/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: [0.06783319 0.06749201 0.06719685 0.06756043 0.06702876 0.06712174 0.06725192 0.06728387 0.06741643 0.06739259] mean value: 0.06735777854919434 key: score_time value: [0.01468825 0.01459002 0.01462436 0.01456857 0.01446748 0.01512146 0.01455379 0.01437521 0.01465225 0.01447845] mean value: 0.01461198329925537 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. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (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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Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 9 for this parallel run (total 100)... 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 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 1 of 8 for this parallel run (total 100)... 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 5 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 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 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 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 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 8 for this parallel run (total 100)... 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 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 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 9 for this parallel run (total 100)... 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 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 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 3 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 9 for this parallel run (total 100)... 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 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 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 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 2 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 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 5 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 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 6 of 9 for this parallel run (total 100)... Building estimator 5 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: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s 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 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: 0.2s 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 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 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 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)]: Using backend ThreadingBackend with 12 concurrent workers. [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)]: 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 Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... 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 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 9 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 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 4 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 8 for this parallel run (total 100)... 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 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 4 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 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 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)... [Parallel(n_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)]: 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)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 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 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 1 of 8 for this parallel run (total 100)... 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[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 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 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: 0.2s 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)]: 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 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: [0.33801174 0.1162312 0.12495995 0.15188313 0.11752677 0.11360383 0.09869051 0.1314795 0.12863684 0.14009118] mean value: 0.14611146450042725 key: score_time value: [0.07473135 0.07370019 0.05618 0.05608582 0.04175258 0.07161951 0.04995871 0.06391716 0.0629313 0.05745983] mean value: 0.06083364486694336 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: [0.02262235 0.01043868 0.01007891 0.00932217 0.00924635 0.00878453 0.00917578 0.00993061 0.00921011 0.00897956] mean value: 0.01077890396118164 key: score_time value: [0.00955677 0.0096209 0.00956392 0.00855827 0.00834942 0.00865555 0.00930476 0.00844455 0.00916529 0.00850511] mean value: 0.008972454071044921 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: [0.0095408 0.00976348 0.00828624 0.00929856 0.00957036 0.00922179 0.00847864 0.0090251 0.00884175 0.0087657 ] mean value: 0.009079241752624511 key: score_time value: [0.00931311 0.00936508 0.00882268 0.00957179 0.00949097 0.00839734 0.00880003 0.00881672 0.00898719 0.00926876] mean value: 0.009083366394042969 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: [0.07736874 0.07587075 0.07727671 0.07741952 0.07707381 0.07746553 0.07830501 0.0808382 0.07705951 0.0806613 ] mean value: 0.0779339075088501 key: score_time value: [0.01677871 0.01699662 0.01756477 0.01691628 0.0181694 0.01817966 0.01690698 0.01796007 0.01764846 0.01745462] mean value: 0.01745755672454834 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: [0.13188934 0.12295365 0.11698937 0.1254096 0.12188792 0.1071713 0.12261558 0.1298039 0.13340831 0.11206651] mean value: 0.12241954803466797 key: score_time value: [0.00997305 0.00993562 0.00953174 0.00948119 0.00890565 0.00924945 0.01009345 0.00936818 