696] mean value: 0.7842163924303514 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.56521739 0.70833333 0.86956522 0.91304348 0.86956522 0.69565217 0.65217391 0.65217391 0.60869565 0.82608696] mean value: 0.7360507246376812 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.68085106 0.70212766 0.86956522 0.86956522 0.86956522 0.7826087 0.7173913 0.69565217 0.67391304 0.82608696] mean value: 0.7687326549491211 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.67844203 0.70199275 0.86956522 0.86956522 0.86956522 0.7826087 0.7173913 0.69565217 0.67391304 0.82608696] mean value: 0.7684782608695652 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.46428571 0.5483871 0.76923077 0.77777778 0.76923077 0.61538462 0.53571429 0.51724138 0.48275862 0.7037037 ] mean value: 0.6183714732101828 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.54 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04420304 0.03033781 0.02911472 0.02327442 0.02925849 0.02678967 0.02476597 0.02580643 0.02592611 0.02680779] mean value: 0.028628444671630858 key: score_time value: [0.01033163 0.01019883 0.00906682 0.00869608 0.00890613 0.00876045 0.00895572 0.00886106 0.00899482 0.00914621] mean value: 0.009191775321960449 key: test_mcc value: [0.27586252 0.23593505 0.56521739 0.4454354 0.57396402 0.52223297 0.31526414 0.3927922 0.48007936 0.43519414] mean value: 0.4241977205056256 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.62222222 0.60869565 0.7826087 0.68292683 0.8 0.75555556 0.6 0.68181818 0.72727273 0.71111111] mean value: 0.6972210975074178 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.63636364 0.63636364 0.7826087 0.77777778 0.74074074 0.77272727 0.70588235 0.71428571 0.76190476 0.72727273] mean value: 0.7255927316029618 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.60869565 0.58333333 0.7826087 0.60869565 0.86956522 0.73913043 0.52173913 0.65217391 0.69565217 0.69565217] mean value: 0.6757246376811594 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.63829787 0.61702128 0.7826087 0.7173913 0.7826087 0.76086957 0.65217391 0.69565217 0.73913043 0.7173913 ] mean value: 0.7103145235892692 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.63768116 0.61775362 0.7826087 0.7173913 0.7826087 0.76086957 0.65217391 0.69565217 0.73913043 0.7173913 ] mean value: 0.7103260869565218 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4516129 0.4375 0.64285714 0.51851852 0.66666667 0.60714286 0.42857143 0.51724138 0.57142857 0.55172414] mean value: 0.5393263605652371 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.42 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00978446 0.01097965 0.00996351 0.01028752 0.00940895 0.00941777 0.01064324 0.00948477 0.01060796 0.00940275] mean value: 0.009998059272766114 key: score_time value: [0.00951838 0.00929594 0.0091939 0.00843453 0.00937819 0.00864697 0.0092597 0.00842643 0.00840139 0.00933838] mean value: 0.008989381790161132 key: test_mcc value: [0.36116212 0.28051421 0.26111648 0.52223297 0.30434783 0.4454354 0.17407766 0.31526414 0.39130435 0.4454354 ] mean value: 0.350089055812119 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.62222222 0.63829787 0.76595745 0.65217391 0.68292683 0.59574468 0.6 0.69565217 0.68292683] mean value: 0.6602568634381994 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.68181818 0.66666667 0.625 0.75 0.65217391 0.77777778 0.58333333 0.70588235 0.69565217 0.77777778] mean value: 0.6916082177271435 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.65217391 0.58333333 0.65217391 0.7826087 0.65217391 0.60869565 0.60869565 0.52173913 0.69565217 0.60869565] mean value: 0.6365942028985507 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.68085106 0.63829787 0.63043478 0.76086957 0.65217391 0.7173913 0.58695652 0.65217391 0.69565217 0.7173913 ] mean value: 0.6732192414431083 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.68025362 0.63949275 0.63043478 0.76086957 0.65217391 0.7173913 0.58695652 0.65217391 0.69565217 0.7173913 ] mean value: 0.6732789855072463 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.4516129 0.46875 0.62068966 0.48387097 0.51851852 0.42424242 0.42857143 0.53333333 0.51851852] mean value: 0.49481077493243786 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.08 MCC on Training: 0.35 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.13049555 0.12710619 0.12189245 0.12279987 0.12258267 0.12303019 0.12152791 0.12248755 0.12522817 0.12242293] mean value: 0.12395734786987304 key: score_time value: [0.01911163 0.01746535 0.01752901 0.01773119 0.01755929 0.0191927 0.01749802 0.01754379 0.01743269 0.01755095] mean value: 0.017861461639404295 key: test_mcc value: [0.57427536 0.27717391 0.6092718 0.78935222 0.57396402 0.65465367 0.3927922 0.53452248 0.43852901 0.65465367] mean value: 0.5499188347297543 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.7826087 0.63829787 0.80851064 0.89795918 0.8 0.81818182 0.70833333 0.73170732 0.69767442 0.83333333] mean value: 0.7716606610490249 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7826087 0.65217391 0.79166667 0.84615385 0.74074074 0.85714286 0.68 0.83333333 0.75 0.8 ] mean value: 0.7733820052733096 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.7826087 0.625 0.82608696 0.95652174 0.86956522 0.7826087 0.73913043 0.65217391 0.65217391 0.86956522] mean value: 0.7755434782608697 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78723404 0.63829787 0.80434783 0.89130435 0.7826087 0.82608696 0.69565217 0.76086957 0.7173913 0.82608696] mean value: 0.7729879740980574 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78713768 0.63858696 0.80434783 0.89130435 0.7826087 0.82608696 0.69565217 0.76086957 0.7173913 0.82608696] mean value: 0.7730072463768116 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.64285714 0.46875 0.67857143 0.81481481 0.66666667 0.69230769 0.5483871 0.57692308 0.53571429 0.71428571] mean value: 0.6339277918915015 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.55 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.74321055 0.79029107 0.72869802 0.71921492 0.72850013 0.77484059 0.7553792 0.74815416 0.7253325 0.70272756] mean value: 0.7416348695755005 key: score_time value: [0.01059556 0.01052403 0.01000547 0.00902081 0.01065993 0.00962138 0.01023316 0.01032424 0.00910568 0.00929976] mean value: 0.009939002990722656 key: test_mcc value: [0.4899891 0.40437762 0.73913043 0.69631062 0.69631062 0.6092718 0.35082321 0.40533961 0.43519414 0.65465367] mean value: 0.548140083379767 key: train_mcc value: [0.9951922 0.98555336 0.99520381 0.98567945 1. 0.99520381 0.99520381 0.99520381 0.99520381 0.9904304 ] mean value: 0.9932874458290073 key: test_fscore value: [0.72727273 0.72 0.86956522 0.85106383 0.85106383 0.80851064 0.65116279 0.65 0.71111111 0.83333333] mean value: 0.7673083477678491 key: train_fscore value: [0.99760192 0.99273608 0.99759036 0.99273608 1. 0.99759036 0.99759036 0.99759036 0.99759036 0.99516908] mean value: 0.9966194962783426 key: test_precision value: [0.76190476 0.69230769 0.86956522 0.83333333 0.83333333 0.79166667 0.7 0.76470588 0.72727273 0.8 ] mean value: 0.777408961456276 key: train_precision value: [0.99521531 0.99514563 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9990360942072746 key: test_recall value: [0.69565217 0.75 0.86956522 0.86956522 0.86956522 0.82608696 0.60869565 0.56521739 0.69565217 0.86956522] mean value: 0.7619565217391304 key: train_recall value: [1. 0.99033816 0.99519231 0.98557692 1. 0.99519231 0.99519231 0.99519231 0.99519231 0.99038462] mean value: 0.9942261241174284 key: test_accuracy value: [0.74468085 0.70212766 0.86956522 0.84782609 0.84782609 0.80434783 0.67391304 0.69565217 0.7173913 0.82608696] mean value: 0.7729417206290472 key: train_accuracy value: [0.99759036 0.99277108 0.99759615 0.99278846 1. 0.99759615 0.99759615 0.99759615 0.99759615 0.99519231] mean value: 0.9966322984244671 key: test_roc_auc value: [0.74365942 0.70108696 0.86956522 0.84782609 0.84782609 0.80434783 0.67391304 0.69565217 0.7173913 0.82608696] mean value: 0.7727355072463767 key: train_roc_auc value: [0.99758454 0.99276524 0.99759615 0.99278846 1. 0.99759615 0.99759615 0.99759615 0.99759615 0.99519231] mean value: 0.9966311315496098 key: test_jcc value: [0.57142857 0.5625 0.76923077 0.74074074 0.74074074 0.67857143 0.48275862 0.48148148 0.55172414 0.71428571] mean value: 0.6293462205100137 key: train_jcc value: [0.99521531 0.98557692 0.99519231 0.98557692 1. 0.99519231 0.99519231 0.99519231 0.99519231 0.99038462] mean value: 0.9932715311004785 MCC on Blind test: 0.43 MCC on Training: 0.55 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00939536 0.01043391 0.01067448 0.00955057 0.00945044 0.00945926 0.01022243 0.01002598 0.00965452 0.01059604] mean value: 0.009946298599243165 key: score_time value: [0.00931883 0.0094862 0.00883889 0.00869107 0.00871706 0.00939608 0.00898647 0.00871038 0.00948691 0.00965023] mean value: 0.009128212928771973 key: test_mcc value: [ 0.23369565 0.36265926 0.32461723 0.65465367 0.30550505 0.30434783 0.21821789 0.3927922 -0.04364358 0.26311741] mean value: 0.30159626031047254 key: train_mcc value: [0.38324624 0.38347131 0.31200924 0.34621787 0.36128324 0.38468652 0.4280083 0.36091494 0.39430369 0.35636278] mean value: 0.37105041314264414 key: test_fscore value: [0.60869565 0.70588235 0.7037037 0.83333333 0.63636364 0.65217391 0.59090909 0.70833333 0.5 0.65306122] mean value: 0.6592456240291462 key: train_fscore value: [0.69668246 0.69668246 0.69815195 0.67619048 0.68997669 0.6952381 0.71733967 0.68705882 0.7 0.68691589] mean value: 0.694423651987221 key: test_precision value: [0.60869565 0.66666667 0.61290323 0.8 0.66666667 0.65217391 0.61904762 0.68 0.48 0.61538462] mean value: 0.6401538358789411 key: train_precision value: [0.68691589 0.68372093 0.609319 0.66981132 0.66968326 0.68867925 0.70892019 0.67281106 0.69339623 0.66818182] mean value: 0.6751438930753257 key: test_recall value: [0.60869565 0.75 0.82608696 0.86956522 0.60869565 0.65217391 0.56521739 0.73913043 0.52173913 0.69565217] mean value: 0.683695652173913 key: train_recall value: [0.70673077 0.71014493 0.81730769 0.68269231 0.71153846 0.70192308 0.72596154 0.70192308 0.70673077 0.70673077] mean value: 0.7171683389074693 key: test_accuracy value: [0.61702128 0.68085106 0.65217391 0.82608696 0.65217391 0.65217391 0.60869565 0.69565217 0.47826087 0.63043478] mean value: 0.6493524514338576 key: train_accuracy value: [0.69156627 0.69156627 0.64663462 0.67307692 0.68028846 0.69230769 0.71394231 0.68028846 0.69711538 0.67788462] mean value: 0.6844670991658944 key: test_roc_auc value: [0.61684783 0.67934783 0.65217391 0.82608696 0.65217391 0.65217391 0.60869565 0.69565217 0.47826087 0.63043478] mean value: 0.6491847826086957 key: train_roc_auc value: [0.69152964 0.69161093 0.64663462 0.67307692 0.68028846 0.69230769 0.71394231 0.68028846 0.69711538 0.67788462] mean value: 0.6844679022668153 key: test_jcc value: [0.4375 0.54545455 0.54285714 0.71428571 0.46666667 0.48387097 0.41935484 0.5483871 0.33333333 0.48484848] mean value: 0.4976558790671694 key: train_jcc value: [0.53454545 0.53454545 0.5362776 0.51079137 0.52669039 0.53284672 0.55925926 0.52329749 0.53846154 0.52313167] mean value: 0.5319846946666674 MCC on Blind test: 0.59 MCC on Training: 0.3 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.21908188 0.15209913 0.14315701 0.0917716 0.15710664 0.16436839 0.14393473 0.17087102 0.17717195 0.14494157] mean value: 0.15645039081573486 key: score_time value: [0.03445888 0.02474451 0.02419949 0.01514888 0.02954721 0.0283165 0.0265224 0.02767253 0.02704072 0.02821732] mean value: 0.02658684253692627 key: test_mcc value: [0.32108787 0.14754319 0.35082321 0.6092718 0.56736651 0.52623481 0.08703883 0.43452409 0.47826087 0.56736651] mean value: 0.4089517688665271 key: train_mcc value: [0.90895793 0.91986707 0.8975981 0.89526602 0.89526602 0.88153112 0.89526602 0.90079956 0.87311336 0.89988125] mean value: 0.8967546448012618 key: test_fscore value: [0.61904762 0.6 0.65116279 0.80851064 0.77272727 0.74418605 0.55319149 0.61111111 0.73913043 0.77272727] mean value: 0.6871794675264761 key: train_fscore value: [0.95354523 0.95760599 0.94472362 0.94581281 0.94581281 0.93796526 0.94581281 0.94789082 0.93233083 0.94840295] mean value: 0.9459903113922102 key: test_precision value: [0.68421053 0.57692308 0.7 0.79166667 0.80952381 0.8 0.54166667 0.84615385 0.73913043 0.80952381] mean value: 0.7298798836556273 key: train_precision value: [0.97014925 0.98969072 0.98947368 0.96969697 0.96969697 0.96923077 0.96969697 0.97948718 0.97382199 0.96984925] mean value: 0.9750793753160163 key: test_recall value: [0.56521739 0.625 0.60869565 0.82608696 0.73913043 0.69565217 0.56521739 0.47826087 0.73913043 0.73913043] mean value: 0.6581521739130434 key: train_recall value: [0.9375 0.92753623 0.90384615 0.92307692 0.92307692 0.90865385 0.92307692 0.91826923 0.89423077 0.92788462] mean value: 0.9187151616499444 key: test_accuracy value: [0.65957447 0.57446809 0.67391304 0.80434783 0.7826087 0.76086957 0.54347826 0.69565217 0.73913043 0.7826087 ] mean value: 0.7016651248843663 key: train_accuracy value: [0.95421687 0.95903614 0.94711538 0.94711538 0.94711538 0.93990385 0.94711538 0.94951923 0.93509615 0.94951923] mean value: 0.9475753012048191 key: test_roc_auc value: [0.6576087 0.57336957 0.67391304 0.80434783 0.7826087 0.76086957 0.54347826 0.69565217 0.73913043 0.7826087 ] mean value: 0.7013586956521738 key: train_roc_auc value: [0.95425725 0.95896042 0.94711538 0.94711538 0.94711538 0.93990385 0.94711538 0.94951923 0.93509615 0.94951923] mean value: 0.9475717670011147 key: test_jcc value: [0.44827586 0.42857143 0.48275862 0.67857143 0.62962963 0.59259259 0.38235294 0.44 0.5862069 0.62962963] mean value: 0.5298589029481524 key: train_jcc value: [0.91121495 0.91866029 0.8952381 0.89719626 0.89719626 0.88317757 0.89719626 0.9009434 0.87323944 0.90186916] mean value: 0.8975931682455288 MCC on Blind test: -0.01 MCC on Training: 0.41 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02309394 0.01151443 0.01102519 0.01032972 0.01021004 0.00910711 0.01056075 0.01015735 0.00995302 0.00979018] mean value: 0.011574172973632812 key: score_time value: [0.01499772 0.0139029 0.01790619 0.01507545 0.0172205 0.01235747 0.01479053 0.01246357 0.01271415 0.01263285] mean value: 0.014406132698059081 key: test_mcc value: [0.31884058 0.01996374 0.26311741 0.43852901 0.63900965 0.52223297 0. 0.40533961 0.43852901 0.6092718 ] mean value: 0.36548337683815707 key: train_mcc value: [0.59660322 0.66052998 0.57505372 0.58874714 0.54871143 0.59618141 0.64423077 0.62546279 0.58874714 0.6111493 ] mean value: 0.6035416900855897 key: test_fscore value: [0.65217391 0.53061224 0.60465116 0.69767442 0.76923077 0.75555556 0.46511628 0.65 0.69767442 0.8 ] mean value: 0.662268876179753 key: train_fscore value: [0.79104478 0.82025316 0.77468354 0.7839196 0.76847291 0.79710145 0.82211538 0.80882353 0.7839196 0.8009828 ] mean value: 0.7951316751649316 key: test_precision value: [0.65217391 0.52 0.65 0.75 0.9375 0.77272727 0.5 0.76470588 0.75 0.81818182] mean value: 0.7115288886305511 key: train_precision value: [0.81958763 0.86170213 0.81818182 0.82105263 0.78787879 0.80097087 0.82211538 0.825 0.82105263 0.81909548] mean value: 0.8196637361532781 key: test_recall value: [0.65217391 0.54166667 0.56521739 0.65217391 0.65217391 0.73913043 0.43478261 0.56521739 0.65217391 0.7826087 ] mean value: 0.6237318840579711 key: train_recall value: [0.76442308 0.7826087 0.73557692 0.75 0.75 0.79326923 0.82211538 0.79326923 0.75 0.78365385] mean value: 0.7724916387959866 key: test_accuracy value: [0.65957447 0.5106383 0.63043478 0.7173913 0.80434783 0.76086957 0.5 0.69565217 0.7173913 0.80434783] mean value: 0.6800647548566143 key: train_accuracy value: [0.79759036 0.82891566 0.78605769 0.79326923 0.77403846 0.79807692 0.82211538 0.8125 0.79326923 0.80528846] mean value: 0.801112140871177 key: test_roc_auc value: [0.65942029 0.50996377 0.63043478 0.7173913 0.80434783 0.76086957 0.5 0.69565217 0.7173913 0.80434783] mean value: 0.679981884057971 key: train_roc_auc value: [0.79767048 0.82880435 0.78605769 0.79326923 0.77403846 0.79807692 0.82211538 0.8125 0.79326923 0.80528846] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 0.8011090208101077 key: test_jcc value: [0.48387097 0.36111111 0.43333333 0.53571429 0.625 0.60714286 0.3030303 0.48148148 0.53571429 0.66666667] mean value: 0.5033065291936261 key: train_jcc value: [0.65432099 0.69527897 0.6322314 0.6446281 0.624 0.6626506 0.69795918 0.67901235 0.6446281 0.66803279] mean value: 0.6602742479564554 MCC on Blind test: 0.15 MCC on Training: 0.37 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03785682 0.06963444 0.04649997 0.06653237 0.05056787 0.08596659 0.06020093 0.09201121 0.04187703 0.04219794] mean value: 0.059334516525268555 key: score_time value: [0.02310061 0.01528859 0.02183104 0.01337528 0.02639079 0.027879 0.01662707 0.02334523 0.01331639 0.01362801] mean value: 0.019478201866149902 key: test_mcc value: [0.48913043 0.1069309 0.48566186 0.65217391 0.56736651 0.43519414 0.30434783 0.57396402 0.34815531 0.6092718 ] mean value: 0.4572196724541636 key: train_mcc value: [0.75442025 0.74997692 0.72617138 0.73578954 0.75031223 0.74520092 0.7742622 0.76443191 0.76926633 0.74052156] mean value: 0.7510353236899068 key: test_fscore value: [0.73913043 0.63157895 0.71428571 0.82608696 0.77272727 0.71111111 0.65217391 0.76190476 0.66666667 0.8 ] mean value: 0.7275665778411774 key: train_fscore value: [0.87885986 0.87677725 0.86460808 0.86935867 0.87677725 0.87290168 0.88836105 0.882494 0.88516746 0.87142857] mean value: 0.8766733869822367 key: test_precision value: [0.73913043 0.54545455 0.78947368 0.82608696 0.80952381 0.72727273 0.65217391 0.84210526 0.68181818 0.81818182] mean value: 0.7431221333967329 key: train_precision value: [0.8685446 0.86046512 0.85446009 0.85915493 0.86448598 0.8708134 0.87793427 0.88038278 0.88095238 0.86320755] mean value: 0.8680401094672092 key: test_recall value: [0.73913043 0.75 0.65217391 0.82608696 0.73913043 0.69565217 0.65217391 0.69565217 0.65217391 0.7826087 ] mean value: 0.7184782608695652 key: train_recall value: [0.88942308 0.89371981 0.875 0.87980769 0.88942308 0.875 0.89903846 0.88461538 0.88942308 0.87980769] mean value: 0.8855258268301747 key: test_accuracy value: [0.74468085 0.55319149 0.73913043 0.82608696 0.7826087 0.7173913 0.65217391 0.7826087 0.67391304 0.80434783] mean value: 0.7276133209990749 key: train_accuracy value: [0.87710843 0.8746988 0.86298077 0.86778846 0.875 0.87259615 0.88701923 0.88221154 0.88461538 0.87019231] mean value: 0.8754211075069509 key: test_roc_auc value: [0.74456522 0.54891304 0.73913043 0.82608696 0.7826087 0.7173913 0.65217391 0.7826087 0.67391304 0.80434783] mean value: 0.7271739130434782 key: train_roc_auc value: [0.87707869 0.87474452 0.86298077 0.86778846 0.875 0.87259615 0.88701923 0.88221154 0.88461538 0.87019231] mean value: 0.8754227053140097 key: test_jcc value: [0.5862069 0.46153846 0.55555556 0.7037037 0.62962963 0.55172414 0.48387097 0.61538462 0.5 0.66666667] mean value: 0.5754280634703327 key: train_jcc value: [0.78389831 0.78059072 0.76150628 0.76890756 0.78059072 0.77446809 0.7991453 0.78969957 0.79399142 0.7721519 ] mean value: 0.7804949848970062 MCC on Blind test: 0.24 MCC on Training: 0.46 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03641868 0.03739595 0.03892827 0.03965306 0.04179573 0.04313278 0.03698707 0.03768492 0.03703046 0.03840637] mean value: 0.038743329048156736 key: score_time value: [0.01245332 0.01235485 0.01241517 0.01229048 0.0122292 0.01235533 0.01229644 0.01224709 0.01227069 0.01308513] mean value: 0.012399768829345703 key: test_mcc value: [0.27717391 0.14754319 0.56736651 0.65217391 0.69631062 0.43519414 0.35634832 0.43852901 0.39130435 0.52623481] mean value: 0.4488178782509732 key: train_mcc value: [0.63442763 0.62558036 0.64067562 0.61609705 0.60677988 0.62526019 0.67320142 0.59659528 0.60130215 0.62604273] mean value: 0.6245962298155966 key: test_fscore value: [0.63829787 0.6 0.77272727 0.82608696 0.85106383 0.71111111 0.70588235 0.69767442 0.69565217 0.7755102 ] mean value: 0.7274006192028285 key: train_fscore value: [0.82159624 0.81775701 0.82517483 0.81220657 0.80841121 0.81516588 0.83809524 0.80188679 0.80378251 0.81775701] mean value: 0.8161833288956577 key: test_precision value: [0.625 0.57692308 0.80952381 0.82608696 0.83333333 0.72727273 0.64285714 0.75 0.69565217 0.73076923] mean value: 0.7217418451114102 key: train_precision value: [0.80275229 0.7918552 0.80090498 0.79357798 0.78636364 0.80373832 0.83018868 0.78703704 0.79069767 0.79545455] mean value: 0.7982570346500948 key: test_recall value: [0.65217391 0.625 0.73913043 0.82608696 0.86956522 0.69565217 0.7826087 0.65217391 0.69565217 0.82608696] mean value: 0.7364130434782609 key: train_recall value: [0.84134615 0.84541063 0.85096154 0.83173077 0.83173077 0.82692308 0.84615385 0.81730769 0.81730769 0.84134615] mean value: 0.8350218320327016 key: test_accuracy value: [0.63829787 0.57446809 0.7826087 0.82608696 0.84782609 0.7173913 0.67391304 0.7173913 0.69565217 0.76086957] mean value: 0.7234505087881591 key: train_accuracy value: [0.81686747 0.81204819 0.81971154 0.80769231 0.80288462 0.8125 0.83653846 0.79807692 0.80048077 0.8125 ] mean value: 0.8119300278035219 key: test_roc_auc value: [0.63858696 0.57336957 0.7826087 0.82608696 0.84782609 0.7173913 0.67391304 0.7173913 0.69565217 0.76086957] mean value: 0.7233695652173913 key: train_roc_auc value: [0.81680834 0.81212839 0.81971154 0.80769231 0.80288462 0.8125 0.83653846 0.79807692 0.80048077 0.8125 ] mean value: 0.8119321348940913 key: test_jcc value: [0.46875 0.42857143 0.62962963 0.7037037 0.74074074 0.55172414 0.54545455 0.53571429 0.53333333 0.63333333] mean value: 0.5770955138412035 key: train_jcc value: [0.69721116 0.6916996 0.70238095 0.68379447 0.67843137 0.688 0.72131148 0.66929134 0.67193676 0.6916996 ] mean value: 0.689575672908358 MCC on Blind test: 0.37 MCC on Training: 0.45 Running classifier: 12 Model_name: Logistic RegressionCV Model func: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.51111317 0.59489012 0.50340986 0.51187325 0.50765681 0.60859895 0.52678251 0.49674916 0.49338722 0.62038064] mean value: 0.5374841690063477 key: score_time value: [0.0121057 0.01210523 0.01204681 0.01198626 0.01205611 0.0121882 0.01213217 0.01203275 0.01199603 0.01209903] mean value: 0.012074828147888184 key: test_mcc value: [0.28051421 0.27586252 0.6092718 0.56521739 0.65217391 0.47826087 0.31526414 0.40533961 0.1754116 0.53452248] mean value: 0.42918385457059677 key: train_mcc value: [0.61044501 0.60089349 0.58751702 0.50009248 0.57759098 0.54342626 0.63039809 0.49524382 0.51923077 0.57759098] mean value: 0.5642428917730185 key: test_fscore value: [0.65306122 0.65306122 0.8 0.7826087 0.82608696 0.73913043 0.69230769 0.65 0.6122449 0.78431373] mean value: 0.7192814851693186 key: train_fscore value: [0.81030445 0.80470588 0.79906542 0.75238095 0.79342723 0.76885645 0.81882353 0.74940334 0.75961538 0.79342723] mean value: 0.7850009868041745 key: test_precision value: [0.61538462 0.64 0.81818182 0.7826087 0.82608696 0.73913043 0.62068966 0.76470588 0.57692308 0.71428571] mean value: 0.7097996849257101 key: train_precision value: [0.78995434 0.78440367 0.77727273 0.74528302 0.77522936 0.77832512 0.80184332 0.74407583 0.75961538 0.77522936] mean value: 0.7731232124485626 key: test_recall value: [0.69565217 0.66666667 0.7826087 0.7826087 0.82608696 0.73913043 0.7826087 0.56521739 0.65217391 0.86956522] mean value: 0.736231884057971 key: train_recall value: [0.83173077 0.82608696 0.82211538 0.75961538 0.8125 0.75961538 0.83653846 0.75480769 0.75961538 0.8125 ] mean value: 0.79751254180602 key: test_accuracy value: [0.63829787 0.63829787 0.80434783 0.7826087 0.82608696 0.73913043 0.65217391 0.69565217 0.58695652 0.76086957] mean value: 0.7124421831637373 key: train_accuracy value: [0.80481928 0.8 0.79326923 0.75 0.78846154 0.77163462 0.81490385 0.74759615 0.75961538 0.78846154] mean value: 0.7818761584800742 key: test_roc_auc value: [0.63949275 0.63768116 0.80434783 0.7826087 0.82608696 0.73913043 0.65217391 0.69565217 0.58695652 0.76086957] mean value: 0.7125000000000001 key: train_roc_auc value: [0.80475427 0.80006271 0.79326923 0.75 0.78846154 0.77163462 0.81490385 0.74759615 0.75961538 0.78846154] mean value: 0.7818759290226682 key: test_jcc value: [0.48484848 0.48484848 0.66666667 0.64285714 0.7037037 0.5862069 0.52941176 0.48148148 0.44117647 0.64516129] mean value: 0.5666362386574387 key: train_jcc value: [0.68110236 0.67322835 0.66536965 0.60305344 0.65758755 0.62450593 0.69322709 0.59923664 0.6124031 0.65758755] mean value: 0.6467301653341423 MCC on Blind test: 0.38 MCC on Training: 0.43 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.50226164 0.66074967 1.4108386 0.64475298 1.74169517 1.21051311 1.34739971 0.35793018 0.95520544 1.36887836] mean value: 1.1200224876403808 key: score_time value: [0.01237845 0.01233482 0.01223135 0.0124445 0.01237249 0.01239324 0.01239562 0.01232553 0.01289845 0.01247859] mean value: 0.01242530345916748 key: test_mcc value: [0.40653424 0.31876614 0.52623481 0.75056834 0.69631062 0.43519414 0.23186945 0.43452409 0.49541508 0.6092718 ] mean value: 0.4904688721816178 key: train_mcc value: [0.73533529 0.64413837 0.73107346 0.53913807 0.74526983 0.72118719 0.66627074 0.54862773 0.6466567 0.70857569] mean value: 0.668627305275608 key: test_fscore value: [0.70833333 0.68 0.74418605 0.88 0.85106383 0.71111111 0.66666667 0.61111111 0.76923077 0.80851064] mean value: 0.7430213506049727 key: train_fscore value: [0.86997636 0.82993197 0.86729858 0.78813559 0.8716707 0.85990338 0.84233261 0.75452196 0.82949309 0.8478803 ] mean value: 0.836114455139295 key: test_precision value: [0.68 0.65384615 0.8 0.81481481 0.83333333 0.72727273 0.58064516 0.84615385 0.68965517 0.79166667] mean value: 0.7417387875791659 key: train_precision value: [0.85581395 0.78205128 0.85514019 0.70454545 0.87804878 0.86407767 0.76470588 0.81564246 0.79646018 0.88082902] mean value: 0.8197314860380406 key: test_recall value: [0.73913043 0.70833333 0.69565217 0.95652174 0.86956522 0.69565217 0.7826087 0.47826087 0.86956522 0.82608696] mean value: 0.7621376811594203 key: train_recall value: [0.88461538 0.88405797 0.87980769 0.89423077 0.86538462 0.85576923 0.9375 0.70192308 0.86538462 0.81730769] mean value: 0.8585981047937569 key: test_accuracy value: [0.70212766 0.65957447 0.76086957 0.86956522 0.84782609 0.7173913 0.60869565 0.69565217 0.73913043 0.80434783] mean value: 0.7405180388529139 key: train_accuracy value: [0.86746988 0.81927711 0.86538462 0.75961538 0.87259615 0.86057692 0.82451923 0.77163462 0.82211538 0.85336538] mean value: 0.83165546802595 key: test_roc_auc value: [0.70289855 0.65851449 0.76086957 0.86956522 0.84782609 0.7173913 0.60869565 0.69565217 0.73913043 0.80434783] mean value: 0.7404891304347826 key: train_roc_auc value: [0.86742847 0.81943283 0.86538462 0.75961538 0.87259615 0.86057692 0.82451923 0.77163462 0.82211538 0.85336538] mean value: 0.8316668989223338 key: test_jcc value: [0.5483871 0.51515152 0.59259259 0.78571429 0.74074074 0.55172414 0.5 0.44 0.625 0.67857143] mean value: 0.597788179747579 key: train_jcc value: [0.76987448 0.70930233 0.76569038 0.65034965 0.77253219 0.75423729 0.72761194 0.60580913 0.70866142 0.73593074] mean value: 0.7199999528647109 MCC on Blind test: 0.3 MCC on Training: 0.49 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01355481 0.01107502 0.00998092 0.00955057 0.00919724 0.00917244 0.00979543 0.00945115 0.00916743 0.00947905] mean value: 0.010042405128479004 key: score_time value: [0.01305914 0.00916314 0.00874591 0.00849628 0.00916338 0.00866985 0.00836039 0.00849009 0.00877571 0.00878263] mean value: 0.00917065143585205 key: test_mcc value: [0.15385544 0.14889238 0.35634832 0.57396402 0.26111648 0.30434783 0.34815531 0.35082321 0. 