0.00927591 0.00894117] mean value: 0.00947554111480713 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: [0.00851822 0.00807571 0.00810242 0.00794816 0.00804257 0.00805759 0.0081172 0.00809193 0.00792861 0.00833821] mean value: 0.008122062683105469 key: score_time value: [0.0083971 0.00834107 0.00834846 0.00837612 0.00838208 0.0083952 0.00828958 0.00836086 0.00835633 0.00842142] mean value: 0.008366823196411133 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: [0.01431918 0.00979662 0.01003432 0.00989723 0.01114225 0.01084018 0.0105567 0.01138735 0.01066327 0.01148033] mean value: 0.011011743545532226 key: score_time value: [0.0105226 0.00871873 0.00889897 0.00847268 0.00946379 0.00971556 0.00955772 0.00972652 0.00884008 0.00960922] mean value: 0.009352588653564453 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: [0.00786042 0.01122022 0.00837564 0.00769067 0.00771523 0.00770259 0.00853324 0.00769567 0.00769591 0.00785661] mean value: 0.008234620094299316 key: score_time value: [0.04532456 0.0244391 0.00903034 0.00913548 0.00892591 0.01360607 0.0107758 0.0088439 0.0089047 0.00878835] mean value: 0.014777421951293945 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: [0.01125813 0.01287079 0.0132699 0.01359797 0.02441359 0.01546049 0.01397657 0.01555419 0.02130198 0.01370549] mean value: 0.01554090976715088 key: score_time value: [0.01102233 0.01101065 0.01145315 0.01126719 0.01160836 0.01171756 0.01192927 0.01170683 0.01162624 0.0114224 ] mean value: 0.011476397514343262 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: [0.02254915 0.01716089 0.01368785 0.01643443 0.01762676 0.01714158 0.01446795 0.01608992 0.01492 0.01535702] mean value: 0.01654355525970459 key: score_time value: [0.011446 0.00895071 0.00907683 0.00912094 0.00917935 0.0093472 0.00887179 0.00914478 0.0087173 0.00850916] mean value: 0.009236407279968262 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: [0.16370106 0.16942191 0.15406322 0.17140174 0.17334843 0.16096044 0.17889285 0.18087649 0.17605615 0.18340373] mean value: 0.17121260166168212 key: score_time value: [0.00998878 0.00968575 0.00946856 0.0090487 0.00928116 0.00910926 0.0089922 0.00937796 0.0093286 0.00960755] mean value: 0.0093888521194458 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: [0.42803121 0.32382941 0.36591315 0.41544604 0.28536773 0.44046259 0.35071921 0.25666571 0.38323689 0.473207 ] mean value: 0.3722878932952881 key: score_time value: [0.0120132 0.01182461 0.01206875 0.0118978 0.01172709 0.01191211 0.01193714 0.01184559 0.01392198 0.01191545] mean value: 0.012106370925903321 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: [0.01151156 0.01146364 0.00860405 0.00855684 0.00811434 0.00844884 0.00846481 0.00822067 0.00822854 0.0082438 ] mean value: 0.00898571014404297 key: score_time value: [0.01145649 0.0117712 0.00868416 0.00829554 0.00837445 0.00831199 0.00846839 0.0083921 0.00859284 0.00819755] mean value: 0.009054470062255859 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: [0.00851035 0.00897813 0.00825548 0.00862885 0.00820041 0.00846744 0.00815582 0.00831628 0.00837684 0.00813842] mean value: 0.008402800559997559 key: score_time value: [0.00844598 0.0085485 0.00846505 0.00835204 0.00858307 0.00835848 0.00881934 0.00842476 0.00843501 0.00827336] mean value: 0.008470559120178222 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: [0.01073408 0.01036596 0.00996065 0.01032019 0.01004267 0.01050591 0.01174164 0.00820446 0.0084703 0.0082078 ] mean value: 0.009855365753173828 key: score_time value: [0.00928855 0.00973248 0.01046205 0.00961065 0.00970221 0.00994372 0.00830007 0.00833607 0.00825286 0.00831652] mean value: 0.009194517135620117 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: [0.00982022 0.0089469 0.00972486 0.00881529 0.00842929 0.00917721 0.00929332 0.00873256 0.00961733 0.00884748] mean value: 0.009140443801879884 key: score_time value: [0.0091517 0.00970984 0.00981545 0.00939012 0.00857449 0.00830364 0.00898528 0.00835466 0.00848341 0.00840712] mean value: 0.008917570114135742 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: [0.54139686 0.56162095 0.59128451 0.53191924 0.55515862 0.55220151 0.58865476 0.56306243 0.54508495 0.65264559] mean value: 0.5683029413223266 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)) [0.14018679 0.18660378 0.16074944 0.12714553 0.13100195 0.15878963 0.10892344 0.14203811 0.12527323 0.15291429] mean value: 0.1433626174926758 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: [0.82077956 0.86412668 0.82393479 0.85645032 0.82342315 0.85749602 0.87960815 0.85137367 0.86805773 0.8365984 ] mean value: 0.8481848478317261 key: score_time value: [0.1831224 0.1837678 0.18144393 0.21479392 0.18798923 0.14913464 0.18382502 0.20368242 0.17561436 0.2173512 ] mean value: 0.18807249069213866 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: [0.02242637 0.00887513 0.00930929 0.00864196 0.00999069 0.00892949 0.00903988 0.00942945 0.00865984 0.00961828] mean value: 0.010492038726806641 key: score_time value: [0.02070379 0.00826454 0.00920129 0.0107677 0.00901461 0.00880051 0.00831652 0.00908804 0.00824785 0.0089829 ] mean value: 0.010138773918151855 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: [0.02685428 0.02728009 0.0258832 0.02600622 0.02606964 0.02597809 0.02666664 0.02640772 0.02730489 0.02763128] mean value: 0.026608204841613768 key: score_time value: [0.00927496 0.00907326 0.00940371 0.00936985 0.00923967 0.00927377 0.00926185 0.00888109 0.00936222 0.00940442] mean value: 0.00925447940826416 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: [0.01024556 0.00896025 0.00895429 0.00989628 0.00901198 0.00885582 0.00902534 0.00874472 0.00950718 0.00901079] mean value: 0.009221220016479492 key: score_time value: [0.00949359 0.0089159 0.00953913 0.00939822 0.00879288 0.00892472 0.00897861 0.00856781 0.00927472 0.00903463] mean value: 0.009092020988464355 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:427: 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:454: 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: [0.00863791 0.00830722 0.00813651 0.00829339 0.00823212 0.00840449 0.0086019 0.00838947 0.00839806 0.00823975] mean value: 0.008364081382751465 key: score_time value: [0.00840497 0.00825453 0.00825953 0.00823116 0.0082612 0.00835109 0.00835919 0.00820303 0.0082593 0.00819135] mean value: 0.008277535438537598 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: [0.08621764 0.03232837 0.03493404 0.05668974 0.03353143 0.06770754 0.10060072 0.03396583 0.03277302 0.03329682] mean value: 0.051204514503479 key: score_time value: [0.01044512 0.01086426 0.01261115 0.00993538 0.01037407 0.01312995 0.01070309 0.01091051 0.01024461 0.01019049] mean value: 0.010940861701965333 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: 15 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 23 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: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall'] 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: 0.1s 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: 0.1s [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: 0.0s [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: 0.1s 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 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (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: 0.1s 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: 0.0s [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: 0.1s [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 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 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 7 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 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 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 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 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 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 3 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 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 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 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 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 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... ? 0{%0{%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 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 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 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 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 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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: 0.0s 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. 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 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 5 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 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 6 of 8 for this parallel run (total 100)... 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 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 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 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 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 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 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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 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 6 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 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 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 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 8 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 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 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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 5 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 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 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 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 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 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 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 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 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 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 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 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 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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: 0.0s remaining: 0.1s 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 4 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... ?E??6l? ?tE?"?"? ?J?UU5?UU?;?OD?ى?OD?] ?^Z??] ?U?> є=E>b<0??\?!?l?>>Rk?\G?rx?.:=J'?]tQ?o?= >O#,?>>=ӰQ??T?I?Ez>(?@>ï>?@??@?????]x ?B?L(?;?>"?D-?0=?9&?{:?H7?L?iE?i?#0?]6?:?Ֆ+??yh?5i?TP?;7?