0.04637389] mean value: 0.254387688434083 key: train_mcc value: [0.29970801 0.33824374 0.28775695 0.28540407 0.27335618 0.30630562 0.34745733 0.26740967 0.28105051 0.32624755] mean value: 0.3012939616278832 key: test_fscore value: [0.6 0.62962963 0.70588235 0.8 0.63829787 0.65217391 0.68085106 0.69387755 0.53061224 0.59259259] mean value: 0.6523917220295457 key: train_fscore value: [0.67410714 0.68778281 0.66962306 0.66059226 0.66371681 0.67561521 0.68663594 0.65771812 0.66063348 0.68596882] mean value: 0.6722393659234346 key: test_precision value: [0.55555556 0.56666667 0.64285714 0.74074074 0.625 0.65217391 0.66666667 0.65384615 0.5 0.51612903] mean value: 0.6119635871634468 key: train_precision value: [0.62916667 0.64680851 0.62139918 0.62770563 0.6147541 0.63179916 0.65929204 0.61506276 0.62393162 0.63900415] mean value: 0.630892381371962 key: test_recall value: [0.65217391 0.70833333 0.7826087 0.86956522 0.65217391 0.65217391 0.69565217 0.73913043 0.56521739 0.69565217] mean value: 0.7012681159420289 key: train_recall value: [0.72596154 0.73429952 0.72596154 0.69711538 0.72115385 0.72596154 0.71634615 0.70673077 0.70192308 0.74038462] mean value: 0.7195837978446674 key: test_accuracy value: [0.57446809 0.57446809 0.67391304 0.7826087 0.63043478 0.65217391 0.67391304 0.67391304 0.5 0.52173913] mean value: 0.6257631822386679 key: train_accuracy value: [0.64819277 0.66746988 0.64182692 0.64182692 0.63461538 0.65144231 0.67307692 0.63221154 0.63942308 0.66105769] mean value: 0.6491143419833179 key: test_roc_auc value: [0.57608696 0.57155797 0.67391304 0.7826087 0.63043478 0.65217391 0.67391304 0.67391304 0.5 0.52173913] mean value: 0.6256340579710145 key: train_roc_auc value: [0.64800492 0.66763053 0.64182692 0.64182692 0.63461538 0.65144231 0.67307692 0.63221154 0.63942308 0.66105769] mean value: 0.6491116220735786 key: test_jcc value: [0.42857143 0.45945946 0.54545455 0.66666667 0.46875 0.48387097 0.51612903 0.53125 0.36111111 0.42105263] mean value: 0.4882315842842157 key: train_jcc value: [0.50841751 0.52413793 0.50333333 0.49319728 0.49668874 0.51013514 0.52280702 0.49 0.49324324 0.5220339 ] mean value: 0.5063994087646065 MCC on Blind test: 0.32 MCC on Training: 0.25 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01671839 0.0103786 0.0107429 0.01100397 0.01116991 0.01063704 0.01125884 0.01157618 0.01180387 0.01120138] mean value: 0.011649107933044434 key: score_time value: [0.01104712 0.00925922 0.0089097 0.00909448 0.0096004 0.00944567 0.01028395 0.00985003 0.00968432 0.00949597] mean value: 0.009667086601257324 key: test_mcc value: [0.19048769 0.0634058 0.48566186 0.66226618 0.32461723 0.39735971 0.3927922 0.34815531 0.26311741 0.39735971] mean value: 0.35252230936049267 key: train_mcc value: [0.46483333 0.43320784 0.43932243 0.41152745 0.43859149 0.47222507 0.46943906 0.43666688 0.4498791 0.42085006] mean value: 0.4436542694364737 key: test_fscore value: [0.55813953 0.54166667 0.71428571 0.80952381 0.57894737 0.66666667 0.68181818 0.66666667 0.60465116 0.66666667] mean value: 0.6489032438389843 key: train_fscore value: [0.69866667 0.70050761 0.68947368 0.67021277 0.69109948 0.69892473 0.7037037 0.69587629 0.69312169 0.69211196] mean value: 0.6933698583443146 key: test_precision value: [0.6 0.54166667 0.78947368 0.89473684 0.73333333 0.73684211 0.71428571 0.68181818 0.65 0.73684211] mean value: 0.7078998632946001 key: train_precision value: [0.78443114 0.73796791 0.76162791 0.75 0.75862069 0.79268293 0.78235294 0.75 0.77058824 0.73513514] mean value: 0.7623406887229962 key: test_recall value: [0.52173913 0.54166667 0.65217391 0.73913043 0.47826087 0.60869565 0.65217391 0.65217391 0.56521739 0.60869565] mean value: 0.6019927536231884 key: train_recall value: [0.62980769 0.66666667 0.62980769 0.60576923 0.63461538 0.625 0.63942308 0.64903846 0.62980769 0.65384615] mean value: 0.6363782051282051 key: test_accuracy value: [0.59574468 0.53191489 0.73913043 0.82608696 0.65217391 0.69565217 0.69565217 0.67391304 0.63043478 0.69565217] mean value: 0.6736355226641997 key: train_accuracy value: [0.72771084 0.71566265 0.71634615 0.70192308 0.71634615 0.73076923 0.73076923 0.71634615 0.72115385 0.70913462] mean value: 0.7186161955514365 key: test_roc_auc value: [0.5942029 0.5317029 0.73913043 0.82608696 0.65217391 0.69565217 0.69565217 0.67391304 0.63043478 0.69565217] mean value: 0.6734601449275361 key: train_roc_auc value: [0.72794732 0.71554487 0.71634615 0.70192308 0.71634615 0.73076923 0.73076923 0.71634615 0.72115385 0.70913462] mean value: 0.7186280657748048 key: test_jcc value: [0.38709677 0.37142857 0.55555556 0.68 0.40740741 0.5 0.51724138 0.5 0.43333333 0.5 ] mean value: 0.48520630212287613 key: train_jcc value: [0.53688525 0.5390625 0.52610442 0.504 0.528 0.53719008 0.54285714 0.53359684 0.53036437 0.52918288] mean value: 0.5307243478865825 MCC on Blind test: 0.36 MCC on Training: 0.35 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01846743 0.01700592 0.01833677 0.01780796 0.01806283 0.01872158 0.01928091 0.01842856 0.01671171 0.0181303 ] mean value: 0.01809539794921875 key: score_time value: [0.00940728 0.01248574 0.01200843 0.01248145 0.01246834 0.01258588 0.01257944 0.01254463 0.01254702 0.01213002] mean value: 0.012123823165893555 key: test_mcc value: [0.18822754 0.20768533 0.40533961 0.34921515 0.63900965 0.45643546 0.2548236 0.32461723 0.21081851 0.24140227] mean value: 0.3277574352430299 key: train_mcc value: [0.38820038 0.58093348 0.58425129 0.34889028 0.506968 0.5965788 0.47981265 0.53294804 0.47037484 0.53921427] mean value: 0.502817203617396 key: test_fscore value: [0.33333333 0.6779661 0.65 0.35714286 0.83018868 0.66666667 0.47058824 0.7037037 0.42424242 0.67857143] mean value: 0.5792403429894729 key: train_fscore value: [0.42857143 0.80434783 0.74011299 0.38461538 0.77593361 0.77248677 0.59934853 0.78688525 0.60702875 0.78709677] mean value: 0.6686427324360082 key: test_precision value: [0.71428571 0.57142857 0.76470588 1. 0.73333333 0.8125 0.72727273 0.61290323 0.7 0.57575758] mean value: 0.7212187030237316 key: train_precision value: [0.98275862 0.7312253 0.89726027 0.96153846 0.68248175 0.85882353 0.92929293 0.68571429 0.9047619 0.71206226] mean value: 0.8345919310458447 key: test_recall value: [0.2173913 0.83333333 0.56521739 0.2173913 0.95652174 0.56521739 0.34782609 0.82608696 0.30434783 0.82608696] mean value: 0.5659420289855073 key: train_recall value: [0.27403846 0.89371981 0.62980769 0.24038462 0.89903846 0.70192308 0.44230769 0.92307692 0.45673077 0.87980769] mean value: 0.634083519137867 key: test_accuracy value: [0.57446809 0.59574468 0.69565217 0.60869565 0.80434783 0.7173913 0.60869565 0.65217391 0.58695652 0.60869565] mean value: 0.6452821461609621 key: train_accuracy value: [0.63373494 0.78313253 0.77884615 0.61538462 0.74038462 0.79326923 0.70432692 0.75 0.70432692 0.76201923] mean value: 0.7265425162187211 key: test_roc_auc value: [0.56702899 0.59057971 0.69565217 0.60869565 0.80434783 0.7173913 0.60869565 0.65217391 0.58695652 0.60869565] mean value: 0.6440217391304348 key: train_roc_auc value: [0.63460377 0.78339836 0.77884615 0.61538462 0.74038462 0.79326923 0.70432692 0.75 0.70432692 0.76201923] mean value: 0.7266559829059829 key: test_jcc value: [0.2 0.51282051 0.48148148 0.2173913 0.70967742 0.5 0.30769231 0.54285714 0.26923077 0.51351351] mean value: 0.42546644512983917 key: train_jcc value: [0.27272727 0.67272727 0.58744395 0.23809524 0.63389831 0.62931034 0.42790698 0.64864865 0.43577982 0.64893617] mean value: 0.5195473991769818 MCC on Blind test: 0.27 MCC on Training: 0.33 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02680969 0.02877307 0.02974892 0.03160405 0.02889276 0.04562259 0.05577183 0.02953601 0.03064895 0.02876997] mean value: 0.03361778259277344 key: score_time value: [0.01255727 0.01432323 0.01367378 0.01320624 0.01304889 0.0141449 0.01265478 0.01440167 0.01317024 0.01346326] mean value: 0.01346442699432373 key: test_mcc value: [0.26534705 0.38613937 0.32461723 0.51011279 0.22941573 0.43452409 0.21081851 0.29704426 0.31526414 0.43452409] mean value: 0.3407807279256215 key: train_mcc value: [0.48985674 0.88849844 0.67142455 0.60697698 0.51423505 0.69818945 0.68933106 0.54772256 0.57364927 0.50675199] mean value: 0.6186636083885592 key: test_fscore value: [0.67857143 0.73684211 0.7037037 0.77777778 0.68852459 0.75 0.6779661 0.70175439 0.69230769 0.75 ] mean value: 0.7157447785447523 key: train_fscore value: [0.76611418 0.94470046 0.84146341 0.8125 0.77467412 0.85360825 0.84897959 0.78787879 0.79846449 0.77179963] mean value: 0.8200182918842153 key: test_precision value: [0.57575758 0.63636364 0.61290323 0.67741935 0.55263158 0.63636364 0.55555556 0.58823529 0.62068966 0.63636364] mean value: 0.6092283149286631 key: train_precision value: [0.62089552 0.9030837 0.72887324 0.68421053 0.63221884 0.74729242 0.73758865 0.65 0.66453674 0.62839879] mean value: 0.6997098437575475 key: test_recall value: [0.82608696 0.875 0.82608696 0.91304348 0.91304348 0.91304348 0.86956522 0.86956522 0.7826087 0.91304348] mean value: 0.8701086956521739 key: train_recall value: [1. 0.99033816 0.99519231 1. 1. 0.99519231 1. 1. 1. 1. ] mean value: 0.9980722779635822 key: test_accuracy value: [0.61702128 0.68085106 0.65217391 0.73913043 0.58695652 0.69565217 0.58695652 0.63043478 0.65217391 0.69565217] mean value: 0.653700277520814 key: train_accuracy value: [0.6939759 0.94216867 0.8125 0.76923077 0.70913462 0.82932692 0.82211538 0.73076923 0.74759615 0.70432692] mean value: 0.7761144578313254 key: test_roc_auc value: [0.62137681 0.67663043 0.65217391 0.73913043 0.58695652 0.69565217 0.58695652 0.63043478 0.65217391 0.69565217] mean value: 0.653713768115942 key: train_roc_auc value: [0.69323671 0.94228447 0.8125 0.76923077 0.70913462 0.82932692 0.82211538 0.73076923 0.74759615 0.70432692] mean value: 0.7760521181716834 key: test_jcc value: [0.51351351 0.58333333 0.54285714 0.63636364 0.525 0.6 0.51282051 0.54054054 0.52941176 0.6 ] mean value: 0.558384044413456 key: train_jcc value: [0.62089552 0.89519651 0.72631579 0.68421053 0.63221884 0.74460432 0.73758865 0.65 0.66453674 0.62839879] mean value: 0.698396569149643 MCC on Blind test: -0.15 MCC on Training: 0.34 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.65709305 0.69978142 0.62545228 0.65745568 0.69736481 0.73267889 0.66349864 0.65022254 0.63908505 0.65422797] mean value: 0.6676860332489014 key: score_time value: [0.15124011 0.16973948 0.17055154 0.16789556 0.18964005 0.1833303 0.21841836 0.21106052 0.15268183 0.14892912] mean value: 0.17634868621826172 key: test_mcc value: [0.48913043 0.32108787 0.65217391 0.82922798 0.65465367 0.70164642 0.47826087 0.61394061 0.43519414 0.65465367] mean value: 0.5829969576186709 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.73913043 0.69230769 0.82608696 0.91666667 0.83333333 0.85714286 0.73913043 0.79069767 0.71111111 0.83333333] mean value: 0.7938940494400555 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.73913043 0.64285714 0.82608696 0.88 0.8 0.80769231 0.73913043 0.85 0.72727273 0.8 ] mean value: 0.7812170003909135 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.75 0.82608696 0.95652174 0.86956522 0.91304348 0.73913043 0.73913043 0.69565217 0.86956522] mean value: 0.8097826086956521 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.74468085 0.65957447 0.82608696 0.91304348 0.82608696 0.84782609 0.73913043 0.80434783 0.7173913 0.82608696] mean value: 0.7904255319148936 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.74456522 0.6576087 0.82608696 0.91304348 0.82608696 0.84782609 0.73913043 0.80434783 0.7173913 0.82608696] mean value: 0.790217391304348 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5862069 0.52941176 0.7037037 0.84615385 0.71428571 0.75 0.5862069 0.65384615 0.55172414 0.71428571] mean value: 0.6635824828015497 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.52 MCC on Training: 0.58 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.97723532 0.96423912 1.08495498 1.01774263 0.97305417 0.99307847 1.02365017 1.00099492 0.98308229 1.02520442] mean value: 1.0043236494064331 key: score_time value: [0.23413467 0.25363135 0.15628767 0.25054097 0.26481223 0.22856998 0.20827866 0.19615436 0.20930648 0.24786615] mean value: 0.22495825290679933 key: test_mcc value: [0.44646172 0.31876614 0.65217391 0.83887049 0.73913043 0.56736651 0.43519414 0.61394061 0.48007936 0.61394061] mean value: 0.5705923939636847 key: train_mcc value: [0.87474161 0.89401436 0.87504045 0.88968016 0.88461538 0.87020236 0.87504045 0.87989922 0.87504045 0.87989922] mean value: 0.8798173671204126 key: test_fscore value: [0.71111111 0.68 0.82608696 0.92 0.86956522 0.79166667 0.71111111 0.79069767 0.72727273 0.81632653] mean value: 0.7843837995105509 key: train_fscore value: [0.93719807 0.94660194 0.93719807 0.94403893 0.94230769 0.93493976 0.93719807 0.94033413 0.93719807 0.93946731] mean value: 0.939648203429015 key: test_precision value: [0.72727273 0.65384615 0.82608696 0.85185185 0.86956522 0.76 0.72727273 0.85 0.76190476 0.76923077] mean value: 0.7797031165292034 key: train_precision value: [0.94174757 0.95121951 0.94174757 0.95566502 0.94230769 0.93719807 0.94174757 0.93364929 0.94174757 0.94634146] mean value: 0.94333713405425 key: test_recall value: [0.69565217 0.70833333 0.82608696 1. 0.86956522 0.82608696 0.69565217 0.73913043 0.69565217 0.86956522] mean value: 0.792572463768116 key: train_recall value: [0.93269231 0.94202899 0.93269231 0.93269231 0.94230769 0.93269231 0.93269231 0.94711538 0.93269231 0.93269231] mean value: 0.9360298216276478 key: test_accuracy value: [0.72340426 0.65957447 0.82608696 0.91304348 0.86956522 0.7826087 0.7173913 0.80434783 0.73913043 0.80434783] mean value: 0.7839500462534689 key: train_accuracy value: [0.9373494 0.94698795 0.9375 0.94471154 0.94230769 0.93509615 0.9375 0.93990385 0.9375 0.93990385] mean value: 0.9398760426320667 key: test_roc_auc value: [0.72282609 0.65851449 0.82608696 0.91304348 0.86956522 0.7826087 0.7173913 0.80434783 0.73913043 0.80434783] mean value: 0.7837862318840579 key: train_roc_auc value: [0.93736065 0.94697603 0.9375 0.94471154 0.94230769 0.93509615 0.9375 0.93990385 0.9375 0.93990385] mean value: 0.9398759754738016 key: test_jcc value: [0.55172414 0.51515152 0.7037037 0.85185185 0.76923077 0.65517241 0.55172414 0.65384615 0.57142857 0.68965517] mean value: 0.6513488427281532 key: train_jcc value: [0.88181818 0.89861751 0.88181818 0.89400922 0.89090909 0.87782805 0.88181818 0.88738739 0.88181818 0.88584475] mean value: 0.8861868736836895 MCC on Blind test: 0.58 MCC on Training: 0.57 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02537894 0.05431008 0.05821085 0.03705859 0.01492882 0.01516914 0.03801107 0.03725719 0.04412794 0.04152274] mean value: 0.03659753799438477 key: score_time value: [0.02224064 0.02324867 0.1121695 0.01221228 0.01213861 0.01187682 0.02026367 0.01285648 0.02113318 0.02049446] mean value: 0.02686343193054199 key: test_mcc value: [0.36231884 0.19048769 0.52623481 0.78334945 0.56736651 0.3927922 0.35082321 0.6092718 0.35082321 0.69631062] mean value: 0.48297783484306295 key: train_mcc value: [0.69656843 0.68682786 0.69310918 0.66373774 0.68383129 0.67808056 0.70221529 0.67826882 0.6971879 0.68383129] mean value: 0.6863658374778553 key: test_fscore value: [0.68085106 0.62745098 0.74418605 0.89361702 0.77272727 0.70833333 0.69387755 0.8 0.69387755 0.85106383] mean value: 0.7465984649898824 key: train_fscore value: [0.85035629 0.8441247 0.84976526 0.83412322 0.84579439 0.84085511 0.85308057 0.84160757 0.849642 0.84579439] mean value: 0.8455143506181961 key: test_precision value: [0.66666667 0.59259259 0.8 0.875 0.80952381 0.68 0.65384615 0.81818182 0.65384615 0.83333333] mean value: 0.7382990527990528 key: train_precision value: [0.84037559 0.83809524 0.83027523 0.82242991 0.82272727 0.83098592 0.8411215 0.82790698 0.8436019 0.82272727] mean value: 0.8320246789602941 key: test_recall value: [0.69565217 0.66666667 0.69565217 0.91304348 0.73913043 0.73913043 0.73913043 0.7826087 0.73913043 0.86956522] mean value: 0.7579710144927536 key: train_recall value: [0.86057692 0.85024155 0.87019231 0.84615385 0.87019231 0.85096154 0.86538462 0.85576923 0.85576923 0.87019231] mean value: 0.8595433853586029 key: test_accuracy value: [0.68085106 0.59574468 0.76086957 0.89130435 0.7826087 0.69565217 0.67391304 0.80434783 0.67391304 0.84782609] mean value: 0.7407030527289546 key: train_accuracy value: [0.84819277 0.84337349 0.84615385 0.83173077 0.84134615 0.83894231 0.85096154 0.83894231 0.84855769 0.84134615] mean value: 0.8429547034291011 key: test_roc_auc value: [0.68115942 0.5942029 0.76086957 0.89130435 0.7826087 0.69565217 0.67391304 0.80434783 0.67391304 0.84782609] mean value: 0.7405797101449274 key: train_roc_auc value: [0.84816286 0.84339 0.84615385 0.83173077 0.84134615 0.83894231 0.85096154 0.83894231 0.84855769 0.84134615] mean value: 0.8429533630620588 key: test_jcc value: [0.51612903 0.45714286 0.59259259 0.80769231 0.62962963 0.5483871 0.53125 0.66666667 0.53125 0.74074074] mean value: 0.6021480923497051 key: train_jcc value: [0.73966942 0.73029046 0.73877551 0.71544715 0.73279352 0.72540984 0.74380165 0.72653061 0.73858921 0.73279352] mean value: 0.7324100899950469 MCC on Blind test: 0.17 MCC on Training: 0.48 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.12368989 0.1066854 0.16785455 0.11932683 0.11811018 0.11867952 0.11921787 0.1225791 0.14868784 0.09553647] mean value: 0.12403676509857178 key: score_time value: [0.03771687 0.02363038 0.03766918 0.02168107 0.02331543 0.02412963 0.02418709 0.02479506 0.0243032 0.02174902] mean value: 0.026317691802978514 key: test_mcc value: [0.28051421 0.23315467 0.6092718 0.65217391 0.6092718 0.43519414 0.35634832 0.3927922 0.43519414 0.53452248] mean value: 0.4538437664772256 key: train_mcc value: [0.62009843 0.62197067 0.5828703 0.59217483 0.57823436 0.59684401 0.64449897 0.58206048 0.57694975 0.59217483] mean value: 0.598787661730896 key: test_fscore value: [0.65306122 0.64 0.8 0.82608696 0.8 0.72340426 0.70588235 0.68181818 0.72340426 0.78431373] mean value: 0.7337970951899387 key: train_fscore value: [0.81498829 0.81755196 0.7972028 0.80093677 0.79534884 0.8028169 0.82464455 0.79432624 0.78947368 0.80093677] mean value: 0.8038226800675252 key: test_precision value: [0.61538462 0.61538462 0.81818182 0.82608696 0.81818182 0.70833333 0.64285714 0.71428571 0.70833333 0.71428571] mean value: 0.7181315061749844 key: train_precision value: [0.79452055 0.78318584 0.77375566 0.78082192 0.77027027 0.78440367 0.81308411 0.78139535 0.78571429 0.78082192] mean value: 0.7847973567074275 key: test_recall value: [0.69565217 0.66666667 0.7826087 0.82608696 0.7826087 0.73913043 0.7826087 0.65217391 0.73913043 0.86956522] mean value: 0.7536231884057971 key: train_recall value: [0.83653846 0.85507246 0.82211538 0.82211538 0.82211538 0.82211538 0.83653846 0.80769231 0.79326923 0.82211538] mean value: 0.82396878483835 key: test_accuracy value: [0.63829787 0.61702128 0.80434783 0.82608696 0.80434783 0.7173913 0.67391304 0.69565217 0.7173913 0.76086957] mean value: 0.7255319148936171 key: train_accuracy value: [0.80963855 0.80963855 0.79086538 0.79567308 0.78846154 0.79807692 0.82211538 0.79086538 0.78846154 0.79567308] mean value: 0.7989469416126042 key: test_roc_auc value: [0.63949275 0.61594203 0.80434783 0.82608696 0.80434783 0.7173913 0.67391304 0.69565217 0.7173913 0.76086957] mean value: 0.7255434782608696 key: train_roc_auc value: [0.80957358 0.80974777 0.79086538 0.79567308 0.78846154 0.79807692 0.82211538 0.79086538 0.78846154 0.79567308] mean value: 0.7989513656633221 key: test_jcc value: [0.48484848 0.47058824 0.66666667 0.7037037 0.66666667 0.56666667 0.54545455 0.51724138 0.56666667 0.64516129] mean value: 0.5833664305600444 key: train_jcc value: [0.68774704 0.69140625 0.6627907 0.66796875 0.66023166 0.67058824 0.7016129 0.65882353 0.65217391 0.66796875] mean value: 0.6721311724454369 MCC on Blind test: 0.37 MCC on Training: 0.45 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03672791 0.01817465 0.0184536 0.0183835 0.01839828 0.01809144 0.01811481 0.01849818 0.01828456 0.01849842] mean value: 0.020162534713745118 key: score_time value: [0.01194334 0.01112199 0.01109982 0.01134276 0.01138878 0.01117325 0.01119542 0.01122355 0.01133084 0.01135159] mean value: 0.011317133903503418 key: test_mcc value: [0.28051421 0.23369565 0.65217391 0.6092718 0.65217391 0.39130435 0.39735971 0.6092718 0.26111648 0.43519414] mean value: 0.4522075955119762 key: train_mcc value: [0.65372952 0.65804025 0.64910598 0.61584029 0.61177296 0.63464472 0.66833875 0.64002249 0.61058398 0.63464472] mean value: 0.6376723668002897 key: test_fscore value: [0.65306122 0.625 0.82608696 0.8 0.82608696 0.69565217 0.72 0.8 0.63829787 0.72340426] mean value: 0.7307589439105893 key: train_fscore value: [0.83098592 0.83054893 0.82577566 0.81132075 0.81118881 0.81818182 0.83292978 0.82352941 0.8057554 0.81818182] mean value: 0.8208398289631773 key: test_precision value: [0.61538462 0.625 0.82608696 0.81818182 0.82608696 0.69565217 0.66666667 0.81818182 0.625 0.70833333] mean value: 0.7224574338704773 key: train_precision value: [0.81192661 0.82075472 0.81990521 0.7962963 0.78733032 0.81428571 0.83902439 0.80645161 0.80382775 0.81428571] mean value: 0.8114088331708968 key: test_recall value: [0.69565217 0.625 0.82608696 0.7826087 0.82608696 0.69565217 0.7826087 0.7826087 0.65217391 0.73913043] mean value: 0.7407608695652175 key: train_recall value: [0.85096154 0.84057971 0.83173077 0.82692308 0.83653846 0.82211538 0.82692308 0.84134615 0.80769231 0.82211538] mean value: 0.8306925863991081 key: test_accuracy value: [0.63829787 0.61702128 0.82608696 0.80434783 0.82608696 0.69565217 0.69565217 0.80434783 0.63043478 0.7173913 ] mean value: 0.725531914893617 key: train_accuracy value: [0.82650602 0.82891566 0.82451923 0.80769231 0.80528846 0.81730769 0.83413462 0.81971154 0.80528846 0.81730769] mean value: 0.8186671686746987 key: test_roc_auc value: [0.63949275 0.61684783 0.82608696 0.80434783 0.82608696 0.69565217 0.69565217 0.80434783 0.63043478 0.7173913 ] mean value: 0.7256340579710144 key: train_roc_auc value: [0.82644695 0.8289437 0.82451923 0.80769231 0.80528846 0.81730769 0.83413462 0.81971154 0.80528846 0.81730769] mean value: 0.8186640654031958 key: test_jcc value: [0.48484848 0.45454545 0.7037037 0.66666667 0.7037037 0.53333333 0.5625 0.66666667 0.46875 0.56666667] mean value: 0.5811384680134679 key: train_jcc value: [0.71084337 0.71020408 0.70325203 0.68253968 0.68235294 0.69230769 0.71369295 0.7 0.6746988 0.69230769] mean value: 0.6962199237217306 MCC on Blind test: 0.27 MCC on Training: 0.45 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01745319 0.02357984 0.01748824 0.02169561 0.02794528 0.02433038 0.02330685 0.02224898 0.02313733 0.02121758] mean value: 0.022240328788757324 key: score_time value: [0.00905681 0.01170516 0.01175117 0.01206541 0.01299191 0.01282907 0.01211691 0.01206493 0.01214361 0.01213646] mean value: 0.011886143684387207 key: test_mcc value: [0.18822754 0.23435724 0.56694671 0.56521739 0.56736651 0.39130435 0.17817416 0.43452409 0.28347335 0.52623481] mean value: 0.39358261602589084 key: train_mcc value: [0.41411102 0.6982629 0.43201782 0.58828152 0.66496951 0.6466567 0.62670224 0.54752544 0.61040309 0.65460311] mean value: 0.5883533356282384 key: test_fscore value: [0.33333333 0.65384615 0.8 0.7826087 0.77272727 0.69565217 0.53658537 0.61111111 0.54054054 0.7755102 ] mean value: 0.6501914851058921 key: train_fscore value: [0.4535316 0.85314685 0.74906367 0.80092593 0.82587065 0.82949309 0.767507 0.71428571 0.75354108 0.83098592] mean value: 0.7578351491388593 key: test_precision value: [0.71428571 0.60714286 0.6875 0.7826087 0.80952381 0.69565217 0.61111111 0.84615385 0.71428571 0.73076923] mean value: 0.7199033152837501 key: train_precision value: [1. 0.82432432 0.61349693 0.77232143 0.8556701 0.79646018 0.91946309 0.88028169 0.91724138 0.81192661] mean value: 0.8391185727699124 key: test_recall value: [0.2173913 0.70833333 0.95652174 0.7826087 0.73913043 0.69565217 0.47826087 0.47826087 0.43478261 0.82608696] mean value: 0.6317028985507247 key: train_recall value: [0.29326923 0.88405797 0.96153846 0.83173077 0.79807692 0.86538462 0.65865385 0.60096154 0.63942308 0.85096154] mean value: 0.7384057971014493 key: test_accuracy value: [0.57446809 0.61702128 0.76086957 0.7826087 0.7826087 0.69565217 0.58695652 0.69565217 0.63043478 0.76086957] mean value: 0.6887141535615171 key: train_accuracy value: [0.64578313 0.84819277 0.67788462 0.79326923 0.83173077 0.82211538 0.80048077 0.75961538 0.79086538 0.82692308] mean value: 0.7796860518999074 key: test_roc_auc value: [0.56702899 0.61503623 0.76086957 0.7826087 0.7826087 0.69565217 0.58695652 0.69565217 0.63043478 0.76086957] mean value: 0.6877717391304349 key: train_roc_auc value: [0.64663462 0.84827899 0.67788462 0.79326923 0.83173077 0.82211538 0.80048077 0.75961538 0.79086538 0.82692308] mean value: 0.7797798216276476 key: test_jcc value: [0.2 0.48571429 0.66666667 0.64285714 0.62962963 0.53333333 0.36666667 0.44 0.37037037 0.63333333] mean value: 0.4968571428571428 key: train_jcc value: [0.29326923 0.74390244 0.5988024 0.66795367 0.70338983 0.70866142 0.62272727 0.55555556 0.60454545 0.71084337] mean value: 0.6209650637110438 MCC on Blind test: 0.29 MCC on Training: 0.39 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.29105473 0.10998178 0.11196208 0.11976004 0.12253022 0.11672807 0.11245704 0.11275005 0.1138413 0.12194395] mean value: 0.13330092430114746 key: score_time value: [0.01103878 0.01199341 0.01201701 0.01155806 0.01096201 0.01100326 0.011199 0.01096129 0.01105022 0.01214099] mean value: 0.01139240264892578 key: test_mcc value: [0.44874504 0.27657348 0.74194083 0.73913043 0.65465367 0.6092718 0.48566186 0.3927922 0.39735971 0.65465367] mean value: 0.5400782693382726 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69767442 0.66666667 0.86363636 0.86956522 0.83333333 0.8 0.71428571 0.68181818 0.66666667 0.83333333] mean value: 0.7626979895736216 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.62962963 0.9047619 0.86956522 0.8 0.81818182 0.78947368 0.71428571 0.73684211 0.8 ] mean value: 0.7812740073724055 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.65217391 0.70833333 0.82608696 0.86956522 0.86956522 0.7826087 0.65217391 0.65217391 0.60869565 0.86956522] mean value: 0.7490942028985507 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72340426 0.63829787 0.86956522 0.86956522 0.82608696 0.80434783 0.73913043 0.69565217 0.69565217 0.82608696] mean value: 0.7687789084181313 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.72192029 0.63677536 0.86956522 0.86956522 0.82608696 0.80434783 0.73913043 0.69565217 0.69565217 0.82608696] mean value: 0.7684782608695652 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53571429 0.5 0.76 0.76923077 0.71428571 0.66666667 0.55555556 0.51724138 0.5 0.71428571] mean value: 0.6232980085049051 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.54 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16214132 0.15775251 0.15776205 0.15732479 0.15814471 0.160074 0.15804458 0.15844131 0.15728068 0.1611557 ] mean value: 0.15881216526031494 key: score_time value: [0.01522183 0.01510215 0.01480293 0.01498771 0.01500392 0.01500034 0.01500154 0.01490474 0.01514745 0.01497746] mean value: 0.015015006065368652 key: test_mcc value: [0.45455353 0.31876614 0.70164642 0.80178373 0.6092718 0.53452248 0.63900965 0.30905755 0.26311741 0.75056834] mean value: 0.5382297037599983 key: train_mcc value: [0.87161017 0.89463072 0.8564029 0.83251046 0.833902 0.8577143 0.86226268 0.88968016 0.83669319 0.880062 ] mean value: 0.8615468574045166 key: test_fscore value: [0.68292683 0.68 0.8372093 0.87804878 0.80851064 0.73170732 0.76923077 0.61904762 0.65306122 0.88 ] mean value: 0.7539742480220906 key: train_fscore value: [0.93300248 0.94581281 0.92647059 0.91400491 0.9127182 0.92537313 0.9280397 0.94403893 0.91747573 0.93917275] mean value: 0.9286109239549404 key: test_precision value: [0.77777778 0.65384615 0.9 1. 