[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 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: [0.07132602 0.06682777 0.06677747 0.06710386 0.06674194 0.06700015 0.06825066 0.06934452 0.06717873 0.06770229] mean value: 0.0678253412246704 key: score_time value: [0.01529694 0.01437569 0.0144577 0.01442981 0.01447797 0.01481962 0.01443458 0.01433897 0.0144999 0.01434636] mean value: 0.01454775333404541 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: [0.12906003 0.10704207 0.12686491 0.12080836 0.10430932 0.10011482 0.12024331 0.12664056 0.12828541 0.11073399] mean value: 0.1174102783203125 key: score_time value: [0.03597808 0.05087709 0.05140519 0.05607057 0.03567457 0.07094002 0.04233408 0.07256484 0.06659627 0.04438996] mean value: 0.05268306732177734 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (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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[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 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)]: 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 [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: [0.00995827 0.01006293 0.00977159 0.00960875 0.00932837 0.01003432 0.01015401 0.01002192 0.01014709 0.01005244] mean value: 0.009913969039916991 key: score_time value: [0.00937915 0.0091207 0.00905037 0.00908828 0.00868917 0.00928426 0.00929546 0.0092063 0.00931859 0.00913453] mean value: 0.0091566801071167 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: [0.00974703 0.00838304 0.00848198 0.00882292 0.00814939 0.00913811 0.00892091 0.00848198 0.008219 0.00814247] mean value: 0.008648681640625 key: score_time value: [0.00833654 0.00855279 0.00859737 0.00846529 0.00859642 0.00892782 0.00907707 0.00840664 0.00827885 0.00861287] mean value: 0.008585166931152344 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: [0.076051 0.08050394 0.07719064 0.08136129 0.07856488 0.08007026 0.08113217 0.08063841 0.08339477 0.08117771] mean value: 0.08000850677490234 key: score_time value: [0.01741648 0.01722956 0.01732183 0.01721096 0.01760364 0.01814914 0.01826596 0.01826811 0.01760626 0.01790762] mean value: 0.017697954177856447 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: [0.13448787 0.12742686 0.11794376 0.1357913 0.12634826 0.11400104 0.12737584 0.12645459 0.13348007 0.11710906] mean value: 0.12604186534881592 key: score_time value: [0.00984979 0.00985265 0.01020741 0.0097754 0.00925779 0.00990605 0.0094161 0.00989556 0.00962567 0.00884724] mean value: 0.00966336727142334 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: [0.00802517 0.00784039 0.00806189 0.00785351 0.00780725 0.00784564 0.00790715 0.00786304 0.00797224 0.00807905] mean value: 0.00792553424835205 key: score_time value: [0.00857687 0.00819349 0.00826335 0.00834155 0.00818014 0.00822473 0.00831079 0.00825834 0.00824285 0.00824022] mean value: 0.008283233642578125 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: [0.00984263 0.00996685 0.01091576 0.01114798 0.00994277 0.00994539 0.00990534 0.01119637 0.01126266 0.01121879] mean value: 0.010534453392028808 key: score_time value: [0.00853729 0.00851989 0.00944638 0.00902963 0.00861883 0.00865293 0.00894928 0.00960135 0.00959802 0.00960469] mean value: 0.009055829048156739 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: [0.00772309 0.00805616 0.00851703 0.00791001 0.00859571 0.00777578 0.00866675 0.00818062 0.00799799 0.00829411] mean value: 0.008171725273132324 key: score_time value: [0.01391387 0.00988483 0.00986409 0.00877953 0.00973701 0.00899768 0.00963449 0.00877929 0.0090239 0.009588 ] mean value: 0.009820270538330077 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: [0.01078725 0.01337886 0.01345515 0.01345873 0.01352358 0.0134542 0.01386166 0.01371312 0.01357365 0.01348424] mean value: 0.01326904296875 key: score_time value: [0.01115131 0.01128483 0.01133561 0.01127672 0.0112915 0.01131248 0.01135087 0.01136351 0.01129174 0.01131058] mean value: 0.011296916007995605 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: [0.02276278 0.01777101 0.01533675 0.01758695 0.01738787 0.01787591 0.01559329 0.01682639 0.01681209 0.01718521] mean value: 0.01751382350921631 key: score_time value: [0.01165128 0.00993705 0.00950909 0.0094161 0.00942922 0.00944781 0.00945854 0.00943589 0.00949621 0.00946188] mean value: 0.009724307060241699 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: [0.14756513 0.15518308 0.14987803 0.15751052 0.1684587 0.1498332 0.16689873 0.1696198 0.16432261 0.17528272] mean value: 0.16045525074005126 key: score_time value: [0.00851631 0.00870752 0.00872874 0.00852203 0.00867891 0.00866747 0.00855327 0.00857377 0.00856733 0.00858259] mean value: 0.008609795570373535 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: [0.26154828 0.32903314 0.21032548 0.23672295 0.2415185 0.25381923 0.26724911 0.24981642 0.23853779 0.35887623] mean value: 0.2647447109222412 key: score_time value: [0.01267409 0.01199841 0.01201987 0.01393771 0.01174808 0.01212859 0.01208258 0.01208258 0.01205397 0.01210666] mean value: 0.012283253669738769 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: [0.01118302 0.01106453 0.00836563 0.00830483 0.00809264 0.00797749 0.00792384 0.00803685 0.00835824 0.00805259] mean value: 0.008735966682434083 key: score_time value: [0.01111627 0.01106596 0.00859427 0.00837588 0.00841665 0.00832319 0.00818777 0.00810027 0.00839496 0.00814033] mean value: 0.00887155532836914 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: [0.00921559 0.00892997 0.00834703 0.00904179 0.00921392 0.00909472 0.00917387 0.00914264 0.00951838 0.00931478] mean value: 0.009099268913269043 key: score_time value: [0.00903344 0.00910711 0.00899053 0.00905228 0.00956821 0.00909376 0.00895715 0.00923181 0.00912786 0.0089848 ] mean value: 0.009114694595336915 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: [0.00911546 0.00983477 0.00927544 0.00967193 0.00957656 0.00978923 0.00937676 0.00916934 0.00889277 0.00861812] mean value: 0.009332036972045899 key: score_time value: [0.00904799 0.00918436 0.00818276 0.0091207 0.00896502 0.0089519 0.00935125 0.00835657 0.00825596 0.00836968] mean value: 0.008778619766235351 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: [0.0092628 0.00901246 0.00931168 0.00920653 0.00939894 0.00953746 0.00833154 0.0083909 0.00859189 0.00833082] mean value: 0.008937501907348632 key: score_time value: [0.00874949 0.00910711 0.00965714 0.00918436 0.00914836 0.008636 0.00839758 0.00832081 0.00838661 0.00837803] mean value: 0.00879654884338379 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: [0.55358386 0.62967014 0.5608449 0.59313846 0.55381012 0.58645582 0.52134705 0.54654431 0.60201859 0.5584178 ] mean value: 0.5705831050872803 key: score_time value: [0.13278913 0.12353516 0.16475773 0.17462468 0.14252162 0.18101025 0.19316149 0.14391518 0.23816967 0.18874288] mean value: 0.16832277774810792 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: [0.90452671 0.8731966 0.91812658 0.84927154 0.94887924 0.84053969 0.85708642 0.87757754 0.82549334 0.93582559] mean value: 0.8830523252487182 key: score_time value: [0.16090035 0.16618824 0.19430923 0.20362377 0.14965487 0.22289467 0.19717145 0.18432331 0.18372726 0.18409944] mean value: 0.18468925952911378 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: [0.01039004 0.00990462 0.00964117 0.00976992 0.00990582 0.00934601 0.01006198 0.01024485 0.01005363 0.01038742] mean value: 0.009970545768737793 key: score_time value: [0.00966334 0.00948763 0.00927234 0.0087626 0.00877118 0.00932288 0.00956345 0.00921845 0.00949717 0.00895643] mean value: 0.009251546859741212 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: [0.02538943 0.02745485 0.02640367 0.0266459 0.02600169 0.0269928 0.02637315 0.02653122 0.02626705 0.02588439] mean value: 0.026394414901733398 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)) [0.00916243 0.00952506 0.00921535 0.00920939 0.0092051 0.00893593 0.00936699 0.00993633 0.0092423 0.00926065] mean value: 0.009305953979492188 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: [0.00931573 0.00952458 0.00878596 0.00907946 0.00951433 0.00869846 0.00847411 0.00910711 0.0096128 0.00942516] mean value: 0.00915377140045166 key: score_time value: [0.00917435 0.00869799 0.00853705 0.00877333 0.00908065 0.00860214 0.00900173 0.00927615 0.00915623 0.00883126] mean value: 0.008913087844848632 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: [0.008991 0.00889349 0.00859237 0.00896955 0.00868869 0.00950837 0.00857234 0.00952172 0.00874186 0.00952697] mean value: 0.009000635147094727 key: score_time value: [0.00860119 0.00874877 0.00874877 0.00896454 0.00894547 0.00921011 0.00875974 0.00933814 0.00897598 0.00920892] mean value: 0.008950161933898925 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/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:427: 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:454: 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: [0.0475421 0.03474045 0.03474998 0.03431797 0.03458786 0.03289366 0.03387642 0.03446102 0.03263259 0.03263879] mean value: 0.035244083404541014 key: score_time value: [0.01011229 0.0104084 0.01022196 0.01030111 0.01056361 0.01001573 0.00998259 0.01012969 0.01034617 0.01025987] mean value: 0.01023414134979248 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: 15 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 23 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: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall'] 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 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (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 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 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 2 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 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 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 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 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 3 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 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 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 6 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 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 4 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 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 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 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (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 Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 3 of 9 for this parallel run (total 100)... 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 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 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 4 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 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 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 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 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 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 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 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 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 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 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (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: 0.0s remaining: 0.0s Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 7 of 9 for this parallel run (total 100)... 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 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 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 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 3 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 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 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 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 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 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 6 of 8 for this parallel run (total 100)... 