0.79166667 0.83333333 0.9375 0.68421053 0.61538462 0.81481481] mean value: 0.8008533888139151 key: train_precision value: [0.96410256 0.96482412 0.945 0.93467337 0.94818653 0.95876289 0.95897436 0.95566502 0.92647059 0.95073892] mean value: 0.950739835473145 key: test_recall value: [0.60869565 0.70833333 0.7826087 0.7826087 0.82608696 0.65217391 0.65217391 0.56521739 0.69565217 0.95652174] mean value: 0.7230072463768116 key: train_recall value: [0.90384615 0.92753623 0.90865385 0.89423077 0.87980769 0.89423077 0.89903846 0.93269231 0.90865385 0.92788462] mean value: 0.907657469342252 key: test_accuracy value: [0.72340426 0.65957447 0.84782609 0.89130435 0.80434783 0.76086957 0.80434783 0.65217391 0.63043478 0.86956522] mean value: 0.7643848288621646 key: train_accuracy value: [0.93493976 0.94698795 0.92788462 0.91586538 0.91586538 0.92788462 0.93028846 0.94471154 0.91826923 0.93990385] mean value: 0.9302600787766451 key: test_roc_auc value: [0.72101449 0.65851449 0.84782609 0.89130435 0.80434783 0.76086957 0.80434783 0.65217391 0.63043478 0.86956522] mean value: 0.7640398550724639 key: train_roc_auc value: [0.93501486 0.94694119 0.92788462 0.91586538 0.91586538 0.92788462 0.93028846 0.94471154 0.91826923 0.93990385] mean value: 0.9302629134150873 key: test_jcc value: [0.51851852 0.51515152 0.72 0.7826087 0.67857143 0.57692308 0.625 0.44827586 0.48484848 0.78571429] mean value: 0.6135611867448449 key: train_jcc value: [0.8744186 0.89719626 0.8630137 0.84162896 0.83944954 0.86111111 0.86574074 0.89400922 0.84753363 0.8853211 ] mean value: 0.8669422867170106 MCC on Blind test: 0.19 MCC on Training: 0.54 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.24946189 0.23844218 0.2844038 0.24463248 0.25927758 0.25903702 0.29447961 0.2657187 0.23403478 0.25877309] mean value: 0.25882611274719236 key: score_time value: [0.06320357 0.07146215 0.06800842 0.04732823 0.07619572 0.04681563 0.07905531 0.0484817 0.07686424 0.04044485] mean value: 0.06178598403930664 key: test_mcc value: [0.58127976 0.44746377 0.78334945 0.87038828 0.69631062 0.71269665 0.53452248 0.53452248 0.43852901 0.78935222] mean value: 0.6388414720046397 key: train_mcc value: [0.99519231 1. 1. 0.99520381 1. 1. 1. 1. 1. 1. ] mean value: 0.9990396116974392 key: test_fscore value: [0.76190476 0.72340426 0.88888889 0.93333333 0.85106383 0.82926829 0.73170732 0.73170732 0.69767442 0.89795918] mean value: 0.8046911598340756 key: train_fscore value: [0.99759036 1. 1. 0.99759036 1. 1. 1. 1. 1. 1. ] mean value: 0.9995180722891567 key: test_precision value: [0.84210526 0.73913043 0.90909091 0.95454545 0.83333333 0.94444444 0.83333333 0.83333333 0.75 0.84615385] mean value: 0.8485470352175157 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.69565217 0.70833333 0.86956522 0.91304348 0.86956522 0.73913043 0.65217391 0.65217391 0.65217391 0.95652174] mean value: 0.7708333333333333 key: train_recall value: [0.99519231 1. 1. 0.99519231 1. 1. 1. 1. 1. 1. ] mean value: 0.9990384615384615 key: test_accuracy value: [0.78723404 0.72340426 0.89130435 0.93478261 0.84782609 0.84782609 0.76086957 0.76086957 0.7173913 0.89130435] mean value: 0.8162812210915819 key: train_accuracy value: [0.99759036 1. 1. 0.99759615 1. 1. 1. 1. 1. 1. ] mean value: 0.9995186515291937 key: test_roc_auc value: [0.78532609 0.72373188 0.89130435 0.93478261 0.84782609 0.84782609 0.76086957 0.76086957 0.7173913 0.89130435] mean value: 0.8161231884057972 key: train_roc_auc value: [0.99759615 1. 1. 0.99759615 1. 1. 1. 1. 1. 1. ] mean value: 0.9995192307692307 key: test_jcc value: [0.61538462 0.56666667 0.8 0.875 0.74074074 0.70833333 0.57692308 0.57692308 0.53571429 0.81481481] mean value: 0.681050061050061 key: train_jcc value: [0.99519231 1. 1. 0.99519231 1. 1. 1. 1. 1. 1. ] mean value: 0.9990384615384615 MCC on Blind test: 0.31 MCC on Training: 0.64 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04692483 0.02358413 0.03096175 0.02697945 0.02645612 0.02655983 0.02590704 0.02708864 0.02592158 0.02649808] mean value: 0.02868814468383789 key: score_time value: [0.00900555 0.00948858 0.00863719 0.00882745 0.00866222 0.00852919 0.00892019 0.00916052 0.00884104 0.00874805] mean value: 0.00888199806213379 key: test_mcc value: [0.57560058 0.49183384 0.65465367 0.56061191 0.52223297 0.61394061 0.45643546 0.66226618 0.48007936 0.61394061] mean value: 0.5631595198047168 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77272727 0.73913043 0.83333333 0.64705882 0.75555556 0.79069767 0.66666667 0.80952381 0.72727273 0.79069767] mean value: 0.7532663972228595 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.80952381 0.77272727 0.8 1. 0.77272727 0.85 0.8125 0.89473684 0.76190476 0.85 ] mean value: 0.832411995898838 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.70833333 0.86956522 0.47826087 0.73913043 0.73913043 0.56521739 0.73913043 0.69565217 0.73913043] mean value: 0.701268115942029 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78723404 0.74468085 0.82608696 0.73913043 0.76086957 0.80434783 0.7173913 0.82608696 0.73913043 0.80434783] mean value: 0.7749306197964847 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78623188 0.74547101 0.82608696 0.73913043 0.76086957 0.80434783 0.7173913 0.82608696 0.73913043 0.80434783] mean value: 0.7749094202898551 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.62962963 0.5862069 0.71428571 0.47826087 0.60714286 0.65384615 0.5 0.68 0.57142857 0.65384615] mean value: 0.6074646846296022 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.56 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00947976 0.00967526 0.00973964 0.00952339 0.00956464 0.00971031 0.00950813 0.00946403 0.00976682 0.00972819] mean value: 0.00961601734161377 key: score_time value: [0.00853539 0.00854301 0.00871372 0.00862098 0.00852132 0.00846529 0.00872898 0.00853133 0.00847626 0.00857997] mean value: 0.008571624755859375 key: test_mcc value: [0.51676308 0.36116212 0.62360956 0.62360956 0.3927922 0.52623481 0.48007936 0.35634832 0.43852901 0.40533961] mean value: 0.47244676483880743 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.68421053 0.69387755 0.7804878 0.7804878 0.68181818 0.74418605 0.72727273 0.63414634 0.73469388 0.65 ] mean value: 0.7111180861709269 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86666667 0.68 0.88888889 0.88888889 0.71428571 0.8 0.76190476 0.72222222 0.69230769 0.76470588] mean value: 0.7779870717517776 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.56521739 0.70833333 0.69565217 0.69565217 0.65217391 0.69565217 0.69565217 0.56521739 0.7826087 0.56521739] mean value: 0.6621376811594203 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.74468085 0.68085106 0.80434783 0.80434783 0.69565217 0.76086957 0.73913043 0.67391304 0.7173913 0.69565217] mean value: 0.7316836262719704 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.74094203 0.68025362 0.80434783 0.80434783 0.69565217 0.76086957 0.73913043 0.67391304 0.7173913 0.69565217] mean value: 0.73125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.52 0.53125 0.64 0.64 0.51724138 0.59259259 0.57142857 0.46428571 0.58064516 0.48148148] mean value: 0.5538924900389027 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.47 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.13126945 0.12755108 0.12300372 0.1226635 0.12122345 0.1212008 0.12118149 0.12905502 0.12544465 0.12356329] mean value: 0.12461564540863038 key: score_time value: [0.01948094 0.01807022 0.01756334 0.01756859 0.01769042 0.01812291 0.01748919 0.01918674 0.01917768 0.01759052] mean value: 0.018194055557250975 key: test_mcc value: [0.62091661 0.4078185 0.69631062 0.82608696 0.66226618 0.74194083 0.6092718 0.57396402 0.43519414 0.73913043] mean value: 0.6312900086043766 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.79069767 0.73076923 0.84444444 0.91304348 0.84 0.86363636 0.8 0.76190476 0.72340426 0.86956522] mean value: 0.8137465426144729 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 0.67857143 0.86363636 0.91304348 0.77777778 0.9047619 0.81818182 0.84210526 0.70833333 0.86956522] mean value: 0.8225976585072695 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.79166667 0.82608696 0.91304348 0.91304348 0.82608696 0.7826087 0.69565217 0.73913043 0.86956522] mean value: 0.8096014492753623 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80851064 0.70212766 0.84782609 0.91304348 0.82608696 0.86956522 0.80434783 0.7826087 0.7173913 0.86956522] mean value: 0.8141073080481036 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.80706522 0.70018116 0.84782609 0.91304348 0.82608696 0.86956522 0.80434783 0.7826087 0.7173913 0.86956522] mean value: 0.8137681159420291 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.65384615 0.57575758 0.73076923 0.84 0.72413793 0.76 0.66666667 0.61538462 0.56666667 0.76923077] mean value: 0.6902459609356161 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.63 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.65268755 0.64820075 0.63048697 0.63160372 0.63887739 0.65202069 0.6618259 0.648669 0.64970064 0.64763045] mean value: 0.6461703062057496 key: score_time value: [0.01054811 0.00923491 0.00959945 0.00916743 0.00918484 0.0098269 0.0096786 0.00940013 0.00938845 0.00920677] mean value: 0.009523558616638183 key: test_mcc value: [0.59180008 0.27657348 0.78334945 0.91304348 0.57396402 0.74194083 0.54772256 0.4454354 0.48007936 0.65217391] mean value: 0.6006082569071685 key: train_mcc value: [0.99519231 1. 0.9904304 1. 1. 0.99520381 0.99520381 0.9904304 1. 0.99520381] mean value: 0.9961664539282606 key: test_fscore value: [0.75 0.66666667 0.89361702 0.95652174 0.8 0.86363636 0.71794872 0.68292683 0.72727273 0.82608696] mean value: 0.7884677021721538 key: train_fscore value: [0.99759036 1. 0.99516908 1. 1. 0.99759036 0.99759036 0.99516908 1. 0.99760192] mean value: 0.9980711167053787 key: test_precision value: [0.88235294 0.62962963 0.875 0.95652174 0.74074074 0.9047619 0.875 0.77777778 0.76190476 0.82608696] mean value: 0.822977645164346 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99521531] mean value: 0.9995215311004785 key: test_recall value: [0.65217391 0.70833333 0.91304348 0.95652174 0.86956522 0.82608696 0.60869565 0.60869565 0.69565217 0.82608696] mean value: 0.7664855072463769 key: train_recall value: [0.99519231 1. 0.99038462 1. 1. 0.99519231 0.99519231 0.99038462 1. 1. ] mean value: 0.9966346153846153 key: test_accuracy value: [0.78723404 0.63829787 0.89130435 0.95652174 0.7826087 0.86956522 0.76086957 0.7173913 0.73913043 0.82608696] mean value: 0.7969010175763181 key: train_accuracy value: [0.99759036 1. 0.99519231 1. 1. 0.99759615 0.99759615 0.99519231 1. 0.99759615] mean value: 0.9980763438368859 key: test_roc_auc value: [0.78442029 0.63677536 0.89130435 0.95652174 0.7826087 0.86956522 0.76086957 0.7173913 0.73913043 0.82608696] mean value: 0.7964673913043477 key: train_roc_auc value: [0.99759615 1. 0.99519231 1. 1. 0.99759615 0.99759615 0.99519231 1. 0.99759615] mean value: 0.998076923076923 key: test_jcc value: [0.6 0.5 0.80769231 0.91666667 0.66666667 0.76 0.56 0.51851852 0.57142857 0.7037037 ] mean value: 0.6604676434676435 key: train_jcc value: [0.99519231 1. 0.99038462 1. 1. 0.99519231 0.99519231 0.99038462 1. 0.99521531] mean value: 0.9961561464850938 MCC on Blind test: 0.19 MCC on Training: 0.6 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00942278 0.00934887 0.00966954 0.01006079 0.00994015 0.00936174 0.00928116 0.0107553 0.00974107 0.01035833] mean value: 0.009793972969055176 key: score_time value: [0.00947094 0.00887251 0.00875735 0.00950837 0.0086832 0.00893641 0.00895882 0.00915623 0.00862265 0.00885653] mean value: 0.008982300758361816 key: test_mcc value: [ 0.19202899 0.23747392 0.28347335 0.57396402 0.43852901 0.1351132 0.34815531 0.47826087 -0.1754116 0.17817416] mean value: 0.2689761233277752 key: train_mcc value: [0.3648201 0.35601992 0.27569686 0.37040212 0.32446757 0.35213964 0.37527767 0.31516656 0.36151821 0.352435 ] mean value: 0.34479436600022495 key: test_fscore value: [0.59574468 0.66666667 0.69090909 0.8 0.69767442 0.61538462 0.66666667 0.73913043 0.44897959 0.62745098] mean value: 0.6548607146094255 key: train_fscore value: [0.69444444 0.69124424 0.68312757 0.69030733 0.68027211 0.68822171 0.69339623 0.6772009 0.69141531 0.68965517] mean value: 0.6879285017536392 key: test_precision value: [0.58333333 0.6 0.59375 0.74074074 0.75 0.55172414 0.68181818 0.73913043 0.42307692 0.57142857] mean value: 0.6235002323111394 key: train_precision value: [0.66964286 0.66079295 0.5971223 0.67906977 0.64377682 0.66222222 0.68055556 0.63829787 0.66816143 0.66079295] mean value: 0.6560434738956809 key: test_recall value: [0.60869565 0.75 0.82608696 0.86956522 0.65217391 0.69565217 0.65217391 0.73913043 0.47826087 0.69565217] mean value: 0.6967391304347826 key: train_recall value: [0.72115385 0.72463768 0.79807692 0.70192308 0.72115385 0.71634615 0.70673077 0.72115385 0.71634615 0.72115385] mean value: 0.7248676142697882 key: test_accuracy value: [0.59574468 0.61702128 0.63043478 0.7826087 0.7173913 0.56521739 0.67391304 0.73913043 0.41304348 0.58695652] mean value: 0.6321461609620721 key: train_accuracy value: [0.68192771 0.67710843 0.62980769 0.68509615 0.66105769 0.67548077 0.6875 0.65625 0.68028846 0.67548077] mean value: 0.6709997683039852 key: test_roc_auc value: [0.59601449 0.61413043 0.63043478 0.7826087 0.7173913 0.56521739 0.67391304 0.73913043 0.41304348 0.58695652] mean value: 0.6318840579710144 key: train_roc_auc value: [0.68183296 0.67722269 0.62980769 0.68509615 0.66105769 0.67548077 0.6875 0.65625 0.68028846 0.67548077] mean value: 0.6710017186919361 key: test_jcc value: [0.42424242 0.5 0.52777778 0.66666667 0.53571429 0.44444444 0.5 0.5862069 0.28947368 0.45714286] mean value: 0.49316690367507066 key: train_jcc value: [0.53191489 0.52816901 0.51875 0.52707581 0.51546392 0.52464789 0.53068592 0.51194539 0.52836879 0.52631579] mean value: 0.5243337421694624 MCC on Blind test: 0.57 MCC on Training: 0.27 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12477422 0.11778212 0.13731694 0.1299541 0.12796903 0.1547761 0.15706682 0.1281333 0.1279521 0.10621834] mean value: 0.13119430541992189 key: score_time value: [0.02357841 0.02029967 0.02439928 0.0236578 0.02338505 0.02392721 0.02337933 0.02321219 0.02547455 0.01453209] mean value: 0.02258455753326416 key: test_mcc value: [0.54211097 0.14754319 0.39735971 0.57396402 0.52623481 0.65465367 0.22518867 0.41736501 0.26111648 0.61394061] mean value: 0.4359477149215141 key: train_mcc value: [0.90895793 0.90566264 0.90992717 0.90916885 0.90029831 0.88563951 0.90916885 0.92414557 0.89489292 0.90916885] mean value: 0.9057030593771422 key: test_fscore value: [0.73170732 0.6 0.66666667 0.76190476 0.74418605 0.81818182 0.55 0.63157895 0.62222222 0.79069767] mean value: 0.6917145454347293 key: train_fscore value: [0.95354523 0.95 0.95308642 0.95354523 0.94814815 0.9408867 0.95354523 0.96059113 0.94607843 0.95354523] mean value: 0.9512971760881455 key: test_precision value: [0.83333333 0.57692308 0.73684211 0.84210526 0.8 0.85714286 0.64705882 0.8 0.63636364 0.85 ] mean value: 0.7579769095713369 key: train_precision value: [0.97014925 0.98445596 0.97969543 0.97014925 0.97461929 0.96464646 0.97014925 0.98484848 0.965 0.97014925] mean value: 0.9733862643781729 key: test_recall value: [0.65217391 0.625 0.60869565 0.69565217 0.69565217 0.7826087 0.47826087 0.52173913 0.60869565 0.73913043] mean value: 0.6407608695652174 key: train_recall value: [0.9375 0.9178744 0.92788462 0.9375 0.92307692 0.91826923 0.9375 0.9375 0.92788462 0.9375 ] mean value: 0.9302489780750649 key: test_accuracy value: [0.76595745 0.57446809 0.69565217 0.7826087 0.76086957 0.82608696 0.60869565 0.69565217 0.63043478 0.80434783] mean value: 0.7144773358001849 key: train_accuracy value: [0.95421687 0.95180723 0.95432692 0.95432692 0.94951923 0.94230769 0.95432692 0.96153846 0.94711538 0.95432692] mean value: 0.9523812557924003 key: test_roc_auc value: [0.76358696 0.57336957 0.69565217 0.7826087 0.76086957 0.82608696 0.60869565 0.69565217 0.63043478 0.80434783] mean value: 0.7141304347826086 key: train_roc_auc value: [0.95425725 0.95172566 0.95432692 0.95432692 0.94951923 0.94230769 0.95432692 0.96153846 0.94711538 0.95432692] mean value: 0.9523771367521368 key: test_jcc value: [0.57692308 0.42857143 0.5 0.61538462 0.59259259 0.69230769 0.37931034 0.46153846 0.4516129 0.65384615] mean value: 0.5352087269217414 key: train_jcc value: [0.91121495 0.9047619 0.91037736 0.91121495 0.90140845 0.88837209 0.91121495 0.92417062 0.89767442 0.91121495] mean value: 0.907162465478246 MCC on Blind test: -0.11 MCC on Training: 0.44 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02279735 0.01039195 0.00924754 0.0091486 0.00983214 0.00998902 0.00966883 0.00896215 0.01093459 0.01054478] mean value: 0.011151695251464843 key: score_time value: [0.01719856 0.0146451 0.01215291 0.01267147 0.01431632 0.01247525 0.01377511 0.01602483 0.01523519 0.01774645] mean value: 0.01462411880493164 key: test_mcc value: [ 0.27586252 -0.15834857 0.13093073 0.30434783 0.35634832 0.53452248 0.08703883 0.26726124 0.08703883 0.43519414] mean value: 0.23201963614322674 key: train_mcc value: [0.53256679 0.58562934 0.55833795 0.51490531 0.53935989 0.55771809 0.52409298 0.53370936 0.58697277 0.5630273 ] mean value: 0.5496319788828729 key: test_fscore value: [0.62222222 0.49056604 0.54545455 0.65217391 0.63414634 0.78431373 0.53333333 0.58536585 0.55319149 0.72340426] mean value: 0.6124171717082427 key: train_fscore value: [0.76849642 0.7902439 0.77339901 0.75184275 0.76237624 0.77990431 0.76372315 0.76513317 0.78921569 0.78588235] mean value: 0.7730216994438206 key: test_precision value: [0.63636364 0.44827586 0.57142857 0.65217391 0.72222222 0.71428571 0.54545455 0.66666667 0.54166667 0.70833333] mean value: 0.62068711315338 key: train_precision value: [0.76303318 0.79802956 0.79292929 0.76884422 0.78571429 0.77619048 0.75829384 0.77073171 0.805 0.76958525] mean value: 0.7788351807581133 key: test_recall value: [0.60869565 0.54166667 0.52173913 0.65217391 0.56521739 0.86956522 0.52173913 0.52173913 0.56521739 0.73913043] mean value: 0.6106884057971014 key: train_recall value: [0.77403846 0.7826087 0.75480769 0.73557692 0.74038462 0.78365385 0.76923077 0.75961538 0.77403846 0.80288462] mean value: 0.7676839464882944 key: test_accuracy value: [0.63829787 0.42553191 0.56521739 0.65217391 0.67391304 0.76086957 0.54347826 0.63043478 0.54347826 0.7173913 ] mean value: 0.6150786308973173 key: train_accuracy value: [0.76626506 0.79277108 0.77884615 0.75721154 0.76923077 0.77884615 0.76201923 0.76682692 0.79326923 0.78125 ] mean value: 0.7746536144578313 key: test_roc_auc value: [0.63768116 0.42300725 0.56521739 0.65217391 0.67391304 0.76086957 0.54347826 0.63043478 0.54347826 0.7173913 ] mean value: 0.6147644927536232 key: train_roc_auc value: [0.76624628 0.79274666 0.77884615 0.75721154 0.76923077 0.77884615 0.76201923 0.76682692 0.79326923 0.78125 ] mean value: 0.7746492939427723 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.4516129 0.325 0.375 0.48387097 0.46428571 0.64516129 0.36363636 0.4137931 0.38235294 0.56666667] mean value: 0.44713799505038143 key: train_jcc value: [0.62403101 0.65322581 0.63052209 0.6023622 0.616 0.63921569 0.61776062 0.61960784 0.65182186 0.64728682] mean value: 0.6301833938507361 MCC on Blind test: 0.08 MCC on Training: 0.23 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05493784 0.04980683 0.1050837 0.06188655 0.07527876 0.06709623 0.04172897 0.05409098 0.07604313 0.0531671 ] mean value: 0.0639120101928711 key: score_time value: [0.03066516 0.0123446 0.02207637 0.02364993 0.01232982 0.01274228 0.01220989 0.02251005 0.02143025 0.0135951 ] mean value: 0.018355345726013182 key: test_mcc value: [0.44646172 0.4078185 0.30550505 0.74194083 0.61394061 0.34815531 0.30434783 0.48007936 0.21821789 0.61394061] mean value: 0.4480407698235111 key: train_mcc value: [0.70122872 0.71574571 0.73558542 0.72617138 0.71642067 0.70680429 0.71157136 0.75013872 0.73599383 0.72596993] mean value: 0.7225630030320067 key: test_fscore value: [0.71111111 0.73076923 0.63636364 0.86363636 0.79069767 0.68085106 0.65217391 0.72727273 0.59090909 0.79069767] mean value: 0.7174482485772635 key: train_fscore value: [0.85167464 0.85851319 0.86810552 0.86131387 0.85714286 0.85441527 0.85507246 0.87378641 0.86552567 0.86330935] mean value: 0.8608859242828029 key: test_precision value: [0.72727273 0.67857143 0.66666667 0.9047619 0.85 0.66666667 0.65217391 0.76190476 0.61904762 0.85 ] mean value: 0.7377065687935254 key: train_precision value: [0.84761905 0.85238095 0.86602871 0.87192118 0.86341463 0.84834123 0.8592233 0.88235294 0.88059701 0.86124402] mean value: 0.8633123032985284 key: test_recall value: [0.69565217 0.79166667 0.60869565 0.82608696 0.73913043 0.69565217 0.65217391 0.69565217 0.56521739 0.73913043] mean value: 0.7009057971014492 key: train_recall value: [0.85576923 0.8647343 0.87019231 0.85096154 0.85096154 0.86057692 0.85096154 0.86538462 0.85096154 0.86538462] mean value: 0.8585888145670755 key: test_accuracy value: [0.72340426 0.70212766 0.65217391 0.86956522 0.80434783 0.67391304 0.65217391 0.73913043 0.60869565 0.80434783] mean value: 0.7229879740980574 key: train_accuracy value: [0.85060241 0.85783133 0.86778846 0.86298077 0.85817308 0.85336538 0.85576923 0.875 0.86778846 0.86298077] mean value: 0.8612279888785913 key: test_roc_auc value: [0.72282609 0.70018116 0.65217391 0.86956522 0.80434783 0.67391304 0.65217391 0.73913043 0.60869565 0.80434783] mean value: 0.7227355072463768 key: train_roc_auc value: [0.85058993 0.85784792 0.86778846 0.86298077 0.85817308 0.85336538 0.85576923 0.875 0.86778846 0.86298077] mean value: 0.8612284002229655 key: test_jcc value: [0.55172414 0.57575758 0.46666667 0.76 0.65384615 0.51612903 0.48387097 0.57142857 0.41935484 0.65384615] mean value: 0.5652624098185834 key: train_jcc value: [0.74166667 0.75210084 0.76694915 0.75641026 0.75 0.74583333 0.74683544 0.77586207 0.76293103 0.75949367] mean value: 0.7558082466661091 MCC on Blind test: 0.12 MCC on Training: 0.45 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04932261 0.03872657 0.04063392 0.04069042 0.04098797 0.03910041 0.03995156 0.03804612 0.03975558 0.0461781 ] mean value: 0.041339325904846194 key: score_time value: [0.01418114 0.01201391 0.01285076 0.01310611 0.01347375 0.01320386 0.01320696 0.01334453 0.01269007 0.01324677] mean value: 0.01313178539276123 key: test_mcc value: [0.40653424 0.23315467 0.3927922 0.6092718 0.56736651 0.26111648 0.35634832 0.48566186 0.13043478 0.41736501] mean value: 0.3860045882315789 key: train_mcc value: [0.58180044 0.58657897 0.60677988 0.54970568 0.55062827 0.5878716 0.6164113 0.5878716 0.55979236 0.54932264] mean value: 0.5776762739742196 key: test_fscore value: [0.70833333 0.64 0.70833333 0.8 0.77272727 0.63829787 0.70588235 0.71428571 0.56521739 0.74074074] mean value: 0.6993818011006343 key: train_fscore value: [0.7972028 0.79812207 0.80841121 0.78240741 0.78440367 0.8 0.81308411 0.8 0.78801843 0.78139535] mean value: 0.7953045049182411 key: test_precision value: [0.68 0.61538462 0.68 0.81818182 0.80952381 0.625 0.64285714 0.78947368 0.56521739 0.64516129] mean value: 0.6870799751784842 key: train_precision value: [0.77375566 0.77625571 0.78636364 0.75446429 0.75 0.77477477 0.79090909 0.77477477 0.75663717 0.75675676] mean value: 0.7694691851306067 key: test_recall value: [0.73913043 0.66666667 0.73913043 0.7826087 0.73913043 0.65217391 0.7826087 0.65217391 0.56521739 0.86956522] mean value: 0.7188405797101449 key: train_recall value: [0.82211538 0.82125604 0.83173077 0.8125 0.82211538 0.82692308 0.83653846 0.82692308 0.82211538 0.80769231] mean value: 0.822990988480119 key: test_accuracy value: [0.70212766 0.61702128 0.69565217 0.80434783 0.7826087 0.63043478 0.67391304 0.73913043 0.56521739 0.69565217] mean value: 0.6906105457909343 key: train_accuracy value: [0.79036145 0.79277108 0.80288462 0.77403846 0.77403846 0.79326923 0.80769231 0.79326923 0.77884615 0.77403846] mean value: 0.7881209453197406 key: test_roc_auc value: [0.70289855 0.61594203 0.69565217 0.80434783 0.7826087 0.63043478 0.67391304 0.73913043 0.56521739 0.69565217] mean value: 0.6905797101449276 key: train_roc_auc value: [0.79028475 0.79283956 0.80288462 0.77403846 0.77403846 0.79326923 0.80769231 0.79326923 0.77884615 0.77403846] mean value: 0.7881201226309922 key: test_jcc value: [0.5483871 0.47058824 0.5483871 0.66666667 0.62962963 0.46875 0.54545455 0.55555556 0.39393939 0.58823529] mean value: 0.5415593514205942 key: train_jcc value: [0.6627907 0.6640625 0.67843137 0.64258555 0.64528302 0.66666667 0.68503937 0.66666667 0.65019011 0.64122137] mean value: 0.6602937331948476 MCC on Blind test: 0.44 MCC on Training: 0.39 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.65846992 0.49982858 0.54187489 0.54448724 0.644912 0.52485704 0.54042053 0.50495243 0.62579131 0.51830578] mean value: 0.5603899717330932 key: score_time value: [0.01211357 0.01222873 0.01208639 0.01216078 0.01500583 0.01463413 0.01483035 0.01203537 0.0150311 0.01212764] mean value: 0.013225388526916505 key: test_mcc value: [0.23369565 0.19048769 0.48566186 0.65217391 0.56736651 0.52223297 0.35082321 0.4454354 0.21821789 0.51011279] mean value: 0.41762078907519495 key: train_mcc value: [0.49911686 0.57227048 0.47640772 0.51046561 0.73626647 0.7791704 0.73599383 0.54468744 0.72145407 0.51077368] mean value: 0.608660655607528 key: test_fscore value: [0.60869565 0.62745098 0.76 0.82608696 0.77272727 0.75555556 0.65116279 0.68292683 0.59090909 0.77777778] mean value: 0.7053292906023472 key: train_fscore value: [0.75471698 0.79156909 0.74352941 0.76168224 0.86486486 0.88780488 0.86552567 0.77958237 0.86255924 0.7627907 ] mean value: 0.807462544379368 key: test_precision value: [0.60869565 0.59259259 0.7037037 0.82608696 0.80952381 0.77272727 0.7 0.77777778 0.61904762 0.67741935] mean value: 0.7087574738907139 key: train_precision value: [0.74074074 0.76818182 0.7281106 0.74090909 0.88442211 0.9009901 0.88059701 0.75336323 0.85046729 0.73873874] mean value: 0.7986520730555945 key: test_recall value: [0.60869565 0.66666667 0.82608696 0.82608696 0.73913043 0.73913043 0.60869565 0.60869565 0.56521739 0.91304348] mean value: 0.7101449275362318 key: train_recall value: [0.76923077 0.81642512 0.75961538 0.78365385 0.84615385 0.875 0.85096154 0.80769231 0.875 0.78846154] mean value: 0.8172194351542178 key: test_accuracy value: [0.61702128 0.59574468 0.73913043 0.82608696 0.7826087 0.76086957 0.67391304 0.7173913 0.60869565 0.73913043] mean value: 0.7060592044403331 key: train_accuracy value: [0.74939759 0.78554217 0.73798077 0.75480769 0.86778846 0.88942308 0.86778846 0.77163462 0.86057692 0.75480769] mean value: 0.8039747451343837 key: test_roc_auc value: [0.61684783 0.5942029 0.73913043 0.82608696 0.7826087 0.76086957 0.67391304 0.7173913 0.60869565 0.73913043] mean value: 0.7058876811594204 key: train_roc_auc value: [0.74934968 0.78561641 0.73798077 0.75480769 0.86778846 0.88942308 0.86778846 0.77163462 0.86057692 0.75480769] mean value: 0.8039773782980305 key: test_jcc value: [0.4375 0.45714286 0.61290323 0.7037037 0.62962963 0.60714286 0.48275862 0.51851852 0.41935484 0.63636364] mean value: 0.5505017887706987 key: train_jcc value: [0.60606061 0.65503876 0.5917603 0.61509434 0.76190476 0.79824561 0.76293103 0.63878327 0.75833333 0.61654135] mean value: 0.6804693372100016 MCC on Blind test: 0.16 MCC on Training: 0.42 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.09042621 1.56228232 0.89076519 0.67291188 1.76593804 0.86217737 0.74632692 1.52349877 1.97562551 1.38716817] mean value: 1.2477120399475097 key: score_time value: [0.01247954 0.01282144 0.01247883 0.01246047 0.01253963 0.01237726 0.01258707 0.01262426 0.01554537 0.01234484] mean value: 0.012825870513916015 key: test_mcc value: [0.36116212 0.23315467 0.34815531 0.62360956 0.63900965 0.27386128 0.35082321 0.49541508 0.36514837 0.36514837] mean value: 0.4055487624993144 key: train_mcc value: [0.62411447 0.74648605 0.62981497 0.51916999 0.66369172 0.65096826 0.53996876 0.74466871 0.76124847 0.63100907] mean value: 0.6511140481800091 key: test_fscore value: [0.66666667 0.64 0.68085106 0.82352941 0.83018868 0.56410256 0.65116279 0.7 0.71698113 0.71698113] mean value: 0.6990463440457625 key: train_fscore value: [0.81339713 0.86716792 0.81534772 0.77966102 0.84120172 0.80106101 0.73796791 0.85106383 0.88487585 0.82683983] mean value: 0.8218583930020247 key: test_precision value: [0.68181818 0.61538462 0.66666667 0.75 0.73333333 0.6875 0.7 0.82352941 0.63333333 0.63333333] mean value: 0.6924898875634169 key: train_precision value: [0.80952381 0.90104167 0.81339713 0.6969697 0.75968992 0.89349112 0.8313253 0.95238095 0.83404255 0.7519685 ] mean value: 0.8243830659802021 key: test_recall value: [0.65217391 0.66666667 0.69565217 0.91304348 0.95652174 0.47826087 0.60869565 0.60869565 0.82608696 0.82608696] mean value: 0.7231884057971015 key: train_recall value: [0.81730769 0.83574879 0.81730769 0.88461538 0.94230769 0.72596154 0.66346154 0.76923077 0.94230769 0.91826923] mean value: 0.8316518023039763 key: test_accuracy value: [0.68085106 0.61702128 0.67391304 0.80434783 0.80434783 0.63043478 0.67391304 0.73913043 0.67391304 0.67391304] mean value: 0.6971785383903792 key: train_accuracy value: [0.81204819 0.87228916 0.81490385 0.75 0.82211538 0.81971154 0.76442308 0.86538462 0.87740385 0.80769231] mean value: 0.8205971964782206 key: test_roc_auc value: [0.68025362 0.61594203 0.67391304 0.80434783 0.80434783 0.63043478 0.67391304 0.73913043 0.67391304 0.67391304] mean value: 0.6970108695652174 key: train_roc_auc value: [0.81203549 0.87220132 0.81490385 0.75 0.82211538 0.81971154 0.76442308 0.86538462 0.87740385 0.80769231] mean value: 0.8205871423262726 key: test_jcc value: [0.5 0.47058824 0.51612903 0.7 0.70967742 0.39285714 0.48275862 0.53846154 0.55882353 0.55882353] mean value: 0.5428119047738886 key: train_jcc value: [0.68548387 0.76548673 0.68825911 0.63888889 0.72592593 0.66814159 0.58474576 0.74074074 0.79352227 0.70479705] mean value: 0.699599193230793 MCC on Blind test: 0.24 MCC on Training: 0.41 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01377177 0.01348257 0.01117182 0.0098145 0.00972342 0.00981712 0.0098393 0.00942993 0.00981474 0.01062608] mean value: 0.010749125480651855 key: score_time value: [0.01192331 0.00975776 0.01004362 0.00973487 0.00851321 0.00943375 0.00890803 0.00938606 0.0093298 0.00933337] mean value: 0.009636378288269043 key: test_mcc value: [0.15385544 0.06022427 0.36514837 0.52623481 0.30434783 0.1351132 0.26311741 0.3927922 0.04415108 0.23186945] mean value: 0.2476854061398283 key: train_mcc value: [0.28061444 0.30640725 0.26870862 0.26781515 0.25009918 0.29288162 0.27207557 0.26405947 0.25340842 0.2697148 ] mean value: 0.272578451197021 key: test_fscore value: [0.6 0.60714286 0.71698113 0.7755102 0.65217391 0.61538462 0.65306122 0.70833333 0.56 0.66666667] mean value: 0.6555253946217852 key: train_fscore value: [0.66666667 0.67849224 0.66225166 0.65924276 0.65645514 0.67256637 0.65919283 0.66079295 0.65333333 0.66520788] mean value: 0.6634201824818666 key: test_precision value: [0.55555556 0.53125 0.63333333 0.73076923 0.65217391 0.55172414 0.61538462 0.68 0.51851852 0.58064516] mean value: 0.604935446582609 key: train_precision value: [0.61983471 0.62704918 0.6122449 0.61410788 0.60240964 0.62295082 0.61764706 0.6097561 0.60743802 0.61044177] mean value: 0.6143880071056336 key: test_recall value: [0.65217391 0.70833333 0.82608696 0.82608696 0.65217391 0.69565217 0.69565217 0.73913043 0.60869565 0.7826087 ] mean value: 0.7186594202898551 key: train_recall value: [0.72115385 0.73913043 0.72115385 0.71153846 0.72115385 0.73076923 0.70673077 0.72115385 0.70673077 0.73076923] mean value: 0.7210284280936455 key: test_accuracy value: [0.57446809 0.53191489 0.67391304 0.76086957 0.65217391 0.56521739 0.63043478 0.69565217 0.52173913 0.60869565] mean value: 0.6215078630897317 key: train_accuracy value: [0.63855422 0.65060241 0.63221154 0.63221154 0.62259615 0.64423077 0.63461538 0.62980769 0.625 0.63221154] mean value: 0.6342041241890639 key: test_roc_auc value: [0.57608696 0.52807971 0.67391304 0.76086957 0.65217391 0.56521739 0.63043478 0.69565217 0.52173913 0.60869565] mean value: 0.6212862318840581 key: train_roc_auc value: [0.6383547 0.65081522 0.63221154 0.63221154 0.62259615 0.64423077 0.63461538 0.62980769 0.625 0.63221154] mean value: 0.6342054533630621 key: test_jcc value: [0.42857143 0.43589744 0.55882353 0.63333333 0.48387097 0.44444444 0.48484848 0.5483871 0.38888889 0.5 ] mean value: 0.490706560991191 key: train_jcc value: [0.5 0.51342282 0.4950495 0.49169435 0.48859935 0.50666667 0.4916388 0.49342105 0.48514851 0.49836066] mean value: 0.49640017103101697 MCC on Blind test: 0.38 MCC on Training: 0.25 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01166058 0.0116086 0.01229978 0.00949788 0.00991869 0.00991797 0.00964618 0.00991416 0.01003218 0.01076627] mean value: 0.010526227951049804 key: score_time value: [0.00998044 0.01004267 0.00847435 0.00851846 0.00863266 0.00909328 0.00949359 0.0092237 0.00973463 0.00893831] mean value: 0.00921320915222168 key: test_mcc value: [ 0.19048769 -0.06533586 0.39735971 0.6092718 0.48566186 0.30434783 0.30550505 0.30550505 0.1754116 0.30550505] mean value: 0.301371976769785 key: train_mcc value: [0.40342229 0.40723709 0.39468717 0.38002296 0.36058109 0.39926463 0.41839013 0.42848422 0.41415134 0.36538462] mean value: 0.39716255387466765 key: test_fscore value: [0.55813953 0.48979592 0.72 0.8 0.71428571 0.65217391 0.63636364 0.66666667 0.55813953 0.66666667] mean value: 0.6462231585160951 key: train_fscore value: [0.69154229 0.70361446 0.68965517 0.68459658 0.67951807 0.69437653 0.70559611 0.70617284 0.6980198 0.68269231] mean value: 0.6935784152460603 key: test_precision value: [0.6 0.48 0.66666667 0.81818182 0.78947368 0.65217391 0.66666667 0.64 0.6 0.64 ] mean value: 0.6553162748769156 key: train_precision value: [0.71649485 0.70192308 0.70707071 0.69651741 0.68115942 0.70646766 0.71428571 0.72588832 0.71938776 0.68269231] mean value: 0.7051887226224488 key: test_recall value: [0.52173913 0.5 0.7826087 0.7826087 0.65217391 0.65217391 0.60869565 0.69565217 0.52173913 0.69565217] mean value: 0.6413043478260869 key: train_recall value: [0.66826923 0.70531401 0.67307692 0.67307692 0.67788462 0.68269231 0.69711538 0.6875 0.67788462 0.68269231] mean value: 0.6825506317354143 key: test_accuracy value: [0.59574468 0.46808511 0.69565217 0.80434783 0.73913043 0.65217391 0.65217391 0.65217391 0.58695652 0.65217391] mean value: 0.6498612395929696 key: train_accuracy value: [0.70120482 0.70361446 0.69711538 0.68990385 0.68028846 0.69951923 0.70913462 0.71394231 0.70673077 0.68269231] mean value: 0.6984146200185357 key: test_roc_auc value: [0.5942029 0.4673913 0.69565217 0.80434783 0.73913043 0.65217391 0.65217391 0.65217391 0.58695652 0.65217391] mean value: 0.6496376811594203 key: train_roc_auc value: [0.70128437 0.70361854 0.69711538 0.68990385 0.68028846 0.69951923 0.70913462 0.71394231 0.70673077 0.68269231] mean value: 0.69842298402081 key: test_jcc value: [0.38709677 0.32432432 0.5625 0.66666667 0.55555556 0.48387097 0.46666667 0.5 0.38709677 0.5 ] mean value: 0.48337777293422446 key: train_jcc value: [0.52851711 0.54275093 0.52631579 0.5204461 0.51459854 0.53183521 0.54511278 0.54580153 0.53612167 0.51824818] mean value: 0.5309747828759372 MCC on Blind test: 0.29 MCC on Training: 0.3 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01628017 0.01726699 0.01780272 0.01799083 0.01981378 0.02159739 0.02038717 0.01910067 0.01709032 0.0218308 ] mean value: 0.01891608238220215 key: score_time value: [0.00965619 0.01286459 0.01294851 0.01259041 0.01258898 0.01290655 0.01347232 0.01342225 0.01314354 0.01277328] mean value: 0.012636661529541016 key: test_mcc value: [0.39631454 0.2395526 0.3441236 0.34921515 0.42163702 0.32461723 0.15430335 0.48566186 0.2548236 0.43452409] mean value: 0.34047730462371895 key: train_mcc value: [0.51494155 0.40401409 0.36342738 0.27374331 0.44546531 0.44910292 0.26545248 0.50105552 0.49986276 0.51442616] mean value: 0.42314914793620717 key: test_fscore value: [0.72727273 0.45714286 0.4516129 0.35714286 0.74576271 0.7037037 0.22222222 0.76 0.47058824 0.75 ] mean value: 0.5645448217868699 key: train_fscore value: [0.77777778 0.4911032 0.39230769 0.24472574 0.75435203 0.75521822 0.25104603 0.77338877 0.64396285 0.77959184] mean value: 0.5863474142120915 key: test_precision value: [0.625 0.72727273 0.875 1. 0.61111111 0.61290323 0.75 0.7037037 0.72727273 0.63636364] mean value: 0.7268627131530357 key: train_precision value: [0.7 0.93243243 0.98076923 1. 0.63106796 0.62382445 0.96774194 0.68131868 0.90434783 0.67730496] mean value: 0.8098807483205887 key: test_recall value: [0.86956522 0.33333333 0.30434783 0.2173913 0.95652174 0.82608696 0.13043478 0.82608696 0.34782609 0.91304348] mean value: 0.572463768115942 key: train_recall value: [0.875 0.33333333 0.24519231 0.13942308 0.9375 0.95673077 0.14423077 0.89423077 0.5 0.91826923] mean value: 0.5943910256410256 key: test_accuracy value: [0.68085106 0.59574468 0.63043478 0.60869565 0.67391304 0.65217391 0.54347826 0.73913043 0.60869565 0.69565217] mean value: 0.6428769657724329 key: train_accuracy value: [0.74939759 0.65542169 0.62019231 0.56971154 0.69471154 0.68990385 0.56971154 0.73798077 0.72355769 0.74038462] mean value: 0.675097312326228 key: test_roc_auc value: [0.68478261 0.60144928 0.63043478 0.60869565 0.67391304 0.65217391 0.54347826 0.73913043 0.60869565 0.69565217] mean value: 0.643840579710145 key: train_roc_auc value: [0.7490942 0.65464744 0.62019231 0.56971154 0.69471154 0.68990385 0.56971154 0.73798077 0.72355769 0.74038462] mean value: 0.6749895484949834 key: test_jcc value: [0.57142857 0.2962963 0.29166667 0.2173913 0.59459459 0.54285714 0.125 0.61290323 0.30769231 0.6 ] mean value: 0.41598301096898566 key: train_jcc value: [0.63636364 0.3254717 0.24401914 0.13942308 0.60559006 0.60670732 0.14354067 0.63050847 0.47488584 0.63879599] mean value: 0.44453059051445354 MCC on Blind test: 0.13 MCC on Training: 0.34 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.026016 0.02925229 0.028826 0.02918983 0.0286305 0.02844596 0.02886105 0.02947593 0.02945757 0.02919173] mean value: 0.028734683990478516 key: score_time value: [0.01308203 0.01291299 0.01382852 0.0149231 0.01483059 0.01441503 0.01490808 0.01421738 0.01424527 0.01368785] mean value: 0.014105081558227539 key: test_mcc value: [0.5326087 0.16435695 0.30434783 0.75056834 0.30550505 0.23186945 0.28347335 0.47826087 0.34815531 0.65217391] mean value: 0.4051319751179186 key: train_mcc value: [0.68928162 0.70753211 0.82983125 0.90205634 0.89839349 0.70291588 0.66290751 0.91350377 0.93424838 0.92324766] mean value: 0.816391801110834 key: test_fscore value: [0.76595745 0.5 0.65217391 0.85714286 0.66666667 0.66666667 0.54054054 0.73913043 0.66666667 0.82608696] mean value: 0.6881032148839734 key: train_fscore value: [0.85281385 0.8 0.9 0.94736842 0.94444444 0.85840708 0.75820896 0.9569378 0.96534653 0.96190476] mean value: 0.8945431848782116 key: test_precision value: [0.75 0.625 0.65217391 0.94736842 0.64 0.58064516 0.71428571 0.73913043 0.68181818 0.82608696] mean value: 0.7156508782794677 key: train_precision value: [0.77559055 1. 0.99418605 0.9895288 0.99468085 0.79508197 1. 0.95238095 0.99489796 0.95283019] mean value: 0.9449177312025064 key: test_recall value: [0.7826087 0.41666667 0.65217391 0.7826087 0.69565217 0.7826087 0.43478261 0.73913043 0.65217391 0.82608696] mean value: 0.6764492753623188 key: train_recall value: [0.94711538 0.66666667 0.82211538 0.90865385 0.89903846 0.93269231 0.61057692 0.96153846 0.9375 0.97115385] mean value: 0.8657051282051282 key: test_accuracy value: [0.76595745 0.57446809 0.65217391 0.86956522 0.65217391 0.60869565 0.63043478 0.73913043 0.67391304 0.82608696] mean value: 0.6992599444958372 key: train_accuracy value: [0.83614458 0.83373494 0.90865385 0.94951923 0.94711538 0.84615385 0.80528846 0.95673077 0.96634615 0.96153846] mean value: 0.9011225671918442 key: test_roc_auc value: [0.76630435 0.57789855 0.65217391 0.86956522 0.65217391 0.60869565 0.63043478 0.73913043 0.67391304 0.82608696] mean value: 0.6996376811594203 key: train_roc_auc value: [0.83587653 0.83333333 0.90865385 0.94951923 0.94711538 0.84615385 0.80528846 0.95673077 0.96634615 0.96153846] mean value: 0.901055602006689 key: test_jcc value: [0.62068966 0.33333333 0.48387097 0.75 0.5 0.5 0.37037037 0.5862069 0.5 0.7037037 ] mean value: 0.5348174926873481 key: train_jcc value: [0.74339623 0.66666667 0.81818182 0.9 0.89473684 0.75193798 0.61057692 0.91743119 0.93301435 0.9266055 ] mean value: 0.8162547512256582 MCC on Blind test: -0.03 MCC on Training: 0.41 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.65391111 0.70671606 0.6285758 0.64390588 0.65715933 0.66632128 0.74775958 0.69262457 0.65364528 0.64024353] mean value: 0.669086241722107 key: score_time value: [0.16332865 0.16557908 0.19087029 0.13187909 0.21399021 0.14795852 0.2209816 0.14586735 0.16121674 0.17602563] mean value: 0.17176971435546876 key: test_mcc value: [0.62091661 0.36265926 0.73913043 0.91304348 0.70164642 0.82922798 0.69631062 0.57396402 0.52223297 0.73913043] mean value: 0.66982622320686 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.79069767 0.70588235 0.86956522 0.95652174 0.85714286 0.90909091 0.84444444 0.76190476 0.75555556 0.86956522] mean value: 0.8320370729411353 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 0.66666667 0.86956522 0.95652174 0.80769231 0.95238095 0.86363636 0.84210526 0.77272727 0.86956522] mean value: 0.84508610001745 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.75 0.86956522 0.95652174 0.91304348 0.86956522 0.82608696 0.69565217 0.73913043 0.86956522] mean value: 0.8228260869565217 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80851064 0.68085106 0.86956522 0.95652174 0.84782609 0.91304348 0.84782609 0.7826087 0.76086957 0.86956522] mean value: 0.8337187789084182 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.80706522 0.67934783 0.86956522 0.95652174 0.84782609 0.91304348 0.84782609 0.7826087 0.76086957 0.86956522] mean value: 0.8334239130434783 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.65384615 0.54545455 0.76923077 0.91666667 0.75 0.83333333 0.73076923 0.61538462 0.60714286 0.76923077] mean value: 0.7191058941058941 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.52 MCC on Training: 0.67 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.07220221 0.97767377 0.98466778 1.06171012 0.99993134 1.00354576 0.9617269 0.97479177 0.99813676 0.99705076] mean value: 1.0031437158584595 key: score_time value: [0.26016784 0.23880696 0.20577288 0.25762105 0.24268794 0.16404605 0.26422739 0.22964311 0.2223196 0.21898341] mean value: 0.23042762279510498 key: test_mcc value: [0.57427536 0.31884058 0.6092718 0.82922798 0.74194083 0.69631062 0.6092718 0.61394061 0.52623481 0.69631062] mean value: 0.6215625016162736 key: train_mcc value: [0.88942064 0.90398524 0.88992718 0.86574488 0.88063245 0.86574488 0.8895156 0.91853466 0.89427211 0.89954801] mean value: 0.8897325654790244 key: test_fscore value: [0.7826087 0.66666667 0.80851064 0.91666667 0.86363636 0.84444444 0.8 0.79069767 0.74418605 0.85106383] mean value: 0.8068481026081654 key: train_fscore value: [0.94403893 0.95098039 0.94376528 0.93170732 0.93857494 0.93170732 0.94430993 0.95863747 0.9468599 0.94865526] mean value: 0.9439236732544636 key: test_precision value: [0.7826087 0.66666667 0.79166667 0.88 0.9047619 0.86363636 0.81818182 0.85 0.8 0.83333333] mean value: 0.8190855448898926 key: train_precision value: [0.95566502 0.96517413 0.960199 0.94554455 0.95979899 0.94554455 0.95121951 0.97044335 0.95145631 0.96517413] mean value: 0.9570219564826328 key: test_recall value: [0.7826087 0.66666667 0.82608696 0.95652174 0.82608696 0.82608696 0.7826087 0.73913043 0.69565217 0.86956522] mean value: 0.7971014492753623 key: train_recall value: [0.93269231 0.93719807 0.92788462 0.91826923 0.91826923 0.91826923 0.9375 0.94711538 0.94230769 0.93269231] mean value: 0.9312198067632851 key: test_accuracy value: [0.78723404 0.65957447 0.80434783 0.91304348 0.86956522 0.84782609 0.80434783 0.80434783 0.76086957 0.84782609] mean value: 0.8098982423681778 key: train_accuracy value: [0.94457831 0.95180723 0.94471154 0.93269231 0.93990385 0.93269231 0.94471154 0.95913462 0.94711538 0.94951923] mean value: 0.9446866311399443 key: test_roc_auc value: [0.78713768 0.65942029 0.80434783 0.91304348 0.86956522 0.84782609 0.80434783 0.80434783 0.76086957 0.84782609] mean value: 0.8098731884057973 key: train_roc_auc value: [0.94460702 0.95177211 0.94471154 0.93269231 0.93990385 0.93269231 0.94471154 0.95913462 0.94711538 0.94951923] mean value: 0.9446859903381641 key: test_jcc value: [0.64285714 0.5 0.67857143 0.84615385 0.76 0.73076923 0.66666667 0.65384615 0.59259259 0.74074074] mean value: 0.6812197802197801 key: train_jcc value: [0.89400922 0.90654206 0.89351852 0.87214612 0.88425926 0.87214612 0.89449541 0.92056075 0.89908257 0.90232558] mean value: 0.8939085598595604 MCC on Blind test: 0.58 MCC on Training: 0.62 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02615476 0.03774023 0.03806281 0.03927231 0.03853655 0.03823352 0.03972077 0.03852391 0.03850031 0.03824496] mean value: 0.03729901313781738 key: score_time value: [0.02351141 0.02280307 0.02250385 0.02180338 0.02409363 0.02324724 0.02352285 0.02175927 0.02026463 0.02323651] mean value: 0.02267458438873291 key: test_mcc value: [0.40653424 0.23435724 0.30434783 0.6092718 0.48007936 0.3927922 0.34815531 0.56736651 0.0877058 0.52623481] mean value: 0.39568451030153046 key: train_mcc value: [0.61116784 0.64028968 0.63039809 0.6111493 0.61609705 0.62140718 0.6449766 0.62546279 0.64940632 0.61434435] mean value: 0.62646991977948 key: test_fscore value: [0.70833333 0.65384615 0.65217391 0.8 0.72727273 0.70833333 0.68085106 0.77272727 0.57142857 0.7755102 ] mean value: 0.705047657289629 key: train_fscore value: [0.81206497 0.82517483 0.81882353 0.80941176 0.81220657 0.81585082 0.82629108 0.81603774 0.82742317 0.81548975] mean value: 0.8178774206050944 key: test_precision value: [0.68 0.60714286 0.65217391 0.81818182 0.76190476 0.68 0.66666667 0.80952381 0.53846154 0.73076923] mean value: 0.694482459569416 key: train_precision value: [0.78475336 0.7972973 0.80184332 0.79262673 0.79357798 0.7918552 0.80733945 0.80092593 0.81395349 0.77489177] mean value: 0.7959064530611311 key: test_recall value: [0.73913043 0.70833333 0.65217391 0.7826087 0.69565217 0.73913043 0.69565217 0.73913043 0.60869565 0.82608696] mean value: 0.7186594202898551 key: train_recall value: [0.84134615 0.85507246 0.83653846 0.82692308 0.83173077 0.84134615 0.84615385 0.83173077 0.84134615 0.86057692] mean value: 0.8412764771460424 key: test_accuracy value: [0.70212766 0.61702128 0.65217391 0.80434783 0.73913043 0.69565217 0.67391304 0.7826087 0.54347826 0.76086957] mean value: 0.6971322849213691 key: train_accuracy value: [0.80481928 0.81927711 0.81490385 0.80528846 0.80769231 0.81009615 0.82211538 0.8125 0.82451923 0.80528846] mean value: 0.8126500231696013 key: test_roc_auc value: [0.70289855 0.61503623 0.65217391 0.80434783 0.73913043 0.69565217 0.67391304 0.7826087 0.54347826 0.76086957] mean value: 0.6970108695652174 key: train_roc_auc value: [0.80473105 0.81936315 0.81490385 0.80528846 0.80769231 0.81009615 0.82211538 0.8125 0.82451923 0.80528846] mean value: 0.8126498049052395 key: test_jcc value: [0.5483871 0.48571429 0.48387097 0.66666667 0.57142857 0.5483871 0.51612903 0.62962963 0.4 0.63333333] mean value: 0.5483546680320874 key: train_jcc value: [0.68359375 0.70238095 0.69322709 0.6798419 0.68379447 0.68897638 0.704 0.68924303 0.70564516 0.68846154] mean value: 0.6919164263243845 MCC on Blind test: 0.3 MCC on Training: 0.4 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.09794545 0.11339998 0.11237288 0.11307669 0.11359906 0.11781859 0.1265862 0.13010955 0.11497927 0.11265349] mean value: 0.1152541160583496 key: score_time value: [0.02171326 0.02172208 0.02028513 0.02393484 0.02050996 0.02318072 0.0237627 0.02239275 0.0212965 0.02005339] mean value: 0.021885132789611815 key: test_mcc value: [0.32123465 0.23435724 0.26111648 0.6092718 0.52223297 0.34815531 0.30905755 0.4454354 0.30550505 0.51011279] mean value: 0.38664792284770366 key: train_mcc value: [0.54853491 0.57769929 0.68319782 0.53504695 0.50692705 0.69230769 0.5778856 0.55934964 0.53041777 0.52077381] mean value: 0.5732140525535063 key: test_fscore value: [0.66666667 0.65384615 0.63829787 0.8 0.75555556 0.68085106 0.68 0.68292683 0.66666667 0.77777778] mean value: 0.7002588585951327 key: train_fscore value: [0.78240741 0.79534884 0.84433962 0.774942 0.76321839 0.84615385 0.79439252 0.78703704 0.77314815 0.76851852] mean value: 0.7929506326644482 key: test_precision value: [0.64 0.60714286 0.625 0.81818182 0.77272727 0.66666667 0.62962963 0.77777778 0.64 0.67741935] mean value: 0.6854545376964732 key: train_precision value: [0.75446429 0.76681614 0.8287037 0.74887892 0.73127753 0.84615385 0.77272727 0.75892857 0.74553571 0.74107143] mean value: 0.7694557422889046 key: test_recall value: [0.69565217 0.70833333 0.65217391 0.7826087 0.73913043 0.69565217 0.73913043 0.60869565 0.69565217 0.91304348] mean value: 0.7230072463768116 key: train_recall value: [0.8125 0.82608696 0.86057692 0.80288462 0.79807692 0.84615385 0.81730769 0.81730769 0.80288462 0.79807692] mean value: 0.818185618729097 key: test_accuracy value: [0.65957447 0.61702128 0.63043478 0.80434783 0.76086957 0.67391304 0.65217391 0.7173913 0.65217391 0.73913043] mean value: 0.6907030527289547 key: train_accuracy value: [0.77349398 0.78795181 0.84134615 0.76682692 0.75240385 0.84615385 0.78846154 0.77884615 0.76442308 0.75961538] mean value: 0.7859522706209454 key: test_roc_auc value: [0.66032609 0.61503623 0.63043478 0.80434783 0.76086957 0.67391304 0.65217391 0.7173913 0.65217391 0.73913043] mean value: 0.6905797101449275 key: train_roc_auc value: [0.77339976 0.78804348 0.84134615 0.76682692 0.75240385 0.84615385 0.78846154 0.77884615 0.76442308 0.75961538] mean value: 0.7859520159791898 key: test_jcc value: [0.5 0.48571429 0.46875 0.66666667 0.60714286 0.51612903 0.51515152 0.51851852 0.5 0.63636364] mean value: 0.5414436511815545 key: train_jcc value: [0.64258555 0.66023166 0.73061224 0.63257576 0.61710037 0.73333333 0.65891473 0.64885496 0.63018868 0.62406015] mean value: 0.6578457439252176 MCC on Blind test: 0.3 MCC on Training: 0.39 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.04057384 0.01980925 0.02304554 0.02324843 0.02221704 0.02156734 0.02276373 0.02288294 0.01848674 0.01890254] mean value: 0.023349738121032713 key: score_time value: [0.01372242 0.01210165 0.01314664 0.01326632 0.01364923 0.01329327 0.01312923 0.01439667 0.01166677 0.01166487] mean value: 0.013003706932067871 key: test_mcc value: [0.36231884 0.1918812 0.35082321 0.6092718 0.6092718 0.35082321 0.34815531 0.52623481 0.30434783 0.39735971] mean value: 0.4050487708179881 key: train_mcc value: [0.59647218 0.60209995 0.56994856 0.55014077 0.57403585 0.59217483 0.5919005 0.59333118 0.53440167 0.57360889] mean value: 0.5778114373759692 key: test_fscore value: [0.68085106 0.64150943 0.69387755 0.8 0.8 0.69387755 0.68085106 0.74418605 0.65217391 0.72 ] mean value: 0.710732662321776 key: train_fscore value: [0.80465116 0.80742459 0.79357798 0.78341014 0.79445727 0.80093677 0.8 0.80369515 0.77283372 0.79350348] mean value: 0.7954490273682403 key: test_precision value: [0.66666667 0.5862069 0.65384615 0.81818182 0.81818182 0.65384615 0.66666667 0.8 0.65217391 0.66666667] mean value: 0.6982436753651148 key: train_precision value: [0.77927928 0.77678571 0.75877193 0.75221239 0.76444444 0.78082192 0.78341014 0.77333333 0.75342466 0.76681614] mean value: 0.7689299947636935 key: test_recall value: [0.69565217 0.70833333 0.73913043 0.7826087 0.7826087 0.73913043 0.69565217 0.69565217 0.65217391 0.7826087 ] mean value: 0.7273550724637682 key: train_recall value: [0.83173077 0.84057971 0.83173077 0.81730769 0.82692308 0.82211538 0.81730769 0.83653846 0.79326923 0.82211538] mean value: 0.8239618171683389 key: test_accuracy value: [0.68085106 0.59574468 0.67391304 0.80434783 0.80434783 0.67391304 0.67391304 0.76086957 0.65217391 0.69565217] mean value: 0.701572617946346 key: train_accuracy value: [0.79759036 0.8 0.78365385 0.77403846 0.78605769 0.79567308 0.79567308 0.79567308 0.76682692 0.78605769] mean value: 0.7881244207599629 key: test_roc_auc value: [0.68115942 0.5932971 0.67391304 0.80434783 0.80434783 0.67391304 0.67391304 0.76086957 0.65217391 0.69565217] mean value: 0.7013586956521738 key: train_roc_auc value: [0.7975079 0.80009755 0.78365385 0.77403846 0.78605769 0.79567308 0.79567308 0.79567308 0.76682692 0.78605769] mean value: 0.7881259290226683 key: test_jcc value: [0.51612903 0.47222222 0.53125 0.66666667 0.66666667 0.53125 0.51612903 0.59259259 0.48387097 0.5625 ] mean value: 0.5539277180406212 key: train_jcc value: [0.67315175 0.6770428 0.65779468 0.64393939 0.65900383 0.66796875 0.66666667 0.67181467 0.62977099 0.65769231] mean value: 0.6604845843232343 MCC on Blind test: 0.26 MCC on Training: 0.41 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01389003 0.01815581 0.02683401 0.02429581 0.019243 0.02160692 0.02259064 0.01758099 0.02391267 0.02240849] mean value: 0.021051836013793946 key: score_time value: [0.00902963 0.01129436 0.01220512 0.01226878 0.01221442 0.01208591 0.01213479 0.01215219 0.01225257 0.0125587 ] mean value: 0.01181964874267578 key: test_mcc value: [-0.00649077 0.37275718 0.30261377 0.58549055 0.48566186 0.26413527 0.33796318 0.3927922 0.2173913 0.41736501] mean value: 0.33696795611476615 key: train_mcc value: [0.3219749 0.58458426 0.44869358 0.55900195 0.53395631 0.33065591 0.55278261 0.43166121 0.6251156 0.51705321] mean value: 0.49054795344775703 key: test_fscore value: [0.63636364 0.65116279 0.4 0.80769231 0.71428571 0.23076923 0.55555556 0.70833333 0.60869565 0.74074074] mean value: 0.6053598961612106 key: train_fscore value: [0.71428571 0.71084337 0.51245552 0.79569892 0.77068558 0.32931727 0.66875 0.74534161 0.81067961 0.77657267] mean value: 0.6834630271467746 key: test_precision value: [0.48837209 0.73684211 0.85714286 0.72413793 0.78947368 1. 0.76923077 0.68 0.60869565 0.64516129] mean value: 0.7299056382401543 key: train_precision value: [0.56010929 0.944 0.98630137 0.71984436 0.75813953 1. 0.95535714 0.65454545 0.81862745 0.70750988] mean value: 0.810443448214679 key: test_recall value: [0.91304348 0.58333333 0.26086957 0.91304348 0.65217391 0.13043478 0.43478261 0.73913043 0.60869565 0.86956522] mean value: 0.6105072463768116 key: train_recall value: [0.98557692 0.57004831 0.34615385 0.88942308 0.78365385 0.19711538 0.51442308 0.86538462 0.80288462 0.86057692] mean value: 0.6815240616871051 key: test_accuracy value: [0.4893617 0.68085106 0.60869565 0.7826087 0.73913043 0.56521739 0.65217391 0.69565217 0.60869565 0.69565217] mean value: 0.651803885291397 key: train_accuracy value: [0.60481928 0.7686747 0.67067308 0.77163462 0.76682692 0.59855769 0.74519231 0.70432692 0.8125 0.75240385] mean value: 0.7195609360518999 key: test_roc_auc value: [0.49818841 0.68297101 0.60869565 0.7826087 0.73913043 0.56521739 0.65217391 0.69565217 0.60869565 0.69565217] mean value: 0.6528985507246376 key: train_roc_auc value: [0.60389957 0.76819723 0.67067308 0.77163462 0.76682692 0.59855769 0.74519231 0.70432692 0.8125 0.75240385] mean value: 0.7194212188777406 key: test_jcc value: [0.46666667 0.48275862 0.25 0.67741935 0.55555556 0.13043478 0.38461538 0.5483871 0.4375 0.58823529] mean value: 0.4521572755866508 key: train_jcc value: [0.55555556 0.55140187 0.34449761 0.66071429 0.62692308 0.19711538 0.50234742 0.59405941 0.68163265 0.63475177] mean value: 0.5348999029514523 MCC on Blind test: 0.3 MCC on Training: 0.34 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1167872 0.09568644 0.0979836 0.09909844 0.09992051 0.10176253 0.11872697 0.09778857 0.10104895 0.09975266] mean value: 0.10285558700561523 key: score_time value: [0.01135349 0.01084042 0.01098919 0.01108551 0.01137185 0.01125836 0.01106644 0.01117873 0.01121426 0.01107669] mean value: 0.01114349365234375 key: test_mcc value: [0.62091661 0.27586252 0.78334945 0.82922798 0.61394061 0.74194083 0.62360956 0.48566186 0.3927922 0.74194083] mean value: 0.610924246869251 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.79069767 0.65306122 0.88888889 0.90909091 0.81632653 0.86363636 0.7804878 0.71428571 0.68181818 0.875 ] mean value: 0.7973293292118753 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 0.64 0.90909091 0.95238095 0.76923077 0.9047619 0.88888889 0.78947368 0.71428571 0.84 ] mean value: 0.8258112822849665 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.66666667 0.86956522 0.86956522 0.86956522 0.82608696 0.69565217 0.65217391 0.65217391 0.91304348] mean value: 0.7753623188405797 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80851064 0.63829787 0.89130435 0.91304348 0.80434783 0.86956522 0.80434783 0.73913043 0.69565217 0.86956522] mean value: 0.8033765032377428 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.80706522 0.63768116 0.89130435 0.91304348 0.80434783 0.86956522 0.80434783 0.73913043 0.69565217 0.86956522] mean value: 0.8031702898550724 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.65384615 0.48484848 0.8 0.83333333 0.68965517 0.76 0.64 0.55555556 0.51724138 0.77777778] mean value: 0.6712257857085443 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.61 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... JJKtbM KCtR.RKt}ahThreadingBackend)}( nesting_levelKinner_max_num_threadsNubNN}tRsbargs)kwargs} loky_pickler cloudpickleu[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... loky_pQ |U KBuilding estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.14263916 0.13458824 0.13381743 0.13633156 0.13700366 0.13786983 0.13615799 0.14215779 0.13796139 0.13446069] mean value: 0.1372987747192383 key: score_time value: [0.01511192 0.015311 0.01508999 0.01533604 0.01520729 0.0156281 0.01494479 0.01593351 0.01517057 0.01501656] mean value: 0.015274977684020996 key: test_mcc value: [0.68884672 0.75592895 0.68884672 0.438357 0.37796447 0.31311215 0.53935989 0.06362848 0.44539933 0.44539933] mean value: 0.475684304315145 key: train_mcc value: [0.86185958 0.88196571 0.92363338 0.91746342 0.90286486 0.90277778 0.91746342 0.95141183 0.90991971 0.86119418] mean value: 0.9030553874226207 key: test_fscore value: [0.84848485 0.88235294 0.84848485 0.72727273 0.70588235 0.64516129 0.69230769 0.57142857 0.68965517 0.68965517] mean value: 0.7300685617246503 key: train_fscore value: [0.92907801 0.94076655 0.96193772 0.95744681 0.95104895 0.95138889 0.95744681 0.97560976 0.95438596 0.93006993] mean value: 0.9509179389008908 key: test_precision value: [0.82352941 0.83333333 0.82352941 0.70588235 0.66666667 0.66666667 0.9 0.52631579 0.76923077 0.76923077] mean value: 0.7484385171072476 key: train_precision value: [0.94927536 0.94405594 0.95862069 0.97826087 0.95774648 0.95138889 0.97826087 0.97902098 0.96453901 0.93661972] mean value: 0.9597788807345559 key: test_recall value: [0.875 0.9375 0.875 0.75 0.75 0.625 0.5625 0.625 0.625 0.625 ] mean value: 0.725 key: train_recall value: [0.90972222 0.9375 0.96527778 0.9375 0.94444444 0.95138889 0.9375 0.97222222 0.94444444 0.92361111] mean value: 0.9423611111111111 key: test_accuracy value: [0.84375 0.875 0.84375 0.71875 0.6875 0.65625 0.75 0.53125 0.71875 0.71875] mean value: 0.734375 key: train_accuracy value: [0.93055556 0.94097222 0.96180556 0.95833333 0.95138889 0.95138889 0.95833333 0.97569444 0.95486111 0.93055556] mean value: 0.9513888888888887 key: test_roc_auc value: [0.84375 0.875 0.84375 0.71875 0.6875 0.65625 0.75 0.53125 0.71875 0.71875] mean value: 0.734375 key: train_roc_auc value: [0.93055556 0.94097222 0.96180556 0.95833333 0.95138889 0.95138889 0.95833333 0.97569444 0.95486111 0.93055556] mean value: 0.9513888888888887 key: test_jcc value: [0.73684211 0.78947368 0.73684211 0.57142857 0.54545455 0.47619048 0.52941176 0.4 0.52631579 0.52631579] mean value: 0.5838274831463686 key: train_jcc value: [0.86754967 0.88815789 0.92666667 0.91836735 0.90666667 0.90728477 0.91836735 0.95238095 0.91275168 0.86928105] mean value: 0.9067474035018754 MCC on Blind test: 0.12 MCC on Training: 0.48 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.19329786 0.24348378 0.22180295 0.22627664 0.20801377 0.19800448 0.22243023 0.1757288 0.19966149 0.2201674 ] mean value: 0.21088674068450927 key: score_time value: [0.0806284 0.05718875 0.05317283 0.03796768 0.04026246 0.07039499 0.04129362 0.0764761 0.04824615 0.04962683] mean value: 0.055525779724121094 key: test_mcc value: [0.68884672 0.75 0.64549722 0.57265629 0.25819889 0.37796447 0.62554324 0.18786729 0.56360186 0.53935989] mean value: 0.5209535875440152 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84848485 0.875 0.83333333 0.75862069 0.66666667 0.70588235 0.72 0.58064516 0.77419355 0.69230769] mean value: 0.7455134293066309 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.82352941 0.875 0.75 0.84615385 0.6 0.66666667 1. 0.6 0.8 0.9 ] mean value: 0.7861349924585219 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.875 0.9375 0.6875 0.75 0.75 0.5625 0.5625 0.75 0.5625] mean value: 0.73125 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.84375 0.875 0.8125 0.78125 0.625 0.6875 0.78125 0.59375 0.78125 0.75 ] mean value: 0.753125 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.84375 0.875 0.8125 0.78125 0.625 0.6875 0.78125 0.59375 0.78125 0.75 ] mean value: 0.753125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73684211 0.77777778 0.71428571 0.61111111 0.5 0.54545455 0.5625 0.40909091 0.63157895 0.52941176] mean value: 0.6018052875057519 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.52 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02059937 0.02230811 0.0223546 0.02256632 0.02171016 0.01998687 0.02147412 0.02016044 0.02017832 0.02105832] mean value: 0.021239662170410158 key: score_time value: [0.0088582 0.00913548 0.00885344 0.00892544 0.00868464 0.07422686 0.00889039 0.00876641 0.0087738 0.00880289] mean value: 0.01539175510406494 key: test_mcc value: [ 0.32897585 0.31311215 0.38729833 0.125 -0.125 0.31311215 0.59215653 0.25197632 0.438357 0.50395263] mean value: 0.31289409484276265 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.7027027 0.66666667 0.72222222 0.5625 0.4375 0.66666667 0.74074074 0.6 0.70967742 0.73333333] mean value: 0.6542009751687171 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.61904762 0.64705882 0.65 0.5625 0.4375 0.64705882 0.90909091 0.64285714 0.73333333 0.78571429] mean value: 0.6634160937102113 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8125 0.6875 0.8125 0.5625 0.4375 0.6875 0.625 0.5625 0.6875 0.6875] mean value: 0.65625 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.65625 0.65625 0.6875 0.5625 0.4375 0.65625 0.78125 0.625 0.71875 0.75 ] mean value: 0.653125 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65625 0.65625 0.6875 0.5625 0.4375 0.65625 0.78125 0.625 0.71875 0.75 ] mean value: 0.653125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.54166667 0.5 0.56521739 0.39130435 0.28 0.5 0.58823529 0.42857143 0.55 0.57894737] mean value: 0.49239424969072304 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.1 MCC on Training: 0.31 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0092032 0.00952435 0.00925493 0.00950551 0.01054978 0.01034045 0.01035762 0.01030993 0.01042199 0.00972962] mean value: 0.00991973876953125 key: score_time value: [0.00850296 0.00852966 0.0087111 0.00859523 0.00929379 0.00894928 0.0090909 0.0092895 0.00931215 0.00871968] mean value: 0.008899426460266114 key: test_mcc value: [0.38729833 0.37796447 0.44539933 0.06262243 0.19088543 0.31814238 0.12598816 0.19088543 0.25197632 0.44539933] mean value: 0.27965616171871555 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.64285714 0.70588235 0.74285714 0.51612903 0.62857143 0.68571429 0.53333333 0.62857143 0.6 0.68965517] mean value: 0.6373571319517797 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.66666667 0.68421053 0.53333333 0.57894737 0.63157895 0.57142857 0.57894737 0.64285714 0.76923077] mean value: 0.64072006940428 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5625 0.75 0.8125 0.5 0.6875 0.75 0.5 0.6875 0.5625 0.625 ] mean value: 0.64375 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.6875 0.71875 0.53125 0.59375 0.65625 0.5625 0.59375 0.625 0.71875] mean value: 0.6375 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6875 0.6875 0.71875 0.53125 0.59375 0.65625 0.5625 0.59375 0.625 0.71875] mean value: 0.6375 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.47368421 0.54545455 0.59090909 0.34782609 0.45833333 0.52173913 0.36363636 0.45833333 0.42857143 0.52631579] mean value: 0.47148033126294 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.03 MCC on Training: 0.28 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11019611 0.12054467 0.12356019 0.122123 0.11798 0.11873531 0.11744189 0.1165309 0.11738873 0.11472154] mean value: 0.11792223453521729 key: score_time value: [0.01789904 0.02005601 0.02062488 0.01949453 0.01943636 0.0197649 0.01886797 0.01940155 0.01928401 0.0185585 ] mean value: 0.019338774681091308 key: test_mcc value: [0.56360186 0.50395263 0.50395263 0.25197632 0.19088543 0.25819889 0.19738551 0.18786729 0.50395263 0.51639778] mean value: 0.367817096330072 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77419355 0.76470588 0.76470588 0.6 0.62857143 0.66666667 0.51851852 0.60606061 0.73333333 0.71428571] mean value: 0.6771041580529247 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.72222222 0.72222222 0.64285714 0.57894737 0.6 0.63636364 0.58823529 0.78571429 0.83333333] mean value: 0.6909895505251542 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.8125 0.8125 0.5625 0.6875 0.75 0.4375 0.625 0.6875 0.625 ] mean value: 0.675 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78125 0.75 0.75 0.625 0.59375 0.625 0.59375 0.59375 0.75 0.75 ] mean value: 0.68125 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78125 0.75 0.75 0.625 0.59375 0.625 0.59375 0.59375 0.75 0.75 ] mean value: 0.68125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.63157895 0.61904762 0.61904762 0.42857143 0.45833333 0.5 0.35 0.43478261 0.57894737 0.55555556] mean value: 0.5175864480040682 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.17 MCC on Training: 0.37 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.49247217 0.51103663 0.49740434 0.50299048 0.51495695 0.50981593 0.51168251 0.48292899 0.48692799 0.48011374] mean value: 0.49903297424316406 key: score_time value: [0.01030564 0.01016402 0.00939965 0.01045179 0.01010704 0.00965118 0.0105598 0.00919104 0.00908232 0.00913024] mean value: 0.009804272651672363 key: test_mcc value: [0.625 0.62994079 0.64549722 0.438357 0.31814238 0.12909944 0.51639778 0.18786729 0.5 0.51639778] mean value: 0.4506699689151878 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8125 0.82352941 0.83333333 0.70967742 0.68571429 0.61111111 0.71428571 0.58064516 0.75 0.71428571] mean value: 0.7235082151140027 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8125 0.77777778 0.75 0.73333333 0.63157895 0.55 0.83333333 0.6 0.75 0.83333333] mean value: 0.7271856725146199 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8125 0.875 0.9375 0.6875 0.75 0.6875 0.625 0.5625 0.75 0.625 ] mean value: 0.73125 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.8125 0.8125 0.71875 0.65625 0.5625 0.75 0.59375 0.75 0.75 ] mean value: 0.721875 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.8125 0.8125 0.71875 0.65625 0.5625 0.75 0.59375 0.75 0.75 ] mean value: 0.721875 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68421053 0.7 0.71428571 0.55 0.52173913 0.44 0.55555556 0.40909091 0.6 0.55555556] mean value: 0.5730437391238306 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.45 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00914311 0.00902367 0.00908422 0.00923014 0.008811 0.00900316 0.00938988 0.00891972 0.00880075 0.00940871] mean value: 0.009081435203552247 key: score_time value: [0.00856137 0.0087893 0.00866508 0.00846267 0.00886083 0.00870705 0.00855684 0.0088439 0.0086596 0.00848985] mean value: 0.008659648895263671 key: test_mcc value: [ 0.38729833 0.64549722 0.31814238 0.25 0.0695048 -0.18786729 0.375 0.31814238 0.25819889 0.68884672] mean value: 0.31227634492653944 key: train_mcc value: [0.36827763 0.34094434 0.34068054 0.3821749 0.40293326 0.41702883 0.375 0.35424355 0.3630057 0.39584288] mean value: 0.374013161363567 key: test_fscore value: [0.64285714 0.78571429 0.68571429 0.625 0.61538462 0.42424242 0.6875 0.68571429 0.66666667 0.83870968] mean value: 0.665750338371306 key: train_fscore value: [0.6894198 0.68013468 0.6779661 0.69624573 0.70547945 0.71428571 0.6875 0.68041237 0.71515152 0.69686411] mean value: 0.6943459474964137 key: test_precision value: [0.75 0.91666667 0.63157895 0.625 0.52173913 0.41176471 0.6875 0.63157895 0.6 0.86666667] mean value: 0.664249506438731 key: train_precision value: [0.67785235 0.66013072 0.66225166 0.68456376 0.69594595 0.7 0.6875 0.67346939 0.6344086 0.6993007 ] mean value: 0.6775423117118222 key: test_recall value: [0.5625 0.6875 0.75 0.625 0.75 0.4375 0.6875 0.75 0.75 0.8125] mean value: 0.68125 key: train_recall value: [0.70138889 0.70138889 0.69444444 0.70833333 0.71527778 0.72916667 0.6875 0.6875 0.81944444 0.69444444] mean value: 0.7138888888888889 key: test_accuracy value: [0.6875 0.8125 0.65625 0.625 0.53125 0.40625 0.6875 0.65625 0.625 0.84375] mean value: 0.653125 key: train_accuracy value: [0.68402778 0.67013889 0.67013889 0.69097222 0.70138889 0.70833333 0.6875 0.67708333 0.67361111 0.69791667] mean value: 0.6861111111111111 key: test_roc_auc value: [0.6875 0.8125 0.65625 0.625 0.53125 0.40625 0.6875 0.65625 0.625 0.84375] mean value: 0.653125 key: train_roc_auc value: [0.68402778 0.67013889 0.67013889 0.69097222 0.70138889 0.70833333 0.6875 0.67708333 0.67361111 0.69791667] mean value: 0.6861111111111111 key: test_jcc value: [0.47368421 0.64705882 0.52173913 0.45454545 0.44444444 0.26923077 0.52380952 0.52173913 0.5 0.72222222] mean value: 0.5078473709177707 key: train_jcc value: [0.52604167 0.51530612 0.51282051 0.53403141 0.54497354 0.55555556 0.52380952 0.515625 0.55660377 0.53475936] mean value: 0.5319526471761025 MCC on Blind test: 0.57 MCC on Training: 0.31 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03419614 0.06681705 0.07722425 0.0871942 0.07695746 0.10801864 0.09231091 0.07731605 0.0789423 0.07984495] mean value: 0.07788219451904296 key: score_time value: [0.01417971 0.03129458 0.03123784 0.03170371 0.0317409 0.03181434 0.03131509 0.03100514 0.03157401 0.03227758] mean value: 0.02981429100036621 key: test_mcc value: [ 0.25197632 0.31311215 0.438357 0.25197632 0.19088543 0.06362848 0.25197632 -0.06262243 0.06262243 0.31814238] mean value: 0.2080054381998834 key: train_mcc value: [0.92470431 0.93091477 0.93199493 0.95159542 0.93806565 0.95196292 0.93806565 0.93136438 0.94453555 0.93091477] mean value: 0.9374118370893209 key: test_fscore value: [0.6 0.66666667 0.70967742 0.6 0.62857143 0.57142857 0.6 0.48484848 0.51612903 0.62068966] mean value: 0.5998011258300469 key: train_fscore value: [0.96085409 0.96478873 0.96428571 0.9754386 0.96819788 0.97526502 0.96819788 0.96453901 0.97202797 0.96478873] mean value: 0.9678383624597695 key: test_precision value: [0.64285714 0.64705882 0.73333333 0.64285714 0.57894737 0.52631579 0.64285714 0.47058824 0.53333333 0.69230769] mean value: 0.6110456004264054 key: train_precision value: [0.98540146 0.97857143 0.99264706 0.9858156 0.98561151 0.99280576 0.98561151 0.98550725 0.97887324 0.97857143] mean value: 0.9849416241449129 key: test_recall value: [0.5625 0.6875 0.6875 0.5625 0.6875 0.625 0.5625 0.5 0.5 0.5625] mean value: 0.59375 key: train_recall value: [0.9375 0.95138889 0.9375 0.96527778 0.95138889 0.95833333 0.95138889 0.94444444 0.96527778 0.95138889] mean value: 0.951388888888889 key: test_accuracy value: [0.625 0.65625 0.71875 0.625 0.59375 0.53125 0.625 0.46875 0.53125 0.65625] mean value: 0.603125 key: train_accuracy value: [0.96180556 0.96527778 0.96527778 0.97569444 0.96875 0.97569444 0.96875 0.96527778 0.97222222 0.96527778] mean value: 0.9684027777777778 key: test_roc_auc value: [0.625 0.65625 0.71875 0.625 0.59375 0.53125 0.625 0.46875 0.53125 0.65625] mean value: 0.603125 key: train_roc_auc value: [0.96180556 0.96527778 0.96527778 0.97569444 0.96875 0.97569444 0.96875 0.96527778 0.97222222 0.96527778] mean value: 0.9684027777777778 key: test_jcc value: [0.42857143 0.5 0.55 0.42857143 0.45833333 0.4 0.42857143 0.32 0.34782609 0.45 ] mean value: 0.43118737060041407 key: train_jcc value: [0.92465753 0.93197279 0.93103448 0.95205479 0.93835616 0.95172414 0.93835616 0.93150685 0.94557823 0.93197279] mean value: 0.9377213937062778 MCC on Blind test: -0.01 MCC on Training: 0.21 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02755928 0.01016927 0.00987101 0.01022387 0.0098958 0.00991273 0.00882554 0.01002645 0.00979233 0.00961494] mean value: 0.011589121818542481 key: score_time value: [0.01534343 0.01433134 0.01429319 0.01355743 0.01370907 0.01275659 0.01320529 0.01500988 0.01656032 0.01184583] mean value: 0.014061236381530761 key: test_mcc value: [ 0.12909944 0.25197632 0.375 0.06262243 0.19738551 0. 0.25197632 -0.06262243 0.31814238 0.31311215] mean value: 0.18366921110709442 key: train_mcc value: [0.49335305 0.50695667 0.43323817 0.46555851 0.47336501 0.47240451 0.45873171 0.54297751 0.47226777 0.47226777] mean value: 0.4791120689797947 key: test_fscore value: [0.5 0.64705882 0.6875 0.51612903 0.64864865 0.52941176 0.64705882 0.48484848 0.62068966 0.64516129] mean value: 0.5926506523014898 key: train_fscore value: [0.74204947 0.75432526 0.69852941 0.73720137 0.72661871 0.73239437 0.72340426 0.76258993 0.73793103 0.73426573] mean value: 0.7349309529791004 key: test_precision value: [0.58333333 0.61111111 0.6875 0.53333333 0.57142857 0.5 0.61111111 0.47058824 0.69230769 0.66666667] mean value: 0.5927380054585938 key: train_precision value: [0.75539568 0.75172414 0.7421875 0.72483221 0.75373134 0.74285714 0.73913043 0.79104478 0.73287671 0.73943662] mean value: 0.7473216565239186 key: test_recall value: [0.4375 0.6875 0.6875 0.5 0.75 0.5625 0.6875 0.5 0.5625 0.625 ] mean value: 0.6 key: train_recall value: [0.72916667 0.75694444 0.65972222 0.75 0.70138889 0.72222222 0.70833333 0.73611111 0.74305556 0.72916667] mean value: 0.7236111111111112 key: test_accuracy value: [0.5625 0.625 0.6875 0.53125 0.59375 0.5 0.625 0.46875 0.65625 0.65625] mean value: 0.590625 key: train_accuracy value: [0.74652778 0.75347222 0.71527778 0.73263889 0.73611111 0.73611111 0.72916667 0.77083333 0.73611111 0.73611111] mean value: 0.7392361111111111 key: test_roc_auc value: [0.5625 0.625 0.6875 0.53125 0.59375 0.5 0.625 0.46875 0.65625 0.65625] mean value: 0.590625 key: train_roc_auc value: [0.74652778 0.75347222 0.71527778 0.73263889 0.73611111 0.73611111 0.72916667 0.77083333 0.73611111 0.73611111] mean value: 0.7392361111111111 key: test_jcc value: [0.33333333 0.47826087 0.52380952 0.34782609 0.48 0.36 0.47826087 0.32 0.45 0.47619048] mean value: 0.424768115942029 key: train_jcc value: [0.58988764 0.60555556 0.53672316 0.58378378 0.57062147 0.57777778 0.56666667 0.61627907 0.58469945 0.5801105 ] mean value: 0.5812105077558507 MCC on Blind test: 0.03 MCC on Training: 0.18 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03582311 0.04379869 0.03112698 0.03133249 0.05768228 0.06538606 0.05158353 0.03187537 0.07674646 0.07786512] mean value: 0.05032200813293457 key: score_time value: [0.02246284 0.0120821 0.01228929 0.01203346 0.02316713 0.02076483 0.01270485 0.01298976 0.02404761 0.02112651] mean value: 0.017366838455200196 key: test_mcc value: [0.46056619 0.37796447 0.50395263 0.19088543 0.19738551 0.31311215 0.32897585 0.06262243 0.25 0.18786729] mean value: 0.2873331936997218 key: train_mcc value: [0.71529503 0.72918425 0.77785281 0.77100067 0.78489257 0.72932496 0.78474114 0.79166667 0.74321686 0.77100067] mean value: 0.7598175623103616 key: test_fscore value: [0.66666667 0.66666667 0.76470588 0.55172414 0.64864865 0.66666667 0.59259259 0.51612903 0.625 0.58064516] mean value: 0.6279445455073602 key: train_fscore value: [0.85813149 0.8650519 0.88811189 0.88659794 0.89122807 0.86597938 0.89198606 0.89583333 0.87285223 0.88659794] mean value: 0.8802370236750825 key: test_precision value: [0.81818182 0.71428571 0.72222222 0.61538462 0.57142857 0.64705882 0.72727273 0.53333333 0.625 0.6 ] mean value: 0.6574167825638414 key: train_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.85517241 0.86206897 0.8943662 0.87755102 0.90070922 0.85714286 0.8951049 0.89583333 0.86394558 0.87755102] mean value: 0.8779445500980303 key: test_recall value: [0.5625 0.625 0.8125 0.5 0.75 0.6875 0.5 0.5 0.625 0.5625] mean value: 0.6125 key: train_recall value: [0.86111111 0.86805556 0.88194444 0.89583333 0.88194444 0.875 0.88888889 0.89583333 0.88194444 0.89583333] mean value: 0.882638888888889 key: test_accuracy value: [0.71875 0.6875 0.75 0.59375 0.59375 0.65625 0.65625 0.53125 0.625 0.59375] mean value: 0.640625 key: train_accuracy value: [0.85763889 0.86458333 0.88888889 0.88541667 0.89236111 0.86458333 0.89236111 0.89583333 0.87152778 0.88541667] mean value: 0.8798611111111111 key: test_roc_auc value: [0.71875 0.6875 0.75 0.59375 0.59375 0.65625 0.65625 0.53125 0.625 0.59375] mean value: 0.640625 key: train_roc_auc value: [0.85763889 0.86458333 0.88888889 0.88541667 0.89236111 0.86458333 0.89236111 0.89583333 0.87152778 0.88541667] mean value: 0.8798611111111112 key: test_jcc value: [0.5 0.5 0.61904762 0.38095238 0.48 0.5 0.42105263 0.34782609 0.45454545 0.40909091] mean value: 0.4612515082171832 key: train_jcc value: [0.75151515 0.76219512 0.79874214 0.7962963 0.80379747 0.76363636 0.80503145 0.81132075 0.77439024 0.7962963 ] mean value: 0.7863221281574839 MCC on Blind test: 0.01 MCC on Training: 0.29 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03974247 0.03677702 0.03809428 0.03903222 0.04005337 0.04097581 0.04326487 0.03627682 0.03880358 0.04415035] mean value: 0.03971707820892334 key: score_time value: [0.01600432 0.01450419 0.01205969 0.01421618 0.01276374 0.01270676 0.01291561 0.01295328 0.01963186 0.01306105] mean value: 0.014081668853759766 key: test_mcc value: [0.25819889 0.438357 0.50395263 0.31311215 0.31814238 0.12909944 0.37796447 0.06362848 0.44539933 0.50395263] mean value: 0.3351807410158598 key: train_mcc value: [0.6045312 0.6187871 0.66165551 0.62718151 0.61164228 0.64043501 0.59774132 0.6187871 0.59866751 0.62596674] mean value: 0.6205395286705155 key: test_fscore value: [0.57142857 0.72727273 0.76470588 0.64516129 0.68571429 0.61111111 0.66666667 0.57142857 0.68965517 0.73333333] mean value: 0.6666477612044581 key: train_fscore value: [0.80546075 0.81355932 0.8361204 0.82 0.80952381 0.82550336 0.80272109 0.81355932 0.80536913 0.81756757] mean value: 0.814938474500706 key: test_precision value: [0.66666667 0.70588235 0.72222222 0.66666667 0.63157895 0.55 0.71428571 0.52631579 0.76923077 0.78571429] mean value: 0.6738563414569606 key: train_precision value: [0.79194631 0.79470199 0.80645161 0.78846154 0.79333333 0.7987013 0.78666667 0.79470199 0.77922078 0.79605263] mean value: 0.7930238143100554 key: test_recall value: [0.5 0.75 0.8125 0.625 0.75 0.6875 0.625 0.625 0.625 0.6875] mean value: 0.66875 key: train_recall value: [0.81944444 0.83333333 0.86805556 0.85416667 0.82638889 0.85416667 0.81944444 0.83333333 0.83333333 0.84027778] mean value: 0.8381944444444445 key: test_accuracy value: [0.625 0.71875 0.75 0.65625 0.65625 0.5625 0.6875 0.53125 0.71875 0.75 ] mean value: 0.665625 key: train_accuracy value: [0.80208333 0.80902778 0.82986111 0.8125 0.80555556 0.81944444 0.79861111 0.80902778 0.79861111 0.8125 ] mean value: 0.8097222222222221 key: test_roc_auc value: [0.625 0.71875 0.75 0.65625 0.65625 0.5625 0.6875 0.53125 0.71875 0.75 ] mean value: 0.665625 key: train_roc_auc value: [0.80208333 0.80902778 0.82986111 0.8125 0.80555556 0.81944444 0.79861111 0.80902778 0.79861111 0.8125 ] mean value: 0.8097222222222221 key: test_jcc value: [0.4 0.57142857 0.61904762 0.47619048 0.52173913 0.44 0.5 0.4 0.52631579 0.57894737] mean value: 0.5033668954996185 key: train_jcc value: [0.67428571 0.68571429 0.7183908 0.69491525 0.68 0.70285714 0.67045455 0.68571429 0.6741573 0.69142857] mean value: 0.6877917907660321 MCC on Blind test: 0.37 MCC on Training: 0.34 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48535752 0.57935905 0.58889604 0.50395608 0.48185396 0.50495768 0.64293098 0.48778009 0.47609234 0.4868598 ] mean value: 0.5238043546676636 key: score_time value: [0.01199365 0.0121727 0.01212859 0.01210666 0.01211929 0.01213861 0.01217294 0.01212168 0.01211643 0.0121851 ] mean value: 0.012125563621520997 key: test_mcc value: [0.38729833 0.438357 0.57265629 0.31311215 0. 0.19088543 0.44539933 0.25197632 0.25 0.438357 ] mean value: 0.3288041852675988 key: train_mcc value: [0.47927069 0.42370307 0.49335305 0.56360186 0.51388889 0.5 0.57032526 0.60664382 0.47226777 0.47222222] mean value: 0.5095276628847858 key: test_fscore value: [0.64285714 0.70967742 0.8 0.64516129 0.55555556 0.62857143 0.68965517 0.64705882 0.625 0.70967742] mean value: 0.665321425195959 key: train_fscore value: [0.74226804 0.71477663 0.75085324 0.78787879 0.75694444 0.75 0.79054054 0.81063123 0.73793103 0.73611111] mean value: 0.7577935063553861 key: test_precision value: [0.75 0.73333333 0.73684211 0.66666667 0.5 0.57894737 0.76923077 0.61111111 0.625 0.73333333] mean value: 0.6704464687359424 key: train_precision value: [0.73469388 0.70748299 0.73825503 0.76470588 0.75694444 0.75 0.76973684 0.77707006 0.73287671 0.73611111] mean value: 0.746787696034214 key: test_recall value: [0.5625 0.6875 0.875 0.625 0.625 0.6875 0.625 0.6875 0.625 0.6875] mean value: 0.66875 key: train_recall value: [0.75 0.72222222 0.76388889 0.8125 0.75694444 0.75 0.8125 0.84722222 0.74305556 0.73611111] mean value: 0.7694444444444445 key: test_accuracy value: [0.6875 0.71875 0.78125 0.65625 0.5 0.59375 0.71875 0.625 0.625 0.71875] mean value: 0.6625 key: train_accuracy value: [0.73958333 0.71180556 0.74652778 0.78125 0.75694444 0.75 0.78472222 0.80208333 0.73611111 0.73611111] mean value: 0.7545138888888887 key: test_roc_auc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( value: [0.6875 0.71875 0.78125 0.65625 0.5 0.59375 0.71875 0.625 0.625 0.71875] mean value: 0.6625 key: train_roc_auc value: [0.73958333 0.71180556 0.74652778 0.78125 0.75694444 0.75 0.78472222 0.80208333 0.73611111 0.73611111] mean value: 0.7545138888888887 key: test_jcc value: [0.47368421 0.55 0.66666667 0.47619048 0.38461538 0.45833333 0.52631579 0.47826087 0.45454545 0.55 ] mean value: 0.5018612184916532 key: train_jcc value: [0.59016393 0.55614973 0.6010929 0.65 0.60893855 0.6 0.65363128 0.68156425 0.58469945 0.58241758] mean value: 0.61086576774032 MCC on Blind test: 0.57 MCC on Training: 0.33 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.18229938 1.3306849 1.28350806 1.33243799 1.40034986 1.27525806 1.38758492 1.41981268 1.24914765 1.37732363] mean value: 1.3238407135009767 key: score_time value: [0.01478696 0.01475191 0.01244736 0.01328635 0.01331496 0.01330829 0.01594639 0.01863217 0.01615667 0.01595211] mean value: 0.014858317375183106 key: test_mcc value: [0.438357 0.37796447 0.57265629 0.18786729 0.19738551 0.06262243 0.25197632 0.19088543 0.375 0.34752402] mean value: 0.3002238756029698 key: train_mcc value: [0.91666667 0.90286486 0.8896615 0.93055556 0.90974416 0.91702052 0.92363338 0.93091477 0.93091477 0.93091477] mean value: 0.9182890973480136 key: test_fscore value: [0.70967742 0.70588235 0.8 0.58064516 0.64864865 0.51612903 0.6 0.62857143 0.6875 0.56 ] mean value: 0.643705404306448 key: train_fscore value: [0.95833333 0.95104895 0.94326241 0.96527778 0.95470383 0.95774648 0.96167247 0.96478873 0.96478873 0.96478873] mean value: 0.9586411456184127 key: test_precision value: [0.73333333 0.66666667 0.73684211 0.6 0.57142857 0.53333333 0.64285714 0.57894737 0.6875 0.77777778] mean value: 0.6528686299081035 key: train_precision value: [0.95833333 0.95774648 0.96376812 0.96527778 0.95804196 0.97142857 0.96503497 0.97857143 0.97857143 0.97857143] mean value: 0.9675345486146159 key: test_recall value: [0.6875 0.75 0.875 0.5625 0.75 0.5 0.5625 0.6875 0.6875 0.4375] mean value: 0.65 key: train_recall value: [0.95833333 0.94444444 0.92361111 0.96527778 0.95138889 0.94444444 0.95833333 0.95138889 0.95138889 0.95138889] mean value: 0.95 key: test_accuracy value: [0.71875 0.6875 0.78125 0.59375 0.59375 0.53125 0.625 0.59375 0.6875 0.65625] mean value: 0.646875 key: train_accuracy value: [0.95833333 0.95138889 0.94444444 0.96527778 0.95486111 0.95833333 0.96180556 0.96527778 0.96527778 0.96527778] mean value: 0.959027777777778 key: test_roc_auc value: [0.71875 0.6875 0.78125 0.59375 0.59375 0.53125 0.625 0.59375 0.6875 0.65625] mean value: 0.646875 key: train_roc_auc value: [0.95833333 0.95138889 0.94444444 0.96527778 0.95486111 0.95833333 0.96180556 0.96527778 0.96527778 0.96527778] mean value: 0.9590277777777778 key: test_jcc value: [0.55 0.54545455 0.66666667 0.40909091 0.48 0.34782609 0.42857143 0.45833333 0.52380952 0.38888889] mean value: 0.4798641382771818 key: train_jcc value: [0.92 0.90666667 0.89261745 0.93288591 0.91333333 0.91891892 0.9261745 0.93197279 0.93197279 0.93197279] mean value: 0.920651513861485 MCC on Blind test: 0.06 MCC on Training: 0.3 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01257801 0.01259112 0.01026654 0.00913429 0.00901628 0.00884724 0.00875187 0.00922227 0.00975657 0.00970602] mean value: 0.00998702049255371 key: score_time value: [0.01175332 0.01096845 0.00898767 0.00874066 0.00831103 0.00847268 0.00867796 0.00916266 0.00880432 0.00860119] mean value: 0.009247994422912598 key: test_mcc value: [0.37796447 0.5 0.32897585 0.19088543 0.13483997 0.06362848 0.12598816 0.12909944 0.12598816 0.56360186] mean value: 0.25409718203617715 key: train_mcc value: [0.25972108 0.24438505 0.27909996 0.30071401 0.27274061 0.30745935 0.29305496 0.265975 0.30745935 0.29211781] mean value: 0.28227271626567535 key: test_fscore value: [0.66666667 0.75 0.7027027 0.62857143 0.63157895 0.57142857 0.58823529 0.61111111 0.58823529 0.78787879] mean value: 0.6526408803962983 key: train_fscore value: [0.65372168 0.64026403 0.65562914 0.66885246 0.6557377 0.67105263 0.66225166 0.65359477 0.67105263 0.65540541] mean value: 0.658756210769208 key: test_precision value: [0.71428571 0.75 0.61904762 0.57894737 0.54545455 0.52631579 0.55555556 0.55 0.55555556 0.76470588] mean value: 0.6159868030146668 key: train_precision value: [0.61212121 0.61006289 0.62658228 0.63354037 0.62111801 0.6375 0.63291139 0.61728395 0.6375 0.63815789] mean value: 0.6266778006536342 key: test_recall value: [0.625 0.75 0.8125 0.6875 0.75 0.625 0.625 0.6875 0.625 0.8125] mean value: 0.7 key: train_recall value: [0.70138889 0.67361111 0.6875 0.70833333 0.69444444 0.70833333 0.69444444 0.69444444 0.70833333 0.67361111] mean value: 0.6944444444444444 key: test_accuracy value: [0.6875 0.75 0.65625 0.59375 0.5625 0.53125 0.5625 0.5625 0.5625 0.78125] mean value: 0.625 key: train_accuracy value: [0.62847222 0.62152778 0.63888889 0.64930556 0.63541667 0.65277778 0.64583333 0.63194444 0.65277778 0.64583333] mean value: 0.6402777777777777 key: test_roc_auc value: [0.6875 0.75 0.65625 0.59375 0.5625 0.53125 0.5625 0.5625 0.5625 0.78125] mean value: 0.625 key: train_roc_auc value: [0.62847222 0.62152778 0.63888889 0.64930556 0.63541667 0.65277778 0.64583333 0.63194444 0.65277778 0.64583333] mean value: 0.6402777777777777 key: test_jcc value: [0.5 0.6 0.54166667 0.45833333 0.46153846 0.4 0.41666667 0.44 0.41666667 0.65 ] mean value: 0.48848717948717957 key: train_jcc value: [0.48557692 0.47087379 0.48768473 0.50246305 0.48780488 0.5049505 0.4950495 0.48543689 0.5049505 0.48743719] mean value: 0.4912227944967739 MCC on Blind test: 0.32 MCC on Training: 0.25 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0105319 0.01013041 0.01058769 0.01079941 0.01055622 0.01044536 0.01027822 0.01023912 0.01036286 0.01053524] mean value: 0.010446643829345703 key: score_time value: [0.00952601 0.00920248 0.00943494 0.00948286 0.00956249 0.00960541 0.00965118 0.00966358 0.00961208 0.00972128] mean value: 0.009546232223510743 key: test_mcc value: [ 0.25819889 0.38729833 0.375 0.438357 0.25197632 0.25197632 0.26967994 -0.06362848 0.12598816 0.57265629] mean value: 0.2867502771842139 key: train_mcc value: [0.4216162 0.38126619 0.37037187 0.40469494 0.38607187 0.41052481 0.41260759 0.43323817 0.42601432 0.39073994] mean value: 0.40371459161978107 key: test_fscore value: [0.57142857 0.64285714 0.6875 0.70967742 0.64705882 0.6 0.53846154 0.4137931 0.53333333 0.75862069] mean value: 0.6102730622068284 key: train_fscore value: [0.68421053 0.65648855 0.640625 0.68613139 0.66666667 0.6953405 0.68634686 0.69852941 0.6744186 0.67883212] mean value: 0.676758962792703 key: test_precision value: [0.66666667 0.75 0.6875 0.73333333 0.61111111 0.64285714 0.7 0.46153846 0.57142857 0.84615385] mean value: 0.6670589133089132 key: train_precision value: [0.74590164 0.72881356 0.73214286 0.72307692 0.72357724 0.71851852 0.73228346 0.7421875 0.76315789 0.71538462] mean value: 0.732504420786534 key: test_recall value: [0.5 0.5625 0.6875 0.6875 0.6875 0.5625 0.4375 0.375 0.5 0.6875] mean value: 0.56875 key: train_recall value: [0.63194444 0.59722222 0.56944444 0.65277778 0.61805556 0.67361111 0.64583333 0.65972222 0.60416667 0.64583333] mean value: 0.6298611111111111 key: test_accuracy value: [0.625 0.6875 0.6875 0.71875 0.625 0.625 0.625 0.46875 0.5625 0.78125] mean value: 0.640625 key: train_accuracy value: [0.70833333 0.6875 0.68055556 0.70138889 0.69097222 0.70486111 0.70486111 0.71527778 0.70833333 0.69444444] mean value: 0.6996527777777778 key: test_roc_auc value: [0.625 0.6875 0.6875 0.71875 0.625 0.625 0.625 0.46875 0.5625 0.78125] mean value: 0.640625 key: train_roc_auc value: [0.70833333 0.6875 0.68055556 0.70138889 0.69097222 0.70486111 0.70486111 0.71527778 0.70833333 0.69444444] mean value: 0.6996527777777778 key: test_jcc value: [0.4 0.47368421 0.52380952 0.55 0.47826087 0.42857143 0.36842105 0.26086957 0.36363636 0.61111111] mean value: 0.4458364125068931 key: train_jcc value: [0.52 0.48863636 0.47126437 0.52222222 0.5 0.53296703 0.52247191 0.53672316 0.50877193 0.51381215] mean value: 0.5116869145116573 MCC on Blind test: 0.32 MCC on Training: 0.29 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01235509 0.01634693 0.01688242 0.0161407 0.01453829 0.01498008 0.01466012 0.0163157 0.0157783 0.01681662] mean value: 0.01548142433166504 key: score_time value: [0.00908256 0.01164055 0.01169658 0.01160407 0.01168299 0.01160288 0.01170111 0.01157475 0.01176929 0.01169491] mean value: 0.011404967308044434 key: test_mcc value: [0.26967994 0.50395263 0.52915026 0.31311215 0.43033148 0.31814238 0.25197632 0.37796447 0.18898224 0.32025631] mean value: 0.3503548180283083 key: train_mcc value: [0.48433245 0.56099271 0.41558831 0.61986674 0.41558831 0.48752098 0.50820798 0.27317918 0.43429952 0.45916135] mean value: 0.4658737541396595 key: test_fscore value: [0.53846154 0.73333333 0.7804878 0.64516129 0.74418605 0.62068966 0.64705882 0.4 0.3 0.45454545] mean value: 0.5863923946754409 key: train_fscore value: [0.703125 0.76119403 0.74331551 0.81605351 0.74331551 0.68571429 0.77575758 0.24390244 0.55769231 0.61883408] mean value: 0.6648904246505261 key: test_precision value: [0.7 0.78571429 0.64 0.66666667 0.59259259 0.69230769 0.61111111 1. 0.75 0.83333333] mean value: 0.7271725681725681 key: train_precision value: [0.80357143 0.82258065 0.60434783 0.78709677 0.60434783 0.83168317 0.68817204 1. 0.90625 0.87341772] mean value: 0.7921467432946752 key: test_recall value: [0.4375 0.6875 1. 0.625 1. 0.5625 0.6875 0.25 0.1875 0.3125] mean value: 0.575 key: train_recall value: [0.625 0.70833333 0.96527778 0.84722222 0.96527778 0.58333333 0.88888889 0.13888889 0.40277778 0.47916667] mean value: 0.6604166666666667 key: test_accuracy value: [0.625 0.75 0.71875 0.65625 0.65625 0.65625 0.625 0.625 0.5625 0.625 ] mean value: 0.65 key: train_accuracy value: [0.73611111 0.77777778 0.66666667 0.80902778 0.66666667 0.73263889 0.74305556 0.56944444 0.68055556 0.70486111] mean value: 0.7086805555555555 key: test_roc_auc value: [0.625 0.75 0.71875 0.65625 0.65625 0.65625 0.625 0.625 0.5625 0.625 ] mean value: 0.65 key: train_roc_auc value: [0.73611111 0.77777778 0.66666667 0.80902778 0.66666667 0.73263889 0.74305556 0.56944444 0.68055556 0.70486111] mean value: 0.7086805555555555 key: test_jcc value: [0.36842105 0.57894737 0.64 0.47619048 0.59259259 0.45 0.47826087 0.25 0.17647059 0.29411765] mean value: 0.43050005946950354 key: train_jcc value: [0.54216867 0.61445783 0.59148936 0.68926554 0.59148936 0.52173913 0.63366337 0.13888889 0.38666667 0.44805195] mean value: 0.5157880766530436 MCC on Blind test: 0.06 MCC on Training: 0.35 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0235343 0.02579904 0.03195047 0.02613425 0.02645969 0.02684021 0.0243206 0.0238688 0.02547765 0.02512312] mean value: 0.025950813293457033 key: score_time value: [0.01293135 0.01240301 0.01674247 0.01323223 0.01240587 0.01297069 0.01477027 0.01514053 0.0147469 0.01299572] mean value: 0.013833904266357422 key: test_mcc value: [ 0. 0.34752402 0.31814238 0.32897585 0.12598816 -0.0695048 0.37796447 0. 0.12909944 0.0695048 ] mean value: 0.16276943279487815 key: train_mcc value: [0.64168895 0.73491969 0.85418726 0.91702052 0.89104002 0.85120556 0.8633971 0.88676859 0.7574764 0.74616135] mean value: 0.8143865435907172 key: test_fscore value: [0.55555556 0.71794872 0.62068966 0.59259259 0.53333333 0.56410256 0.70588235 0.42857143 0.61111111 0.61538462] mean value: 0.5945171926713508 key: train_fscore value: [0.82758621 0.87009063 0.92682927 0.95890411 0.94244604 0.92604502 0.93203883 0.93772894 0.88073394 0.87537994] mean value: 0.9077782935306249 key: test_precision value: [0.5 0.60869565 0.69230769 0.72727273 0.57142857 0.47826087 0.66666667 0.5 0.55 0.52173913] mean value: 0.5816371309849571 key: train_precision value: [0.70588235 0.77005348 0.93006993 0.94594595 0.97761194 0.86227545 0.87272727 0.99224806 0.78688525 0.77837838] mean value: 0.8622078053315979 key: test_recall value: [0.625 0.875 0.5625 0.5 0.5 0.6875 0.75 0.375 0.6875 0.75 ] mean value: 0.63125 key: train_recall value: [1. 1. 0.92361111 0.97222222 0.90972222 1. 1. 0.88888889 1. 1. ] mean value: 0.9694444444444444 key: test_accuracy value: [0.5 0.65625 0.65625 0.65625 0.5625 0.46875 0.6875 0.5 0.5625 0.53125] mean value: 0.578125 key: train_accuracy value: [0.79166667 0.85069444 0.92708333 0.95833333 0.94444444 0.92013889 0.92708333 0.94097222 0.86458333 0.85763889] mean value: 0.898263888888889 key: test_roc_auc value: [0.5 0.65625 0.65625 0.65625 0.5625 0.46875 0.6875 0.5 0.5625 0.53125] mean value: 0.578125 key: train_roc_auc value: [0.79166667 0.85069444 0.92708333 0.95833333 0.94444444 0.92013889 0.92708333 0.94097222 0.86458333 0.85763889] mean value: 0.898263888888889 key: test_jcc value: [0.38461538 0.56 0.45 0.42105263 0.36363636 0.39285714 0.54545455 0.27272727 0.44 0.44444444] mean value: 0.4274787785314101 key: train_jcc value: [0.70588235 0.77005348 0.86363636 0.92105263 0.89115646 0.86227545 0.87272727 0.88275862 0.78688525 0.77837838] mean value: 0.8334806253476092 MCC on Blind test: 0.15 MCC on Training: 0.16 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.60848284 0.61434412 0.60912824 0.60591388 0.61317539 0.64259505 0.59735179 0.63619351 0.62971306 0.64721942] mean value: 0.620411729812622 key: score_time value: [0.15509129 0.15062189 0.14945245 0.14845681 0.17296481 0.14146852 0.13990831 0.27776098 0.14494896 0.1757009 ] mean value: 0.16563749313354492 key: test_mcc value: [0.56360186 0.68884672 0.57265629 0.56360186 0.12909944 0.19088543 0.32897585 0.31311215 0.50395263 0.69991324] mean value: 0.4554645467566232 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77419355 0.84848485 0.8 0.77419355 0.61111111 0.62857143 0.59259259 0.66666667 0.73333333 0.82758621] mean value: 0.7256733284430726 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.82352941 0.73684211 0.8 0.55 0.57894737 0.72727273 0.64705882 0.78571429 0.92307692] mean value: 0.7372441645042265 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.875 0.875 0.75 0.6875 0.6875 0.5 0.6875 0.6875 0.75 ] mean value: 0.725 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78125 0.84375 0.78125 0.78125 0.5625 0.59375 0.65625 0.65625 0.75 0.84375] mean value: 0.725 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78125 0.84375 0.78125 0.78125 0.5625 0.59375 0.65625 0.65625 0.75 0.84375] mean value: 0.725 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.63157895 0.73684211 0.66666667 0.63157895 0.44 0.45833333 0.42105263 0.5 0.57894737 0.70588235] mean value: 0.5770882352941176 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.46 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.97031713 0.95943141 0.99374795 0.94040656 0.94492316 0.99451399 0.97302794 0.97843933 0.9392004 0.94009089] mean value: 0.9634098768234253 key: score_time value: [0.26469851 0.13895726 0.17934918 0.22831488 0.17357707 0.20637679 0.19471407 0.2515595 0.25561333 0.20813727] mean value: 0.21012978553771972 key: test_mcc value: [0.56360186 0.625 0.57265629 0.625 0.19088543 0.19088543 0.38729833 0.31814238 0.56360186 0.51639778] mean value: 0.45534693640198887 key: train_mcc value: [0.88196571 0.868244 0.86144352 0.86857931 0.86111111 0.86144352 0.875 0.875 0.868244 0.84722222] mean value: 0.86682533904398 key: test_fscore value: [0.77419355 0.8125 0.8 0.8125 0.62857143 0.62857143 0.64285714 0.68571429 0.78787879 0.71428571] mean value: 0.7287072336265885 key: train_fscore value: [0.94117647 0.9347079 0.93150685 0.93515358 0.93055556 0.93150685 0.9375 0.9375 0.9347079 0.92361111] mean value: 0.9337926227062923 key: test_precision value: [0.8 0.8125 0.73684211 0.8125 0.57894737 0.57894737 0.75 0.63157895 0.76470588 0.83333333] mean value: 0.7299355005159959 key: train_precision value: [0.93793103 0.92517007 0.91891892 0.91946309 0.93055556 0.91891892 0.9375 0.9375 0.92517007 0.92361111] mean value: 0.9274738762290007 key: test_recall value: [0.75 0.8125 0.875 0.8125 0.6875 0.6875 0.5625 0.75 0.8125 0.625 ] mean value: 0.7375 key: train_recall value: [0.94444444 0.94444444 0.94444444 0.95138889 0.93055556 0.94444444 0.9375 0.9375 0.94444444 0.92361111] mean value: 0.9402777777777777 key: test_accuracy value: [0.78125 0.8125 0.78125 0.8125 0.59375 0.59375 0.6875 0.65625 0.78125 0.75 ] mean value: 0.725 key: train_accuracy value: [0.94097222 0.93402778 0.93055556 0.93402778 0.93055556 0.93055556 0.9375 0.9375 0.93402778 0.92361111] mean value: 0.9333333333333333 key: test_roc_auc value: [0.78125 0.8125 0.78125 0.8125 0.59375 0.59375 0.6875 0.65625 0.78125 0.75 ] mean value: 0.725 key: train_roc_auc value: [0.94097222 0.93402778 0.93055556 0.93402778 0.93055556 0.93055556 0.9375 0.9375 0.93402778 0.92361111] mean value: 0.9333333333333333 key: test_jcc value: [0.63157895 0.68421053 0.66666667 0.68421053 0.45833333 0.45833333 0.47368421 0.52173913 0.65 0.55555556] mean value: 0.5784312229849988 key: train_jcc value: [0.88888889 0.87741935 0.87179487 0.87820513 0.87012987 0.87179487 0.88235294 0.88235294 0.87741935 0.85806452] mean value: 0.8758422738973023 MCC on Blind test: 0.43 MCC on Training: 0.46 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04711151 0.04781914 0.03152394 0.03411388 0.04424095 0.03016591 0.03526545 0.03555441 0.03579497 0.03633547] mean value: 0.03779256343841553 key: score_time value: [0.04560184 0.02359414 0.02293515 0.02359915 0.02247691 0.02411938 0.02359796 0.02256083 0.02115011 0.02336454] mean value: 0.025300002098083495 key: test_mcc value: [0.25819889 0.5 0.37796447 0.25 0.31814238 0.06362848 0.31814238 0.19088543 0.375 0.50395263] mean value: 0.31559146616014466 key: train_mcc value: [0.65986544 0.68114709 0.68114709 0.65334516 0.67401754 0.69686834 0.66724612 0.67401754 0.6698399 0.68220251] mean value: 0.6739696740162362 key: test_fscore value: [0.57142857 0.75 0.70588235 0.625 0.68571429 0.57142857 0.62068966 0.62857143 0.6875 0.73333333] mean value: 0.6579548198589781 key: train_fscore value: [0.83161512 0.84353741 0.84353741 0.82993197 0.83959044 0.85333333 0.83673469 0.83959044 0.8410596 0.84563758] mean value: 0.8404568024120523 key: test_precision value: [0.66666667 0.75 0.66666667 0.625 0.63157895 0.52631579 0.69230769 0.57894737 0.6875 0.78571429] mean value: 0.6610697416618468 key: train_precision value: [0.82312925 0.82666667 0.82666667 0.81333333 0.82550336 0.82051282 0.82 0.82550336 0.80379747 0.81818182] mean value: 0.8203294736825812 key: test_recall value: [0.5 0.75 0.75 0.625 0.75 0.625 0.5625 0.6875 0.6875 0.6875] mean value: 0.6625 key: train_recall value: [0.84027778 0.86111111 0.86111111 0.84722222 0.85416667 0.88888889 0.85416667 0.85416667 0.88194444 0.875 ] mean value: 0.8618055555555555 key: test_accuracy value: [0.625 0.75 0.6875 0.625 0.65625 0.53125 0.65625 0.59375 0.6875 0.75 ] mean value: 0.65625 key: train_accuracy value: [0.82986111 0.84027778 0.84027778 0.82638889 0.83680556 0.84722222 0.83333333 0.83680556 0.83333333 0.84027778] mean value: 0.8364583333333332 key: test_roc_auc value: [0.625 0.75 0.6875 0.625 0.65625 0.53125 0.65625 0.59375 0.6875 0.75 ] mean value: 0.65625 key: train_roc_auc value: [0.82986111 0.84027778 0.84027778 0.82638889 0.83680556 0.84722222 0.83333333 0.83680556 0.83333333 0.84027778] mean value: 0.8364583333333332 key: test_jcc value: [0.4 0.6 0.54545455 0.45454545 0.52173913 0.4 0.45 0.45833333 0.52380952 0.57894737] mean value: 0.4932829355998692 key: train_jcc value: [0.71176471 0.72941176 0.72941176 0.70930233 0.72352941 0.74418605 0.71929825 0.72352941 0.72571429 0.73255814] mean value: 0.7248706101779758 MCC on Blind test: 0.13 MCC on Training: 0.32 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.1050756 0.10725498 0.12086368 0.1487 0.07782578 0.04893994 0.09808898 0.09489012 0.12086606 0.13351798] mean value: 0.1056023120880127 key: score_time value: [0.02077079 0.02020741 0.02538633 0.02379465 0.01208591 0.01208806 0.02381873 0.02220297 0.01992536 0.02407551] mean value: 0.020435571670532227 key: test_mcc value: [0.31814238 0.5 0.57265629 0.31311215 0.25819889 0.12909944 0.50395263 0.12909944 0.37796447 0.38729833] mean value: 0.34895240315119247 key: train_mcc value: [0.57651401 0.57875215 0.62651251 0.60664382 0.57807797 0.61324414 0.57032526 0.60094327 0.5376471 0.58536941] mean value: 0.5874029648208685 key: test_fscore value: [0.62068966 0.75 0.8 0.64516129 0.66666667 0.61111111 0.73333333 0.61111111 0.66666667 0.64285714] mean value: 0.6747596977241027 key: train_fscore value: [0.79037801 0.79734219 0.81879195 0.81063123 0.79598662 0.81333333 0.79054054 0.80921053 0.77887789 0.8 ] mean value: 0.8005092285160508 key: test_precision value: [0.69230769 0.75 0.73684211 0.66666667 0.6 0.55 0.78571429 0.55 0.71428571 0.75 ] mean value: 0.6795816464237517 key: train_precision value: [0.78231293 0.76433121 0.79220779 0.77707006 0.76774194 0.78205128 0.76973684 0.76875 0.74213836 0.76923077] mean value: 0.771557118491427 key: test_recall value: [0.5625 0.75 0.875 0.625 0.75 0.6875 0.6875 0.6875 0.625 0.5625] mean value: 0.68125 key: train_recall value: [0.79861111 0.83333333 0.84722222 0.84722222 0.82638889 0.84722222 0.8125 0.85416667 0.81944444 0.83333333] mean value: 0.8319444444444445 key: test_accuracy value: [0.65625 0.75 0.78125 0.65625 0.625 0.5625 0.75 0.5625 0.6875 0.6875 ] mean value: 0.671875 key: train_accuracy value: [0.78819444 0.78819444 0.8125 0.80208333 0.78819444 0.80555556 0.78472222 0.79861111 0.76736111 0.79166667] mean value: 0.7927083333333333 key: test_roc_auc value: [0.65625 0.75 0.78125 0.65625 0.625 0.5625 0.75 0.5625 0.6875 0.6875 ] mean value: 0.671875 key: train_roc_auc value: [0.78819444 0.78819444 0.8125 0.80208333 0.78819444 0.80555556 0.78472222 0.79861111 0.76736111 0.79166667] mean value: 0.7927083333333333 key: test_jcc value: [0.45 0.6 0.66666667 0.47619048 0.5 0.44 0.57894737 0.44 0.5 0.47368421] mean value: 0.5125488721804511 key: train_jcc value: [0.65340909 0.66298343 0.69318182 0.68156425 0.66111111 0.68539326 0.65363128 0.67955801 0.63783784 0.66666667] mean value: 0.6675336750323837 MCC on Blind test: 0.44 MCC on Training: 0.35 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02131701 0.0148344 0.01544118 0.01410842 0.0162394 0.0162065 0.01652646 0.01642346 0.01511455 0.01864982] mean value: 0.016486120223999024 key: score_time value: [0.01191902 0.01072097 0.01145005 0.01061082 0.01147723 0.0115478 0.01156878 0.01146102 0.01107311 0.01068163] mean value: 0.011251044273376466 key: test_mcc value: [0.50395263 0.50395263 0.50395263 0.375 0. 0.12598816 0.25197632 0.31814238 0.19088543 0.19738551] mean value: 0.2971235683914689 key: train_mcc value: [0.59774132 0.57673666 0.62718151 0.60094327 0.63208163 0.61818973 0.646223 0.59200756 0.55690001 0.62651251] mean value: 0.607451720762887 key: test_fscore value: [0.73333333 0.76470588 0.76470588 0.6875 0.55555556 0.58823529 0.6 0.68571429 0.62857143 0.51851852] mean value: 0.6526840180516651 key: train_fscore value: [0.80272109 0.79180887 0.82 0.80921053 0.81786942 0.81099656 0.82593857 0.80267559 0.7852349 0.81879195] mean value: 0.8085247465327511 key: test_precision value: [0.78571429 0.72222222 0.72222222 0.6875 0.5 0.55555556 0.64285714 0.63157895 0.57894737 0.63636364] mean value: 0.646296138072454 key: train_precision value: [0.78666667 0.77852349 0.78846154 0.76875 0.80952381 0.80272109 0.81208054 0.77419355 0.75974026 0.79220779] mean value: 0.7872868730268175 key: test_recall value: [0.6875 0.8125 0.8125 0.6875 0.625 0.625 0.5625 0.75 0.6875 0.4375] mean value: 0.66875 key: train_recall value: [0.81944444 0.80555556 0.85416667 0.85416667 0.82638889 0.81944444 0.84027778 0.83333333 0.8125 0.84722222] mean value: 0.83125 key: test_accuracy value: [0.75 0.75 0.75 0.6875 0.5 0.5625 0.625 0.65625 0.59375 0.59375] mean value: 0.646875 key: train_accuracy value: [0.79861111 0.78819444 0.8125 0.79861111 0.81597222 0.80902778 0.82291667 0.79513889 0.77777778 0.8125 ] mean value: 0.803125 key: test_roc_auc value: [0.75 0.75 0.75 0.6875 0.5 0.5625 0.625 0.65625 0.59375 0.59375] mean value: 0.646875 key: train_roc_auc value: [0.79861111 0.78819444 0.8125 0.79861111 0.81597222 0.80902778 0.82291667 0.79513889 0.77777778 0.8125 ] mean value: 0.803125 key: test_jcc value: [0.57894737 0.61904762 0.61904762 0.52380952 0.38461538 0.41666667 0.42857143 0.52173913 0.45833333 0.35 ] mean value: 0.490077807394741 key: train_jcc value: [0.67045455 0.65536723 0.69491525 0.67955801 0.69186047 0.68208092 0.70348837 0.67039106 0.64640884 0.69318182] mean value: 0.6787706523858107 MCC on Blind test: 0.06 MCC on Training: 0.3 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01480222 0.02052855 0.01835275 0.01523113 0.01805067 0.01864076 0.01784253 0.01827812 0.02071142 0.01660013] mean value: 0.017903828620910646 key: score_time value: [0.00883961 0.01134467 0.01187754 0.01196003 0.01226234 0.01211476 0.01230407 0.01209521 0.01204872 0.0120194 ] mean value: 0.01168663501739502 key: test_mcc value: [0. 0.56360186 0.56360186 0.28867513 0.48038446 0.18898224 0.37796447 0. 0.22677868 0.26967994] mean value: 0.2959668658268017 key: train_mcc value: [0.31501848 0.63540112 0.68160823 0.45693378 0.49268455 0.22604179 0.61401991 0.61069551 0.46770717 0.56414839] mean value: 0.50642589230505 key: test_fscore value: [0.11111111 0.78787879 0.78787879 0.7 0.76190476 0.3 0.66666667 0.57894737 0.68292683 0.53846154] mean value: 0.5915775851590999 key: train_fscore value: [0.30588235 0.82508251 0.84459459 0.75842697 0.77094972 0.17721519 0.81456954 0.81553398 0.76086957 0.77256318] mean value: 0.6845687591741604 key: test_precision value: [0.5 0.76470588 0.76470588 0.58333333 0.61538462 0.75 0.71428571 0.5 0.56 0.7 ] mean value: 0.6452415427709546 key: train_precision value: [1. 0.78616352 0.82236842 0.63679245 0.64485981 1. 0.77848101 0.76363636 0.625 0.80451128] mean value: 0.786181286346959 key: test_recall value: [0.0625 0.8125 0.8125 0.875 1. 0.1875 0.625 0.6875 0.875 0.4375] mean value: 0.6375 key: train_recall value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.18055556 0.86805556 0.86805556 0.9375 0.95833333 0.09722222 0.85416667 0.875 0.97222222 0.74305556] mean value: 0.7354166666666667 key: test_accuracy value: [0.5 0.78125 0.78125 0.625 0.6875 0.5625 0.6875 0.5 0.59375 0.625 ] mean value: 0.634375 key: train_accuracy value: [0.59027778 0.81597222 0.84027778 0.70138889 0.71527778 0.54861111 0.80555556 0.80208333 0.69444444 0.78125 ] mean value: 0.7295138888888889 key: test_roc_auc value: [0.5 0.78125 0.78125 0.625 0.6875 0.5625 0.6875 0.5 0.59375 0.625 ] mean value: 0.634375 key: train_roc_auc value: [0.59027778 0.81597222 0.84027778 0.70138889 0.71527778 0.54861111 0.80555556 0.80208333 0.69444444 0.78125 ] mean value: 0.7295138888888889 key: test_jcc value: [0.05882353 0.65 0.65 0.53846154 0.61538462 0.17647059 0.5 0.40740741 0.51851852 0.36842105] mean value: 0.4483487250050717 key: train_jcc value: [0.18055556 0.70224719 0.73099415 0.61085973 0.62727273 0.09722222 0.68715084 0.68852459 0.61403509 0.62941176] mean value: 0.5568273857193253 MCC on Blind test: 0.32 MCC on Training: 0.3 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.08719373 0.09956837 0.10135245 0.0868392 0.07955289 0.07862782 0.08217502 0.07981753 0.0798769 0.08605456] mean value: 0.08610584735870361 key: score_time value: [0.01214862 0.0121963 0.01075554 0.01198959 0.01086569 0.01066232 0.01183677 0.01133776 0.01098633 0.01079416] mean value: 0.011357307434082031 key: test_mcc value: [0.62994079 0.56360186 0.69991324 0.438357 0.37796447 0.375 0.59215653 0.37796447 0.50395263 0.5 ] mean value: 0.5058850995519523 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82352941 0.78787879 0.85714286 0.72727273 0.70588235 0.6875 0.74074074 0.70588235 0.76470588 0.75 ] mean value: 0.7550535113035113 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.77777778 0.76470588 0.78947368 0.70588235 0.66666667 0.6875 0.90909091 0.66666667 0.72222222 0.75 ] mean value: 0.7439986161928887 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.8125 0.9375 0.75 0.75 0.6875 0.625 0.75 0.8125 0.75 ] mean value: 0.775 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.78125 0.84375 0.71875 0.6875 0.6875 0.78125 0.6875 0.75 0.75 ] mean value: 0.75 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.78125 0.84375 0.71875 0.6875 0.6875 0.78125 0.6875 0.75 0.75 ] mean value: 0.75 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7 0.65 0.75 0.57142857 0.54545455 0.52380952 0.58823529 0.54545455 0.61904762 0.6 ] mean value: 0.6093430099312451 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.51 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... ?a07cU0oBuilding estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... @??PؒUpMT?Y@FG0?zGz?@@[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16969228 0.16217756 0.16204238 0.16224408 0.1614089 0.17117286 0.16538382 0.16290164 0.16052341 0.16567492] mean value: 0.1643221855163574 key: score_time value: [0.01647997 0.01534653 0.01529789 0.01524878 0.01905918 0.01561618 0.01584983 0.01513433 0.0156219 0.0162375 ] mean value: 0.015989208221435548 key: test_mcc value: [0.66801039 0.49819858 0.69631062 0.65465367 0.65465367 0.3927922 0.4454354 0.47826087 0.52623481 0.74194083] mean value: 0.5756491045823054 key: train_mcc value: [0.86252033 0.88469861 0.84807706 0.84213486 0.81825389 0.86138365 0.85675996 0.88063245 0.87141174 0.86066645] mean value: 0.8586538988214534 key: test_fscore value: [0.80952381 0.72727273 0.85106383 0.81818182 0.81818182 0.68181818 0.68292683 0.73913043 0.74418605 0.875 ] mean value: 0.7747285495328118 key: train_fscore value: [0.9276808 0.94117647 0.92039801 0.91891892 0.90640394 0.92874693 0.92610837 0.93857494 0.93333333 0.92978208] mean value: 0.9271123795712983 key: test_precision value: [0.89473684 0.8 0.83333333 0.85714286 0.85714286 0.71428571 0.77777778 0.73913043 0.8 0.84 ] mean value: 0.8113549816570412 key: train_precision value: [0.96373057 0.95522388 0.95360825 0.93969849 0.92929293 0.94974874 0.94949495 0.95979899 0.95939086 0.93658537] mean value: 0.9496573036709363 key: test_recall value: [0.73913043 0.66666667 0.86956522 0.7826087 0.7826087 0.65217391 0.60869565 0.73913043 0.69565217 0.91304348] mean value: 0.744927536231884 key: train_recall value: [0.89423077 0.92753623 0.88942308 0.89903846 0.88461538 0.90865385 0.90384615 0.91826923 0.90865385 0.92307692] mean value: 0.905734392419175 key: test_accuracy value: [0.82978723 0.74468085 0.84782609 0.82608696 0.82608696 0.69565217 0.7173913 0.73913043 0.76086957 0.86956522] mean value: 0.7857076780758556 key: train_accuracy value: [0.93012048 0.94216867 0.92307692 0.92067308 0.90865385 0.93028846 0.92788462 0.93990385 0.93509615 0.93028846] mean value: 0.9288154541241891 key: test_roc_auc value: [0.82789855 0.74637681 0.84782609 0.82608696 0.82608696 0.69565217 0.7173913 0.73913043 0.76086957 0.86956522] mean value: 0.7856884057971014 key: train_roc_auc value: [0.93020717 0.9421335 0.92307692 0.92067308 0.90865385 0.93028846 0.92788462 0.93990385 0.93509615 0.93028846] mean value: 0.9288206057227797 key: test_jcc value: [0.68 0.57142857 0.74074074 0.69230769 0.69230769 0.51724138 0.51851852 0.5862069 0.59259259 0.77777778] mean value: 0.6369121861535655 key: train_jcc value: [0.86511628 0.88888889 0.85253456 0.85 0.82882883 0.86697248 0.86238532 0.88425926 0.875 0.86877828] mean value: 0.864276389696685 MCC on Blind test: 0.19 MCC on Training: 0.58 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.22721958 0.23011374 0.27684712 0.2762239 0.26074934 0.25290251 0.22250676 0.25746369 0.24922562 0.29761624] mean value: 0.25508685111999513 key: score_time value: [0.04100513 0.05182886 0.05488753 0.03652477 0.04333138 0.07296157 0.04310846 0.06669021 0.04350424 0.07322574] mean value: 0.052706789970397946 key: test_mcc value: [0.66801039 0.53734864 0.78334945 0.6092718 0.78334945 0.56521739 0.57396402 0.58549055 0.48566186 0.74194083] mean value: 0.633360438630169 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.80952381 0.75555556 0.88888889 0.8 0.88888889 0.7826087 0.76190476 0.75 0.71428571 0.875 ] mean value: 0.8026656314699793 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.80952381 0.90909091 0.81818182 0.90909091 0.7826087 0.84210526 0.88235294 0.78947368 0.84 ] mean value: 0.8477164872189775 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.70833333 0.86956522 0.7826087 0.86956522 0.7826087 0.69565217 0.65217391 0.65217391 0.91304348] mean value: 0.7664855072463768 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82978723 0.76595745 0.89130435 0.80434783 0.89130435 0.7826087 0.7826087 0.7826087 0.73913043 0.86956522] mean value: 0.8139222941720629 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82789855 0.76721014 0.89130435 0.80434783 0.89130435 0.7826087 0.7826087 0.7826087 0.73913043 0.86956522] mean value: 0.8138586956521738 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68 0.60714286 0.8 0.66666667 0.8 0.64285714 0.61538462 0.6 0.55555556 0.77777778] mean value: 0.6745384615384615 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.63 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04520178 0.02677774 0.02755904 0.02933002 0.02695155 0.02885389 0.02732253 0.02616167 0.0279603 0.02913451] mean value: 0.02952530384063721 key: score_time value: [0.00914741 0.00896573 0.00924778 0.00892878 0.00914621 0.00887895 0.00923967 0.00933957 0.00949168 0.00938821] mean value: 0.009177398681640626 key: test_mcc value: [0.62966842 0.49819858 0.65465367 0.54772256 0.52623481 0.61394061 0.63900965 0.52623481 0.40533961 0.69631062] mean value: 0.5737313349471567 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.7804878 0.72727273 0.83333333 0.71794872 0.74418605 0.79069767 0.76923077 0.74418605 0.65 0.84444444] mean value: 0.7601787564549901 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88888889 0.8 0.8 0.875 0.8 0.85 0.9375 0.8 0.76470588 0.86363636] mean value: 0.8379731134878193 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.69565217 0.66666667 0.86956522 0.60869565 0.69565217 0.73913043 0.65217391 0.69565217 0.56521739 0.82608696] mean value: 0.7014492753623188 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80851064 0.74468085 0.82608696 0.76086957 0.76086957 0.80434783 0.80434783 0.76086957 0.69565217 0.84782609] mean value: 0.7814061054579092 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.80615942 0.74637681 0.82608696 0.76086957 0.76086957 0.80434783 0.80434783 0.76086957 0.69565217 0.84782609] mean value: 0.7813405797101449 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.64 0.57142857 0.71428571 0.56 0.59259259 0.65384615 0.625 0.59259259 0.48148148 0.73076923] mean value: 0.6161996336996337 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.57 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01141596 0.0117259 0.01163363 0.01132679 0.0116241 0.01182795 0.01175523 0.01142168 0.0113368 0.01157498] mean value: 0.011564302444458007 key: score_time value: [0.00986505 0.01000571 0.00966644 0.01004291 0.01002312 0.01012039 0.01013923 0.01019597 0.00960398 0.00975513] mean value: 0.009941792488098145 key: test_mcc value: [0.48913043 0.5326087 0.52223297 0.48566186 0.6092718 0.4454354 0.58549055 0.39130435 0.48007936 0.43519414] mean value: 0.49764095616702486 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.73913043 0.76595745 0.76595745 0.71428571 0.8 0.68292683 0.75 0.69565217 0.72727273 0.71111111] mean value: 0.7352293884250519 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.73913043 0.7826087 0.75 0.78947368 0.81818182 0.77777778 0.88235294 0.69565217 0.76190476 0.72727273] mean value: 0.7724355014871909 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.75 0.7826087 0.65217391 0.7826087 0.60869565 0.65217391 0.69565217 0.69565217 0.69565217] mean value: 0.7054347826086955 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.74468085 0.76595745 0.76086957 0.73913043 0.80434783 0.7173913 0.7826087 0.69565217 0.73913043 0.7173913 ] mean value: 0.7467160037002775 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.74456522 0.76630435 0.76086957 0.73913043 0.80434783 0.7173913 0.7826087 0.69565217 0.73913043 0.7173913 ] mean value: 0.7467391304347826 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5862069 0.62068966 0.62068966 0.55555556 0.66666667 0.51851852 0.6 0.53333333 0.57142857 0.55172414] mean value: 0.5824812990330231 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.5 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.13245225 0.13100147 0.13249183 0.13226104 0.13527393 0.12765956 0.12941098 0.1304903 0.12766385 0.12916279] mean value: 0.13078680038452148 key: score_time value: [0.01863456 0.01900887 0.01846075 0.02060509 0.01900554 0.01915431 0.01901078 0.01907229 0.01804209 0.01801324] mean value: 0.018900752067565918 key: test_mcc value: [0.66121206 0.53734864 0.6092718 0.70164642 0.65217391 0.56736651 0.69631062 0.47826087 0.52623481 0.61394061] mean value: 0.6043766257510134 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.81818182 0.75555556 0.8 0.85714286 0.82608696 0.79166667 0.84444444 0.73913043 0.74418605 0.81632653] mean value: 0.7992721310419563 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.80952381 0.81818182 0.80769231 0.82608696 0.76 0.86363636 0.73913043 0.8 0.76923077] mean value: 0.8050625316712272 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.7826087 0.70833333 0.7826087 0.91304348 0.82608696 0.82608696 0.82608696 0.73913043 0.69565217 0.86956522] mean value: 0.7969202898550725 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82978723 0.76595745 0.80434783 0.84782609 0.82608696 0.7826087 0.84782609 0.73913043 0.76086957 0.80434783] mean value: 0.8008788159111934 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82880435 0.76721014 0.80434783 0.84782609 0.82608696 0.7826087 0.84782609 0.73913043 0.76086957 0.80434783] mean value: 0.8009057971014493 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.69230769 0.60714286 0.66666667 0.75 0.7037037 0.65517241 0.73076923 0.5862069 0.59259259 0.68965517] mean value: 0.6674217225941363 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.6 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.6327374 0.64426088 0.64480019 0.63451743 0.63769412 0.67118168 0.65671206 0.66938663 0.6337781 0.62592649] mean value: 0.6450994968414306 key: score_time value: [0.00916815 0.01061821 0.01017284 0.00928283 0.01034999 0.01038694 0.01035953 0.00942779 0.00954771 0.00922251] mean value: 0.009853649139404296 key: test_mcc value: [0.66801039 0.58428436 0.74194083 0.78334945 0.82608696 0.52223297 0.58549055 0.52623481 0.53452248 0.65465367] mean value: 0.6426806474000261 key: train_mcc value: [0.99519231 0.99040717 0.99038462 0.99038462 0.9904304 0.99520381 0.9904304 0.98558831 0.9904304 1. ] mean value: 0.9918452041859153 key: test_fscore value: [0.80952381 0.77272727 0.875 0.89361702 0.91304348 0.75555556 0.75 0.74418605 0.73170732 0.83333333] mean value: 0.8078693834262234 key: train_fscore value: [0.99759036 0.99514563 0.99519231 0.99519231 0.99516908 0.99759036 0.99516908 0.99277108 0.99516908 1. ] mean value: 0.9958989300058303 key: test_precision value: [0.89473684 0.85 0.84 0.875 0.91304348 0.77272727 0.88235294 0.8 0.83333333 0.8 ] mean value: 0.8461193867603208 key: train_precision value: [1. 1. 0.99519231 0.99519231 1. 1. 1. 0.99516908 1. 1. ] mean value: 0.998555369751022 key: test_recall value: [0.73913043 0.70833333 0.91304348 0.91304348 0.91304348 0.73913043 0.65217391 0.69565217 0.65217391 0.86956522] mean value: 0.7795289855072464 key: train_recall value: [0.99519231 0.99033816 0.99519231 0.99519231 0.99038462 0.99519231 0.99038462 0.99038462 0.99038462 1. ] mean value: 0.9932645856558902 key: test_accuracy value: [0.82978723 0.78723404 0.86956522 0.89130435 0.91304348 0.76086957 0.7826087 0.76086957 0.76086957 0.82608696] mean value: 0.8182238667900092 key: train_accuracy value: [0.99759036 0.99518072 0.99519231 0.99519231 0.99519231 0.99759615 0.99519231 0.99278846 0.99519231 1. ] mean value: 0.9959117238183504 key: test_roc_auc value: [0.82789855 0.78894928 0.86956522 0.89130435 0.91304348 0.76086957 0.7826087 0.76086957 0.76086957 0.82608696] mean value: 0.8182065217391304 key: train_roc_auc value: [0.99759615 0.99516908 0.99519231 0.99519231 0.99519231 0.99759615 0.99519231 0.99278846 0.99519231 1. ] mean value: 0.9959111389817912 key: test_jcc value: [0.68 0.62962963 0.77777778 0.80769231 0.84 0.60714286 0.6 0.59259259 0.57692308 0.71428571] mean value: 0.6826043956043957 key: train_jcc value: [0.99519231 0.99033816 0.99043062 0.99043062 0.99038462 0.99519231 0.99038462 0.98564593 0.99038462 1. ] mean value: 0.9918383802823163 MCC on Blind test: 0.19 MCC on Training: 0.64 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00944161 0.00940251 0.00986743 0.00962663 0.00935626 0.00950098 0.00963044 0.0092957 0.00946498 0.010041 ] mean value: 0.00956275463104248 key: score_time value: [0.00874233 0.00871396 0.00887418 0.00878215 0.00876451 0.00873446 0.00885153 0.0088253 0.0088141 0.00875545] mean value: 0.00878579616546631 key: test_mcc value: [0.10929125 0.36265926 0.10540926 0.53452248 0.30905755 0.08908708 0.39130435 0.3927922 0.13043478 0.52223297] mean value: 0.2946791180124452 key: train_mcc value: [0.33198447 0.34572522 0.29265423 0.31909761 0.33843685 0.3568265 0.36091494 0.35710892 0.33880135 0.3568265 ] mean value: 0.3398376587422679 key: test_fscore value: [0.57142857 0.70588235 0.6440678 0.78431373 0.61904762 0.58823529 0.69565217 0.70833333 0.56521739 0.75555556] mean value: 0.663773381374166 key: train_fscore value: [0.6833713 0.68372093 0.69120654 0.67579909 0.68493151 0.68981481 0.68705882 0.69124424 0.68636364 0.68981481] mean value: 0.6863325695366626 key: test_precision value: [0.53846154 0.66666667 0.52777778 0.71428571 0.68421053 0.53571429 0.69565217 0.68 0.56521739 0.77272727] mean value: 0.6380713347166436 key: train_precision value: [0.64935065 0.65919283 0.60142349 0.64347826 0.65217391 0.66517857 0.67281106 0.66371681 0.65086207 0.66517857] mean value: 0.652336622181007 key: test_recall value: [0.60869565 0.75 0.82608696 0.86956522 0.56521739 0.65217391 0.69565217 0.73913043 0.56521739 0.73913043] mean value: 0.7010869565217391 key: train_recall value: [0.72115385 0.71014493 0.8125 0.71153846 0.72115385 0.71634615 0.70192308 0.72115385 0.72596154 0.71634615] mean value: 0.7258221850613155 key: test_accuracy value: [0.55319149 0.68085106 0.54347826 0.76086957 0.65217391 0.54347826 0.69565217 0.69565217 0.56521739 0.76086957] mean value: 0.6451433857539315 key: train_accuracy value: [0.66506024 0.67228916 0.63701923 0.65865385 0.66826923 0.67788462 0.68028846 0.67788462 0.66826923 0.67788462] mean value: 0.6683503243744209 key: test_roc_auc value: [0.55434783 0.67934783 0.54347826 0.76086957 0.65217391 0.54347826 0.69565217 0.69565217 0.56521739 0.76086957] mean value: 0.6451086956521739 key: train_roc_auc value: [0.66492475 0.67238016 0.63701923 0.65865385 0.66826923 0.67788462 0.68028846 0.67788462 0.66826923 0.67788462] mean value: 0.6683458751393533 key: test_jcc value: [0.4 0.54545455 0.475 0.64516129 0.44827586 0.41666667 0.53333333 0.5483871 0.39393939 0.60714286] mean value: 0.5013361045702535 key: train_jcc value: [0.51903114 0.51943463 0.528125 0.51034483 0.52083333 0.52650177 0.52329749 0.52816901 0.52249135 0.52650177] mean value: 0.5224730319937125 MCC on Blind test: 0.57 MCC on Training: 0.29 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12923717 0.13667464 0.15643144 0.1913867 0.20138431 0.10491729 0.15970707 0.15003252 0.16396356 0.13223863] mean value: 0.15259733200073242 key: score_time value: [0.02364492 0.0145669 0.0235517 0.03100061 0.04033375 0.01682305 0.02609992 0.02338576 0.02333403 0.02344298] mean value: 0.024618363380432128 key: test_mcc value: [0.40437762 0.45948781 0.52623481 0.48007936 0.53452248 0.47826087 0.47826087 0.40533961 0.43852901 0.43519414] mean value: 0.46402865867312926 key: train_mcc value: [0.89414635 0.91020802 0.90029831 0.92875444 0.9053541 0.88563951 0.89929833 0.92461695 0.90451542 0.90916885] mean value: 0.9062000284400407 key: test_fscore value: [0.68181818 0.69767442 0.74418605 0.72727273 0.73170732 0.73913043 0.73913043 0.65 0.69767442 0.72340426] mean value: 0.7131998234769377 key: train_fscore value: [0.94660194 0.95261845 0.94814815 0.96314496 0.95049505 0.9408867 0.94890511 0.96039604 0.95098039 0.95354523] mean value: 0.9515722029442074 key: test_precision value: [0.71428571 0.78947368 0.8 0.76190476 0.83333333 0.73913043 0.73913043 0.76470588 0.75 0.70833333] mean value: 0.7600297578985827 key: train_precision value: [0.95588235 0.98453608 0.97461929 0.98492462 0.97959184 0.96464646 0.96059113 0.98979592 0.97 0.97014925] mean value: 0.9734736954355858 key: test_recall value: [0.65217391 0.625 0.69565217 0.69565217 0.65217391 0.73913043 0.73913043 0.56521739 0.65217391 0.73913043] mean value: 0.6755434782608696 key: train_recall value: [0.9375 0.92270531 0.92307692 0.94230769 0.92307692 0.91826923 0.9375 0.93269231 0.93269231 0.9375 ] mean value: 0.9307320698625047 key: test_accuracy value: [0.70212766 0.72340426 0.76086957 0.73913043 0.76086957 0.73913043 0.73913043 0.69565217 0.7173913 0.7173913 ] mean value: 0.7295097132284922 key: train_accuracy value: [0.94698795 0.95421687 0.94951923 0.96394231 0.95192308 0.94230769 0.94951923 0.96153846 0.95192308 0.95432692] mean value: 0.9526204819277109 key: test_roc_auc value: [0.70108696 0.72554348 0.76086957 0.73913043 0.76086957 0.73913043 0.73913043 0.69565217 0.7173913 0.7173913 ] mean value: 0.7296195652173914 key: train_roc_auc value: [0.94701087 0.95414112 0.94951923 0.96394231 0.95192308 0.94230769 0.94951923 0.96153846 0.95192308 0.95432692] mean value: 0.9526151988108511 key: test_jcc value: [0.51724138 0.53571429 0.59259259 0.57142857 0.57692308 0.5862069 0.5862069 0.48148148 0.53571429 0.56666667] mean value: 0.5550176132934752 key: train_jcc value: [0.89861751 0.90952381 0.90140845 0.92890995 0.90566038 0.88837209 0.90277778 0.92380952 0.90654206 0.91121495] mean value: 0.9076836505670249 MCC on Blind test: -0.11 MCC on Training: 0.46 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01247811 0.01022363 0.01047921 0.01122808 0.01036906 0.01043034 0.01022983 0.01189566 0.01106238 0.0104475 ] mean value: 0.010884380340576172 key: score_time value: [0.05314732 0.0139482 0.01346684 0.01980376 0.01384544 0.0186677 0.01772285 0.01375914 0.01381612 0.01481175] mean value: 0.019298911094665527 key: test_mcc value: [0.15063546 0.19490273 0.04364358 0.30434783 0.30905755 0.26111648 0.39735971 0.04347826 0.30550505 0.3927922 ] mean value: 0.2402838839410535 key: train_mcc value: [0.53256679 0.56158085 0.48562744 0.52433547 0.55895988 0.53848643 0.57702979 0.5726517 0.55779546 0.59640203] mean value: 0.5505435841069972 key: test_fscore value: [0.58333333 0.57777778 0.54166667 0.65217391 0.61904762 0.63829787 0.66666667 0.52173913 0.66666667 0.70833333] mean value: 0.617570297931075 key: train_fscore value: [0.76849642 0.77750611 0.74463007 0.75794621 0.77114428 0.76811594 0.79047619 0.78132678 0.77669903 0.79512195] mean value: 0.7731462987169813 key: test_precision value: [0.56 0.61904762 0.52 0.65217391 0.68421053 0.625 0.73684211 0.52173913 0.64 0.68 ] mean value: 0.6239013294104827 key: train_precision value: [0.76303318 0.78712871 0.73933649 0.77114428 0.79896907 0.77184466 0.78301887 0.79899497 0.78431373 0.80693069] mean value: 0.7804714653442224 key: test_recall value: [0.60869565 0.54166667 0.56521739 0.65217391 0.56521739 0.65217391 0.60869565 0.52173913 0.69565217 0.73913043] mean value: 0.6150362318840579 key: train_recall value: [0.77403846 0.76811594 0.75 0.74519231 0.74519231 0.76442308 0.79807692 0.76442308 0.76923077 0.78365385] mean value: 0.7662346711259754 key: test_accuracy value: [0.57446809 0.59574468 0.52173913 0.65217391 0.65217391 0.63043478 0.69565217 0.52173913 0.65217391 0.69565217] mean value: 0.6191951896392229 key: train_accuracy value: [0.76626506 0.78072289 0.74278846 0.76201923 0.77884615 0.76923077 0.78846154 0.78605769 0.77884615 0.79807692] mean value: 0.7751314874884152 key: test_roc_auc value: [0.57518116 0.59692029 0.52173913 0.65217391 0.65217391 0.63043478 0.69565217 0.52173913 0.65217391 0.69565217] mean value: 0.6193840579710145 key: train_roc_auc value: [0.76624628 0.78069259 0.74278846 0.76201923 0.77884615 0.76923077 0.78846154 0.78605769 0.77884615 0.79807692] mean value: 0.7751265793385359 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.41176471 0.40625 0.37142857 0.48387097 0.44827586 0.46875 0.5 0.35294118 0.5 0.5483871 ] mean value: 0.44916683803666063 key: train_jcc value: [0.62403101 0.636 0.59315589 0.61023622 0.62753036 0.62352941 0.65354331 0.64112903 0.63492063 0.65991903] mean value: 0.630399490050307 MCC on Blind test: 0.08 MCC on Training: 0.24 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0316925 0.03898811 0.06297851 0.07713032 0.04189038 0.07010245 0.03854942 0.04320502 0.07552075 0.07505536] mean value: 0.05551128387451172 key: score_time value: [0.01211977 0.01246834 0.02323675 0.01226759 0.02386212 0.01246381 0.01238918 0.01215601 0.01720262 0.01224756] mean value: 0.015041375160217285 key: test_mcc value: [0.40398551 0.40398551 0.3927922 0.48566186 0.48007936 0.39130435 0.6092718 0.48007936 0.35634832 0.39735971] mean value: 0.4400867971553531 key: train_mcc value: [0.71088143 0.71588535 0.74038462 0.73107346 0.70205291 0.72596993 0.70680429 0.75003467 0.74052156 0.6971879 ] mean value: 0.7220796116257239 key: test_fscore value: [0.69565217 0.70833333 0.68181818 0.76 0.72727273 0.69565217 0.8 0.72727273 0.63414634 0.72 ] mean value: 0.7150147658986471 key: train_fscore value: [0.85507246 0.85918854 0.87019231 0.86341463 0.84951456 0.8626506 0.85230024 0.87439614 0.86893204 0.84745763] mean value: 0.860311915862599 key: test_precision value: [0.69565217 0.70833333 0.71428571 0.7037037 0.76190476 0.69565217 0.81818182 0.76190476 0.72222222 0.66666667] mean value: 0.724850733002907 key: train_precision value: [0.8592233 0.8490566 0.87019231 0.87623762 0.85784314 0.8647343 0.85853659 0.87864078 0.87745098 0.85365854] mean value: 0.8645574152013358 key: test_recall value: [0.69565217 0.70833333 0.65217391 0.82608696 0.69565217 0.69565217 0.7826087 0.69565217 0.56521739 0.7826087 ] mean value: 0.709963768115942 key: train_recall value: [0.85096154 0.86956522 0.87019231 0.85096154 0.84134615 0.86057692 0.84615385 0.87019231 0.86057692 0.84134615] mean value: 0.8561872909698997 key: test_accuracy value: [0.70212766 0.70212766 0.69565217 0.73913043 0.73913043 0.69565217 0.80434783 0.73913043 0.67391304 0.69565217] mean value: 0.718686401480111 key: train_accuracy value: [0.85542169 0.85783133 0.87019231 0.86538462 0.85096154 0.86298077 0.85336538 0.875 0.87019231 0.84855769] mean value: 0.8609887627432808 key: test_roc_auc value: [0.70199275 0.70199275 0.69565217 0.73913043 0.73913043 0.69565217 0.80434783 0.73913043 0.67391304 0.69565217] mean value: 0.7186594202898551 key: train_roc_auc value: [0.85543246 0.85785953 0.87019231 0.86538462 0.85096154 0.86298077 0.85336538 0.875 0.87019231 0.84855769] mean value: 0.8609926607209216 key: test_jcc value: [0.53333333 0.5483871 0.51724138 0.61290323 0.57142857 0.53333333 0.66666667 0.57142857 0.46428571 0.5625 ] mean value: 0.558150789236718 key: train_jcc value: [0.74683544 0.75313808 0.77021277 0.75965665 0.73839662 0.75847458 0.74261603 0.77682403 0.76824034 0.73529412] mean value: 0.7549688666498241 MCC on Blind test: 0.12 MCC on Training: 0.44 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03906488 0.04012251 0.04151058 0.0411427 0.04115009 0.04180527 0.03877759 0.05028844 0.04633594 0.07155228] mean value: 0.045175027847290036 key: score_time value: [0.0122962 0.01205444 0.01242948 0.01239347 0.01244402 0.01251459 0.01251626 0.01242304 0.01247144 0.01275325] mean value: 0.012429618835449218 key: test_mcc value: [0.53734864 0.27586252 0.43852901 0.43852901 0.52223297 0.26111648 0.52223297 0.30550505 0.39735971 0.49541508] mean value: 0.4194131441861305 key: train_mcc value: [0.56283991 0.55423083 0.56994856 0.60714608 0.53041777 0.60647053 0.56944396 0.56825417 0.57360889 0.55432792] mean value: 0.5696688605775088 key: test_fscore value: [0.7755102 0.65306122 0.73469388 0.73469388 0.76595745 0.63829787 0.76595745 0.66666667 0.66666667 0.76923077] mean value: 0.7170736052195019 key: train_fscore value: [0.78886311 0.7852194 0.79357798 0.80930233 0.77314815 0.80751174 0.79262673 0.78971963 0.79350348 0.78422274] mean value: 0.7917695273433223 key: test_precision value: [0.73076923 0.64 0.69230769 0.69230769 0.75 0.625 0.75 0.64 0.73684211 0.68965517] mean value: 0.6946881893061567 key: train_precision value: [0.76233184 0.75221239 0.75877193 0.78378378 0.74553571 0.78899083 0.76106195 0.76818182 0.76681614 0.75784753] mean value: 0.7645533923742205 key: test_recall value: [0.82608696 0.66666667 0.7826087 0.7826087 0.7826087 0.65217391 0.7826087 0.69565217 0.60869565 0.86956522] mean value: 0.7449275362318841 key: train_recall value: [0.81730769 0.82125604 0.83173077 0.83653846 0.80288462 0.82692308 0.82692308 0.8125 0.82211538 0.8125 ] mean value: 0.821067911557042 key: test_accuracy value: [0.76595745 0.63829787 0.7173913 0.7173913 0.76086957 0.63043478 0.76086957 0.65217391 0.69565217 0.73913043] mean value: 0.7078168362627196 key: train_accuracy value: [0.78072289 0.77590361 0.78365385 0.80288462 0.76442308 0.80288462 0.78365385 0.78365385 0.78605769 0.77644231] mean value: 0.7840280352177943 key: test_roc_auc value: [0.76721014 0.63768116 0.7173913 0.7173913 0.76086957 0.63043478 0.76086957 0.65217391 0.69565217 0.73913043] mean value: 0.7078804347826086 key: train_roc_auc value: [0.78063452 0.77601263 0.78365385 0.80288462 0.76442308 0.80288462 0.78365385 0.78365385 0.78605769 0.77644231] mean value: 0.7840301003344482 key: test_jcc value: [0.63333333 0.48484848 0.58064516 0.58064516 0.62068966 0.46875 0.62068966 0.5 0.5 0.625 ] mean value: 0.5614601451107291 key: train_jcc value: [0.651341 0.64638783 0.65779468 0.6796875 0.63018868 0.67716535 0.65648855 0.65250965 0.65769231 0.64503817] mean value: 0.6554293717009491 MCC on Blind test: 0.44 MCC on Training: 0.42 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.50839543 0.52644229 0.51978564 0.6598413 0.52846456 0.64202619 0.5265789 0.69545221 0.55552268 0.55018902] mean value: 0.5712698221206665 key: score_time value: [0.01260138 0.01222849 0.01512671 0.01218176 0.01503015 0.01213503 0.01494479 0.01267195 0.01563764 0.01311445] mean value: 0.013567233085632324 key: test_mcc value: [0.44746377 0.36231884 0.34815531 0.43519414 0.52623481 0.22075539 0.48007936 0.34815531 0.35634832 0.43519414] mean value: 0.3959899397794407 key: train_mcc value: [0.53770358 0.53903695 0.58874714 0.54970568 0.75013872 0.46635154 0.73558542 0.56360186 0.74052156 0.74069284] mean value: 0.6212085309784311 key: test_fscore value: [0.72340426 0.68085106 0.68085106 0.72340426 0.74418605 0.64 0.72727273 0.68085106 0.63414634 0.72340426] mean value: 0.6958371072694578 key: train_fscore value: [0.77358491 0.77674419 0.80184332 0.78240741 0.87378641 0.73381295 0.86746988 0.78787879 0.86893204 0.86829268] mean value: 0.8134752563652565 key: test_precision value: [0.70833333 0.69565217 0.66666667 0.70833333 0.8 0.59259259 0.76190476 0.66666667 0.72222222 0.70833333] mean value: 0.7030705083965954 key: train_precision value: [0.75925926 0.74887892 0.7699115 0.75446429 0.88235294 0.73205742 0.86956522 0.76470588 0.87745098 0.88118812] mean value: 0.8039834529557837 key: test_recall value: [0.73913043 0.66666667 0.69565217 0.73913043 0.69565217 0.69565217 0.69565217 0.69565217 0.56521739 0.73913043] mean value: 0.6927536231884057 key: train_recall value: [0.78846154 0.80676329 0.83653846 0.8125 0.86538462 0.73557692 0.86538462 0.8125 0.86057692 0.85576923] mean value: 0.8239455592716463 key: test_accuracy value: [0.72340426 0.68085106 0.67391304 0.7173913 0.76086957 0.60869565 0.73913043 0.67391304 0.67391304 0.7173913 ] mean value: 0.6969472710453284 key: train_accuracy value: [0.7686747 0.7686747 0.79326923 0.77403846 0.875 0.73317308 0.86778846 0.78125 0.87019231 0.87019231] mean value: 0.8102253243744209 key: test_roc_auc value: [0.72373188 0.68115942 0.67391304 0.7173913 0.76086957 0.60869565 0.73913043 0.67391304 0.67391304 0.7173913 ] mean value: 0.6970108695652174 key: train_roc_auc value: [0.7686269 0.76876626 0.79326923 0.77403846 0.875 0.73317308 0.86778846 0.78125 0.87019231 0.87019231] mean value: 0.8102297008547008 key: test_jcc value: [0.56666667 0.51612903 0.51612903 0.56666667 0.59259259 0.47058824 0.57142857 0.51612903 0.46428571 0.56666667] mean value: 0.534728221037519 key: train_jcc value: [0.63076923 0.63498099 0.66923077 0.64258555 0.77586207 0.57954545 0.76595745 0.65 0.76824034 0.76724138] mean value: 0.6884413232901422 MCC on Blind test: 0.16 MCC on Training: 0.4 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.63630271 1.25863171 0.76550698 0.52360129 0.51198506 0.87051153 2.04261684 0.53073072 1.33495784 0.75939655] mean value: 0.9234241247177124 key: score_time value: [0.01263046 0.01267958 0.01249671 0.01229191 0.01239014 0.012568 0.01514673 0.01242685 0.01263475 0.01254225] mean value: 0.01278073787689209 key: test_mcc value: [0.54621844 0.45173716 0.4454354 0.48566186 0.58549055 0.1754116 0.43519414 0.34815531 0.43452409 0.39605902] mean value: 0.43038875904107476 key: train_mcc value: [0.54740879 0.66456531 0.60704667 0.51595339 0.53066374 0.62981497 0.83657714 0.58189892 0.71402586 0.50450905] mean value: 0.6132463844840083 key: test_fscore value: [0.78431373 0.71111111 0.68292683 0.76 0.75 0.6122449 0.72340426 0.68085106 0.61111111 0.73684211] mean value: 0.7052805099351988 key: train_fscore value: [0.78947368 0.8241206 0.7696477 0.77559913 0.7150838 0.81534772 0.91866029 0.78832117 0.83018868 0.776 ] mean value: 0.800244276715793 key: test_precision value: [0.71428571 0.76190476 0.77777778 0.7037037 0.88235294 0.57692308 0.70833333 0.66666667 0.84615385 0.61764706] mean value: 0.725574888074888 key: train_precision value: [0.72580645 0.85863874 0.88198758 0.70916335 0.85333333 0.81339713 0.91428571 0.79802956 0.94478528 0.66438356] mean value: 0.8163810690495049 key: test_recall value: [0.86956522 0.66666667 0.60869565 0.82608696 0.65217391 0.65217391 0.73913043 0.69565217 0.47826087 0.91304348] mean value: 0.7101449275362318 key: train_recall value: [0.86538462 0.79227053 0.68269231 0.85576923 0.61538462 0.81730769 0.92307692 0.77884615 0.74038462 0.93269231] mean value: 0.8003808992939427 key: test_accuracy value: [0.76595745 0.72340426 0.7173913 0.73913043 0.7826087 0.58695652 0.7173913 0.67391304 0.69565217 0.67391304] mean value: 0.707631822386679 key: train_accuracy value: [0.7686747 0.8313253 0.79567308 0.75240385 0.75480769 0.81490385 0.91826923 0.79086538 0.84855769 0.73076923] mean value: 0.8006249999999999 key: test_roc_auc value: [0.76811594 0.72463768 0.7173913 0.73913043 0.7826087 0.58695652 0.7173913 0.67391304 0.69565217 0.67391304] mean value: 0.7079710144927536 key: train_roc_auc value: [0.7684411 0.83123142 0.79567308 0.75240385 0.75480769 0.81490385 0.91826923 0.79086538 0.84855769 0.73076923] mean value: 0.8005922519509477 key: test_jcc value: [0.64516129 0.55172414 0.51851852 0.61290323 0.6 0.44117647 0.56666667 0.51612903 0.44 0.58333333] mean value: 0.5475612675424886 key: train_jcc value: [0.65217391 0.7008547 0.62555066 0.63345196 0.55652174 0.68825911 0.84955752 0.65060241 0.70967742 0.63398693] mean value: 0.6700636359650541 MCC on Blind test: 0.37 MCC on Training: 0.43 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01352811 0.01359987 0.00993109 0.0098424 0.0092504 0.00907898 0.00953269 0.00942206 0.00922728 0.0093677 ] mean value: 0.010278058052062989 key: score_time value: [0.01193213 0.01037407 0.00927377 0.00858784 0.00878835 0.00869942 0.00842476 0.00854325 0.00874376 0.00934672] mean value: 0.00927140712738037 key: test_mcc value: [0.07453397 0.27657348 0.10540926 0.43852901 0.1754116 0.17817416 0.39130435 0.35082321 0.17407766 0.39130435] mean value: 0.255614103162624 key: train_mcc value: [0.28206377 0.28219126 0.28775695 0.24292462 0.27335618 0.28775695 0.25475509 0.26781515 0.23515854 0.2831189 ] mean value: 0.26968974020352343 key: test_fscore value: [0.59259259 0.66666667 0.6440678 0.73469388 0.6122449 0.62745098 0.69565217 0.69387755 0.59574468 0.69565217] mean value: 0.6558643391469348 key: train_fscore value: [0.67105263 0.66814159 0.66962306 0.64573991 0.66371681 0.66962306 0.65789474 0.65924276 0.64912281 0.66814159] mean value: 0.6622298967179072 key: test_precision value: [0.51612903 0.62962963 0.52777778 0.69230769 0.57692308 0.57142857 0.69565217 0.65384615 0.58333333 0.69565217] mean value: 0.6142679615330386 key: train_precision value: [0.61693548 0.61632653 0.62139918 0.60504202 0.6147541 0.62139918 0.60483871 0.61410788 0.59677419 0.61885246] mean value: 0.6130429729619683 key: test_recall value: [0.69565217 0.70833333 0.82608696 0.7826087 0.65217391 0.69565217 0.69565217 0.73913043 0.60869565 0.69565217] mean value: 0.709963768115942 key: train_recall value: [0.73557692 0.7294686 0.72596154 0.69230769 0.72115385 0.72596154 0.72115385 0.71153846 0.71153846 0.72596154] mean value: 0.7200622445187663 key: test_accuracy value: [0.53191489 0.63829787 0.54347826 0.7173913 0.58695652 0.58695652 0.69565217 0.67391304 0.58695652 0.69565217] mean value: 0.6257169287696577 key: train_accuracy value: [0.63855422 0.63855422 0.64182692 0.62019231 0.63461538 0.64182692 0.625 0.63221154 0.61538462 0.63942308] mean value: 0.6327589202965708 key: test_roc_auc value: [0.53532609 0.63677536 0.54347826 0.7173913 0.58695652 0.58695652 0.69565217 0.67391304 0.58695652 0.69565217] mean value: 0.6259057971014492 key: train_roc_auc value: [0.63831986 0.63877276 0.64182692 0.62019231 0.63461538 0.64182692 0.625 0.63221154 0.61538462 0.63942308] mean value: 0.6327573392790784 key: test_jcc value: [0.42105263 0.5 0.475 0.58064516 0.44117647 0.45714286 0.53333333 0.53125 0.42424242 0.53333333] mean value: 0.48971762115094525 key: train_jcc value: [0.5049505 0.50166113 0.50333333 0.47682119 0.49668874 0.50333333 0.49019608 0.49169435 0.48051948 0.50166113] mean value: 0.495085926573754 MCC on Blind test: 0.38 MCC on Training: 0.26 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01155019 0.01110792 0.01150346 0.01137638 0.01106381 0.01110816 0.01116586 0.01093745 0.01148701 0.01183105] mean value: 0.011313128471374511 key: score_time value: [0.00970507 0.00886154 0.01009917 0.00963593 0.00975943 0.01029372 0.00968242 0.0088799 0.01041532 0.01019764] mean value: 0.009753012657165527 key: test_mcc value: [0.10526333 0.36612568 0.23186945 0.52223297 0.3927922 0.30550505 0.26311741 0.26111648 0.17407766 0.43519414] mean value: 0.30572943663032703 key: train_mcc value: [0.43142858 0.4024532 0.3847755 0.37527767 0.37091746 0.39512681 0.40414519 0.40504629 0.40392085 0.37991748] mean value: 0.3953009039143046 key: test_fscore value: [0.53333333 0.66666667 0.66666667 0.75555556 0.68181818 0.63636364 0.60465116 0.63829787 0.57777778 0.71111111] mean value: 0.6472241964424053 key: train_fscore value: [0.71359223 0.69756098 0.68780488 0.68137255 0.67493797 0.68656716 0.69607843 0.69 0.69902913 0.68613139] mean value: 0.6913074709574959 key: test_precision value: [0.54545455 0.71428571 0.58064516 0.77272727 0.71428571 0.66666667 0.65 0.625 0.59090909 0.72727273] mean value: 0.6587246892892054 key: train_precision value: [0.72058824 0.7044335 0.6980198 0.695 0.6974359 0.71134021 0.71 0.71875 0.70588235 0.69458128] mean value: 0.705603127216208 key: test_recall value: [0.52173913 0.625 0.7826087 0.73913043 0.65217391 0.60869565 0.56521739 0.65217391 0.56521739 0.69565217] mean value: 0.6407608695652174 key: train_recall value: [0.70673077 0.69082126 0.67788462 0.66826923 0.65384615 0.66346154 0.68269231 0.66346154 0.69230769 0.67788462] mean value: 0.6777359717577109 key: test_accuracy value: [0.55319149 0.68085106 0.60869565 0.76086957 0.69565217 0.65217391 0.63043478 0.63043478 0.58695652 0.7173913 ] mean value: 0.6516651248843663 key: train_accuracy value: [0.71566265 0.70120482 0.69230769 0.6875 0.68509615 0.69711538 0.70192308 0.70192308 0.70192308 0.68990385] mean value: 0.6974559777571825 key: test_roc_auc value: [0.55253623 0.68206522 0.60869565 0.76086957 0.69565217 0.65217391 0.63043478 0.63043478 0.58695652 0.7173913 ] mean value: 0.6517210144927537 key: train_roc_auc value: [0.71568423 0.70117986 0.69230769 0.6875 0.68509615 0.69711538 0.70192308 0.70192308 0.70192308 0.68990385] mean value: 0.6974556391675957 key: test_jcc value: [0.36363636 0.5 0.5 0.60714286 0.51724138 0.46666667 0.43333333 0.46875 0.40625 0.55172414] mean value: 0.48147447380206004 key: train_jcc value: [0.55471698 0.53558052 0.52416357 0.51672862 0.5093633 0.52272727 0.53383459 0.52671756 0.53731343 0.52222222] mean value: 0.5283368066168734 MCC on Blind test: 0.29 MCC on Training: 0.31 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01286244 0.01763821 0.01713729 0.01692939 0.0170083 0.01809692 0.01749086 0.01754141 0.01624703 0.0174067 ] mean value: 0.016835856437683105 key: score_time value: [0.00908089 0.01231837 0.01219511 0.01218557 0.01239538 0.01222777 0.01220202 0.01223159 0.01219249 0.01236916] mean value: 0.01193983554840088 key: test_mcc value: [0.42102089 0.40398551 0.26311741 0.4454354 0.47826087 0.10540926 0.43852901 0.16439899 0.30550505 0.47245559] mean value: 0.349811796937347 key: train_mcc value: [0.52198946 0.50686585 0.53076094 0.49783909 0.52579755 0.35579736 0.47667487 0.35967692 0.49735594 0.45271344] mean value: 0.4725471422947507 key: test_fscore value: [0.73076923 0.70833333 0.65306122 0.74509804 0.73913043 0.6440678 0.73469388 0.66666667 0.66666667 0.76363636] mean value: 0.7052123633721542 key: train_fscore value: [0.78131635 0.76430206 0.77951002 0.77021277 0.77136259 0.72340426 0.76068376 0.72531418 0.76789588 0.75686275] mean value: 0.7600864605275912 key: test_precision value: [0.65517241 0.70833333 0.61538462 0.67857143 0.73913043 0.52777778 0.69230769 0.54054054 0.64 0.65625 ] mean value: 0.64534682364911 key: train_precision value: [0.69961977 0.72608696 0.72614108 0.69083969 0.74222222 0.57303371 0.68461538 0.57879656 0.69960474 0.63907285] mean value: 0.6760032968952002 key: test_recall value: [0.82608696 0.70833333 0.69565217 0.82608696 0.73913043 0.82608696 0.7826087 0.86956522 0.69565217 0.91304348] mean value: 0.7882246376811594 key: train_recall value: [0.88461538 0.80676329 0.84134615 0.87019231 0.80288462 0.98076923 0.85576923 0.97115385 0.85096154 0.92788462] mean value: 0.8792340208101077 key: test_accuracy value: [0.70212766 0.70212766 0.63043478 0.7173913 0.73913043 0.54347826 0.7173913 0.56521739 0.65217391 0.7173913 ] mean value: 0.668686401480111 key: train_accuracy value: [0.75180723 0.75180723 0.76201923 0.74038462 0.76201923 0.625 0.73076923 0.63221154 0.74278846 0.70192308] mean value: 0.7200729842446709 key: test_roc_auc value: [0.70471014 0.70199275 0.63043478 0.7173913 0.73913043 0.54347826 0.7173913 0.56521739 0.65217391 0.7173913 ] mean value: 0.6689311594202898 key: train_roc_auc value: [0.75148644 0.75193933 0.76201923 0.74038462 0.76201923 0.625 0.73076923 0.63221154 0.74278846 0.70192308] mean value: 0.7200541155704199 key: test_jcc value: [0.57575758 0.5483871 0.48484848 0.59375 0.5862069 0.475 0.58064516 0.5 0.5 0.61764706] mean value: 0.546224227404583 key: train_jcc value: [0.64111498 0.61851852 0.63868613 0.62629758 0.62781955 0.56666667 0.6137931 0.56901408 0.62323944 0.60883281] mean value: 0.6133982858023309 MCC on Blind test: 0.39 MCC on Training: 0.35 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03056741 0.02907324 0.02828407 0.02922034 0.02927876 0.02979016 0.02947283 0.02904963 0.02957869 0.02894092] mean value: 0.02932560443878174 key: score_time value: [0.01315689 0.01281881 0.01434398 0.01452518 0.0141933 0.01356363 0.01407814 0.01441836 0.01461315 0.01324058] mean value: 0.013895201683044433 key: test_mcc value: [0.49819858 0.30500678 0.6092718 0.52223297 0.47826087 0.58549055 0.48566186 0.32461723 0.17407766 0.34815531] mean value: 0.43309736072056476 key: train_mcc value: [0.81573738 0.77397275 0.91498553 0.82305489 0.93286482 0.75093926 0.83077247 0.69312845 0.91913004 0.76272475] mean value: 0.8217310350009779 key: test_fscore value: [0.76 0.56410256 0.8 0.76595745 0.73913043 0.75 0.76 0.57894737 0.59574468 0.66666667] mean value: 0.6980549161632467 key: train_fscore value: [0.9103139 0.85635359 0.95544554 0.89361702 0.96601942 0.83798883 0.91743119 0.78717201 0.96 0.84764543] mean value: 0.8931986936313173 key: test_precision value: [0.7037037 0.73333333 0.81818182 0.75 0.73913043 0.88235294 0.7037037 0.73333333 0.58333333 0.68181818] mean value: 0.7328890783366486 key: train_precision value: [0.85294118 1. 0.98469388 1. 0.9754902 1. 0.87719298 1. 0.94009217 1. ] mean value: 0.9630410398454797 key: test_recall value: [0.82608696 0.45833333 0.7826087 0.7826087 0.73913043 0.65217391 0.82608696 0.47826087 0.60869565 0.65217391] mean value: 0.6806159420289856 key: train_recall value: [0.97596154 0.74879227 0.92788462 0.80769231 0.95673077 0.72115385 0.96153846 0.64903846 0.98076923 0.73557692] mean value: 0.8465138424377555 key: test_accuracy value: [0.74468085 0.63829787 0.80434783 0.76086957 0.73913043 0.7826087 0.73913043 0.65217391 0.58695652 0.67391304] mean value: 0.7122109158186863 key: train_accuracy value: [0.90361446 0.8746988 0.95673077 0.90384615 0.96634615 0.86057692 0.91346154 0.82451923 0.95913462 0.86778846] mean value: 0.9030717099165895 key: test_roc_auc value: [0.74637681 0.64221014 0.80434783 0.76086957 0.73913043 0.7826087 0.73913043 0.65217391 0.58695652 0.67391304] mean value: 0.7127717391304349 key: train_roc_auc value: [0.90343971 0.87439614 0.95673077 0.90384615 0.96634615 0.86057692 0.91346154 0.82451923 0.95913462 0.86778846] mean value: 0.9030239687848385 key: test_jcc value: [0.61290323 0.39285714 0.66666667 0.62068966 0.5862069 0.6 0.61290323 0.40740741 0.42424242 0.5 ] mean value: 0.5423876644510682 key: train_jcc value: [0.83539095 0.74879227 0.91469194 0.80769231 0.9342723 0.72115385 0.84745763 0.64903846 0.92307692 0.73557692] mean value: 0.8117143549288011 MCC on Blind test: -0.11 MCC on Training: 0.43 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.68289351 0.719136 0.6727407 0.69165111 0.62179637 0.63767338 0.65230131 0.67333269 0.67517853 0.74898171] mean value: 0.6775685310363769 key: score_time value: [0.13951659 0.1962204 0.16649771 0.16725421 0.158463 0.19371295 0.16719627 0.1650753 0.14852452 0.20645618] mean value: 0.17089171409606935 key: test_mcc value: [0.66801039 0.53734864 0.69631062 0.70164642 0.78334945 0.69631062 0.61394061 0.48566186 0.52623481 0.65465367] mean value: 0.6363467104208496 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.80952381 0.75555556 0.84444444 0.85714286 0.88888889 0.85106383 0.79069767 0.71428571 0.74418605 0.83333333] mean value: 0.808912215389207 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.80952381 0.86363636 0.80769231 0.90909091 0.83333333 0.85 0.78947368 0.8 0.8 ] mean value: 0.8357487249592512 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.70833333 0.82608696 0.91304348 0.86956522 0.86956522 0.73913043 0.65217391 0.69565217 0.86956522] mean value: 0.7882246376811594 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82978723 0.76595745 0.84782609 0.84782609 0.89130435 0.84782609 0.80434783 0.73913043 0.76086957 0.82608696] mean value: 0.8160962072155412 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82789855 0.76721014 0.84782609 0.84782609 0.89130435 0.84782609 0.80434783 0.73913043 0.76086957 0.82608696] mean value: 0.8160326086956522 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68 0.60714286 0.73076923 0.75 0.8 0.74074074 0.65384615 0.55555556 0.59259259 0.71428571] mean value: 0.6824932844932846 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.44 MCC on Training: 0.64 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.0340414 0.97212029 0.93873668 1.01805902 0.96193409 0.97847986 1.00309205 1.09123445 0.98579884 1.00988054] mean value: 0.9993377208709717 key: score_time value: [0.16648579 0.2402668 0.21577573 0.23890972 0.23374438 0.23371816 0.17140198 0.23840928 0.24530482 0.21011662] mean value: 0.21941332817077636 key: test_mcc value: [0.57560058 0.53734864 0.6092718 0.75056834 0.74194083 0.65465367 0.66226618 0.52223297 0.66226618 0.61394061] mean value: 0.6330089788971093 key: train_mcc value: [0.87952899 0.89929832 0.87516184 0.89988125 0.89116534 0.87068551 0.88992718 0.91363049 0.90401333 0.89913199] mean value: 0.8922424233018493 key: test_fscore value: [0.77272727 0.75555556 0.80851064 0.88 0.86363636 0.83333333 0.80952381 0.75555556 0.80952381 0.81632653] mean value: 0.8104692868765817 key: train_fscore value: [0.93975904 0.94840295 0.9368932 0.94840295 0.94292804 0.93398533 0.94376528 0.95631068 0.95145631 0.94915254] mean value: 0.945105632044729 key: test_precision value: [0.80952381 0.80952381 0.79166667 0.81481481 0.9047619 0.8 0.89473684 0.77272727 0.89473684 0.76923077] mean value: 0.8261722731459573 key: train_precision value: [0.94202899 0.965 0.94607843 0.96984925 0.97435897 0.95024876 0.960199 0.96568627 0.96078431 0.95609756] mean value: 0.9590331547874857 key: test_recall value: [0.73913043 0.70833333 0.82608696 0.95652174 0.82608696 0.86956522 0.73913043 0.73913043 0.73913043 0.86956522] mean value: 0.801268115942029 key: train_recall value: [0.9375 0.93236715 0.92788462 0.92788462 0.91346154 0.91826923 0.92788462 0.94711538 0.94230769 0.94230769] mean value: 0.9316982534373837 key: test_accuracy value: [0.78723404 0.76595745 0.80434783 0.86956522 0.86956522 0.82608696 0.82608696 0.76086957 0.82608696 0.80434783] mean value: 0.8140148011100832 key: train_accuracy value: [0.93975904 0.94939759 0.9375 0.94951923 0.94471154 0.93509615 0.94471154 0.95673077 0.95192308 0.94951923] mean value: 0.9458868164967562 key: test_roc_auc value: [0.78623188 0.76721014 0.80434783 0.86956522 0.86956522 0.82608696 0.82608696 0.76086957 0.82608696 0.80434783] mean value: 0.8140398550724639 key: train_roc_auc value: [0.93976449 0.94935665 0.9375 0.94951923 0.94471154 0.93509615 0.94471154 0.95673077 0.95192308 0.94951923] mean value: 0.9458832683017466 key: test_jcc value: [0.62962963 0.60714286 0.67857143 0.78571429 0.76 0.71428571 0.68 0.60714286 0.68 0.68965517] mean value: 0.6832141944900565 key: train_jcc value: [0.88636364 0.90186916 0.88127854 0.90186916 0.89201878 0.87614679 0.89351852 0.91627907 0.90740741 0.90322581] mean value: 0.895997686341196 MCC on Blind test: 0.52 MCC on Training: 0.63 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02822232 0.01497817 0.01640081 0.03713512 0.03734684 0.03726125 0.0372529 0.03754568 0.03726625 0.03730512] mean value: 0.03207144737243652 key: score_time value: [0.01251268 0.01204777 0.02071142 0.02119827 0.02186608 0.02213073 0.02220035 0.02209449 0.02232742 0.02122402] mean value: 0.019831323623657228 key: test_mcc value: [0.48913043 0.23315467 0.34815531 0.52623481 0.47826087 0.35082321 0.52223297 0.3927922 0.43852901 0.40533961] mean value: 0.41846530947718386 key: train_mcc value: [0.64347444 0.63062748 0.62140718 0.6447078 0.5828703 0.63487958 0.64940632 0.62526019 0.64940632 0.64449897] mean value: 0.6326538585760192 key: test_fscore value: [0.73913043 0.64 0.68085106 0.7755102 0.73913043 0.69387755 0.76595745 0.70833333 0.69767442 0.73076923] mean value: 0.7171234118012771 key: train_fscore value: [0.82380952 0.82051282 0.81585082 0.8254717 0.7972028 0.81990521 0.82742317 0.81516588 0.82742317 0.82464455] mean value: 0.819740963099699 key: test_precision value: [0.73913043 0.61538462 0.66666667 0.73076923 0.73913043 0.65384615 0.75 0.68 0.75 0.65517241] mean value: 0.6980099950024987 key: train_precision value: [0.81603774 0.79279279 0.7918552 0.81018519 0.77375566 0.80841121 0.81395349 0.80373832 0.81395349 0.81308411] mean value: 0.8037767195159541 key: test_recall value: [0.73913043 0.66666667 0.69565217 0.82608696 0.73913043 0.73913043 0.7826087 0.73913043 0.65217391 0.82608696] mean value: 0.7405797101449276 key: train_recall value: [0.83173077 0.85024155 0.84134615 0.84134615 0.82211538 0.83173077 0.84134615 0.82692308 0.84134615 0.83653846] mean value: 0.8364664622816796 key: test_accuracy value: [0.74468085 0.61702128 0.67391304 0.76086957 0.73913043 0.67391304 0.76086957 0.69565217 0.7173913 0.69565217] mean value: 0.7079093432007401 key: train_accuracy value: [0.82168675 0.81445783 0.81009615 0.82211538 0.79086538 0.81730769 0.82451923 0.8125 0.82451923 0.82211538] mean value: 0.8160183039851715 key: test_roc_auc value: [0.74456522 0.61594203 0.67391304 0.76086957 0.73913043 0.67391304 0.76086957 0.69565217 0.7173913 0.69565217] mean value: 0.7077898550724637 key: train_roc_auc value: [0.82166249 0.81454385 0.81009615 0.82211538 0.79086538 0.81730769 0.82451923 0.8125 0.82451923 0.82211538] mean value: 0.8160244797473059 key: test_jcc value: [0.5862069 0.47058824 0.51612903 0.63333333 0.5862069 0.53125 0.62068966 0.5483871 0.53571429 0.57575758] mean value: 0.5604263007407433 key: train_jcc value: [0.70040486 0.69565217 0.68897638 0.70281124 0.6627907 0.69477912 0.70564516 0.688 0.70564516 0.7016129 ] mean value: 0.6946317695092048 MCC on Blind test: 0.3 MCC on Training: 0.42 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.06112051 0.0489552 0.08500767 0.14898086 0.14412618 0.11261106 0.08682156 0.05058575 0.08333468 0.11453366] mean value: 0.09360771179199219 key: score_time value: [0.0120194 0.01194 0.02297926 0.02304339 0.01881552 0.02290821 0.01193595 0.01202059 0.01544499 0.03339195] mean value: 0.018449926376342775 key: test_mcc value: [0.44746377 0.27586252 0.35634832 0.52623481 0.52223297 0.21821789 0.52223297 0.30550505 0.3927922 0.54772256] mean value: 0.4114613057764019 key: train_mcc value: [0.56748557 0.52474634 0.54208216 0.56396841 0.52727192 0.60180368 0.54555007 0.55934964 0.53544521 0.50692705] mean value: 0.5474630063434027 key: test_fscore value: [0.72340426 0.65306122 0.70588235 0.7755102 0.76595745 0.625 0.76595745 0.66666667 0.68181818 0.79245283] mean value: 0.7155710609122303 key: train_fscore value: [0.79069767 0.77030162 0.78181818 0.78886311 0.77448747 0.80562061 0.7816092 0.78703704 0.77598152 0.76321839] mean value: 0.781963481733429 key: test_precision value: [0.70833333 0.64 0.64285714 0.73076923 0.75 0.6 0.75 0.64 0.71428571 0.7 ] mean value: 0.6876245421245422 key: train_precision value: [0.76576577 0.74107143 0.74137931 0.76233184 0.73593074 0.78538813 0.74889868 0.75892857 0.74666667 0.73127753] mean value: 0.7517638656580644 key: test_recall value: [0.73913043 0.66666667 0.7826087 0.82608696 0.7826087 0.65217391 0.7826087 0.69565217 0.65217391 0.91304348] mean value: 0.7492753623188406 key: train_recall value: [0.81730769 0.80193237 0.82692308 0.81730769 0.81730769 0.82692308 0.81730769 0.81730769 0.80769231 0.79807692] mean value: 0.8148086213303604 key: test_accuracy value: [0.72340426 0.63829787 0.67391304 0.76086957 0.76086957 0.60869565 0.76086957 0.65217391 0.69565217 0.76086957] mean value: 0.7035615171137836 key: train_accuracy value: [0.78313253 0.76144578 0.76923077 0.78125 0.76201923 0.80048077 0.77163462 0.77884615 0.76682692 0.75240385] mean value: 0.772727062094532 key: test_roc_auc value: [0.72373188 0.63768116 0.67391304 0.76086957 0.76086957 0.60869565 0.76086957 0.65217391 0.69565217 0.76086957] mean value: 0.7035326086956523 key: train_roc_auc value: [0.78304998 0.76154311 0.76923077 0.78125 0.76201923 0.80048077 0.77163462 0.77884615 0.76682692 0.75240385] mean value: 0.7727285395763656 key: test_jcc value: [0.56666667 0.48484848 0.54545455 0.63333333 0.62068966 0.45454545 0.62068966 0.5 0.51724138 0.65625 ] mean value: 0.5599719174503658 key: train_jcc value: [0.65384615 0.62641509 0.64179104 0.651341 0.63197026 0.6745098 0.64150943 0.64885496 0.63396226 0.61710037] mean value: 0.6421300384967574 MCC on Blind test: 0.3 MCC on Training: 0.41 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03213906 0.02345657 0.02067542 0.02113748 0.01998973 0.02300024 0.02047729 0.01922393 0.02304411 0.02091765] mean value: 0.02240614891052246 key: score_time value: [0.01250052 0.01281047 0.01325512 0.0132072 0.01325393 0.01336145 0.01214194 0.01248932 0.013165 0.01241612] mean value: 0.012860107421875 key: test_mcc value: [0.40398551 0.36231884 0.26726124 0.56736651 0.56736651 0.1754116 0.47826087 0.39735971 0.43519414 0.4454354 ] mean value: 0.40999603425884273 key: train_mcc value: [0.5796756 0.60626227 0.60984244 0.53240213 0.55833796 0.61092298 0.57759098 0.60805033 0.54932264 0.57403585] mean value: 0.5806443173287017 key: test_fscore value: [0.69565217 0.68085106 0.66666667 0.79166667 0.79166667 0.6122449 0.73913043 0.72 0.71111111 0.74509804] mean value: 0.7154087720811421 key: train_fscore value: [0.8 0.80841121 0.81363636 0.77727273 0.79101124 0.80851064 0.79342723 0.81105991 0.78139535 0.79445727] mean value: 0.7979181941660339 key: test_precision value: [0.69565217 0.69565217 0.60714286 0.76 0.76 0.57692308 0.73913043 0.66666667 0.72727273 0.67857143] mean value: 0.6907011539185453 key: train_precision value: [0.75862069 0.78280543 0.77155172 0.73706897 0.74261603 0.79534884 0.77522936 0.77876106 0.75675676 0.76444444] mean value: 0.7663203301085444 key: test_recall value: [0.69565217 0.66666667 0.73913043 0.82608696 0.82608696 0.65217391 0.73913043 0.7826087 0.69565217 0.82608696] mean value: 0.7449275362318841 key: train_recall value: [0.84615385 0.83574879 0.86057692 0.82211538 0.84615385 0.82211538 0.8125 0.84615385 0.80769231 0.82692308] mean value: 0.8326133407655147 key: test_accuracy value: [0.70212766 0.68085106 0.63043478 0.7826087 0.7826087 0.58695652 0.73913043 0.69565217 0.7173913 0.7173913 ] mean value: 0.7035152636447733 key: train_accuracy value: [0.78795181 0.80240964 0.80288462 0.76442308 0.77644231 0.80528846 0.78846154 0.80288462 0.77403846 0.78605769] mean value: 0.7890842215013902 key: test_roc_auc value: [0.70199275 0.68115942 0.63043478 0.7826087 0.7826087 0.58695652 0.73913043 0.69565217 0.7173913 0.7173913 ] mean value: 0.7035326086956522 key: train_roc_auc value: [0.78781122 0.80248978 0.80288462 0.76442308 0.77644231 0.80528846 0.78846154 0.80288462 0.77403846 0.78605769] mean value: 0.7890781772575252 key: test_jcc value: [0.53333333 0.51612903 0.5 0.65517241 0.65517241 0.44117647 0.5862069 0.5625 0.55172414 0.59375 ] mean value: 0.5595164698248599 key: train_jcc value: [0.66666667 0.67843137 0.68582375 0.63568773 0.65427509 0.67857143 0.65758755 0.68217054 0.64122137 0.65900383] mean value: 0.6639439344592415 MCC on Blind test: 0.26 MCC on Training: 0.41 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=165)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01467323 0.01725912 0.01969218 0.02595568 0.01968932 0.02180696 0.0239141 0.02340984 0.02064133 0.02374315] mean value: 0.0210784912109375 key: score_time value: [0.00891995 0.01117373 0.01170349 0.01219726 0.01215696 0.01211619 0.01209903 0.01205301 0.0120697 0.0121882 ] mean value: 0.011667752265930175 key: test_mcc value: [0.09261546 0.32305945 0.43852901 0.15430335 0.38359764 0.14484136 0.25819889 0.16439899 0.30905755 0.30550505] mean value: 0.25741067524428507 key: train_mcc value: [0.35718162 0.4101146 0.59151708 0.35914065 0.39085624 0.55779886 0.19984506 0.43945371 0.54067627 0.56568634] mean value: 0.44122704199063695 key: test_fscore value: [0.15384615 0.72131148 0.69767442 0.22222222 0.73333333 0.44444444 0.6984127 0.375 0.68 0.63636364] mean value: 0.5362608382636976 key: train_fscore value: [0.39694656 0.74144487 0.79809976 0.38610039 0.73584906 0.70588235 0.68552413 0.51929825 0.78902954 0.7204611 ] mean value: 0.6478635992957078 key: test_precision value: [0.66666667 0.59459459 0.75 0.75 0.59459459 0.61538462 0.55 0.66666667 0.62962963 0.66666667] mean value: 0.6484203434203435 key: train_precision value: [0.96296296 0.61128527 0.78873239 0.98039216 0.60559006 0.90909091 0.52417303 0.96103896 0.70300752 0.89928058] mean value: 0.7945553835217639 key: test_recall value: [0.08695652 0.91666667 0.65217391 0.13043478 0.95652174 0.34782609 0.95652174 0.26086957 0.73913043 0.60869565] mean value: 0.5655797101449276 key: train_recall value: [0.25 0.94202899 0.80769231 0.24038462 0.9375 0.57692308 0.99038462 0.35576923 0.89903846 0.60096154] mean value: 0.6600682831661093 key: test_accuracy value: [0.53191489 0.63829787 0.7173913 0.54347826 0.65217391 0.56521739 0.58695652 0.56521739 0.65217391 0.65217391] mean value: 0.6104995374653099 key: train_accuracy value: [0.61927711 0.67228916 0.79567308 0.61778846 0.66346154 0.75961538 0.54567308 0.67067308 0.75961538 0.76682692] mean value: 0.6870893188137164 key: test_roc_auc value: [0.52264493 0.63224638 0.7173913 0.54347826 0.65217391 0.56521739 0.58695652 0.56521739 0.65217391 0.65217391] mean value: 0.608967391304348 key: train_roc_auc value: [0.62016908 0.67293757 0.79567308 0.61778846 0.66346154 0.75961538 0.54567308 0.67067308 0.75961538 0.76682692] mean value: 0.6872433574879228 key: test_jcc value: [0.08333333 0.56410256 0.53571429 0.125 0.57894737 0.28571429 0.53658537 0.23076923 0.51515152 0.46666667] mean value: 0.3921984615726593 key: train_jcc value: [0.24761905 0.58912387 0.66403162 0.23923445 0.58208955 0.54545455 0.52151899 0.3507109 0.65156794 0.56306306] mean value: 0.495441397782565 MCC on Blind test: 0.23 MCC on Training: 0.26 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.12016535 0.10839534 0.10469222 0.10019898 0.10389256 0.10266519 0.11050916 0.09968424 0.11547613 0.10384154] mean value: 0.10695207118988037 key: score_time value: [0.01210022 0.0123353 0.01216435 0.01156831 0.01200652 0.01223159 0.01221561 0.01188469 0.01136732 0.01138353] mean value: 0.011925745010375976 key: test_mcc value: [0.57560058 0.49183384 0.69631062 0.74194083 0.78935222 0.56521739 0.53452248 0.62360956 0.53452248 0.82608696] mean value: 0.6378996968161124 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77272727 0.73913043 0.85106383 0.875 0.88372093 0.7826087 0.73170732 0.7804878 0.73170732 0.91304348] mean value: 0.8061197080467106 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.80952381 0.77272727 0.83333333 0.84 0.95 0.7826087 0.83333333 0.88888889 0.83333333 0.91304348] mean value: 0.8456792145053015 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.73913043 0.70833333 0.86956522 0.91304348 0.82608696 0.7826087 0.65217391 0.69565217 0.65217391 0.91304348] mean value: 0.7751811594202899 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78723404 0.74468085 0.84782609 0.86956522 0.89130435 0.7826087 0.76086957 0.80434783 0.76086957 0.91304348] mean value: 0.8162349676225717 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78623188 0.74547101 0.84782609 0.86956522 0.89130435 0.7826087 0.76086957 0.80434783 0.76086957 0.91304348] mean value: 0.816213768115942 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.62962963 0.5862069 0.74074074 0.77777778 0.79166667 0.64285714 0.57692308 0.64 0.57692308 0.84 ] mean value: 0.6802725008069835 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.64 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['Accuracy', 'ROC_AUC', 'source_data', 'Precision', 'F1', 'JCC', 'Recall', 'MCC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Building estimator 3 of 9 for this parallel run (total 100)... 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Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... `h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~p`h~tFUp+x~+x~+x~h hKqBuilding estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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