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 8 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 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 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 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... 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 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 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)... [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: 0.0s 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: 0.1s 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: 0.0s [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: 0.1s 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: 0.1s 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: 0.1s [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: 0.1s [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: 0.1s [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 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 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 3 of 8 for this parallel run (total 100)... P3@6@ @4@*@ @@@?@@&@@0 $x(AAx(Px(D@!x(x( $ x(`x(4dx(Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 3 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... 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 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 9 for this parallel run (total 100)... 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 6 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 9 for this parallel run (total 100)... 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 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 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 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 4 of 8 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: 0.5s [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 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 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: 0.3s 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 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 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: 0.5s [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 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 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)]: 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)]: 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)]: 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 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)]: 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 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 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)]: 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 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 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. [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 ('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: [0.0706737 0.06998253 0.07171822 0.07156992 0.06753039 0.07276559 0.07164121 0.07182813 0.06849694 0.07091856] mean value: 0.0707125186920166 key: score_time value: [0.01465726 0.01629305 0.01603532 0.01576757 0.01595688 0.01618648 0.01597619 0.01579046 0.01553869 0.01503062] mean value: 0.015723252296447755 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: [0.09015608 0.09956288 0.13018298 0.1281836 0.09882951 0.11058927 0.10722399 0.10750437 0.09557104 0.11693478] mean value: 0.10847384929656982 key: score_time value: [0.04173255 0.06210113 0.05165505 0.06780457 0.05070138 0.05854702 0.05252862 0.06386089 0.07204771 0.04695678] mean value: 0.05679357051849365 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: [0.00920463 0.01015854 0.00991392 0.01018214 0.00919986 0.00899363 0.00970221 0.01006627 0.01015687 0.00998592] mean value: 0.00975639820098877 key: score_time value: [0.00920033 0.009377 0.00934243 0.0094049 0.00862455 0.00882125 0.00914502 0.00914598 0.00916696 0.00915956] mean value: 0.009138798713684082 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: [0.00935197 0.00813079 0.00821328 0.00815511 0.00824046 0.00807285 0.00819373 0.00816941 0.00828099 0.00876212] mean value: 0.008357071876525879 key: score_time value: [0.00913334 0.00846624 0.00820827 0.00829124 0.00821042 0.00843406 0.00823331 0.0084455 0.00835991 0.00913835] mean value: 0.008492064476013184 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: [0.08343029 0.07999516 0.0794611 0.08021879 0.08074927 0.07914686 0.08507109 0.08297443 0.08542418 0.08515954] mean value: 0.08216307163238526 key: score_time value: [0.01861143 0.01846194 0.01848698 0.01834464 0.01951504 0.01870656 0.01854014 0.01856232 0.018502 0.01856565] mean value: 0.01862967014312744 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: [0.12611079 0.1153512 0.10698032 0.1233685 0.11780524 0.10499978 0.11987543 0.12128925 0.12485504 0.10909796] mean value: 0.11697335243225097 key: score_time value: [0.00882769 0.00873971 0.00909352 0.00874662 0.00869823 0.0087409 0.00900912 0.0095787 0.0087769 0.00871825] mean value: 0.008892965316772462 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: [0.00808597 0.00802708 0.00837111 0.00846457 0.01209831 0.00804043 0.00856924 0.00883937 0.00938725 0.00927067] mean value: 0.008915400505065918 key: score_time value: [0.00836539 0.00826764 0.00840521 0.00876927 0.00855994 0.00856376 0.00871682 0.00931144 0.00927901 0.00861907] mean value: 0.008685755729675292 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: [0.0098021 0.00998521 0.00969505 0.00958514 0.00961423 0.00966907 0.00969791 0.00961161 0.00959039 0.00962734] mean value: 0.00968780517578125 key: score_time value: [0.00874043 0.00836539 0.00836205 0.00834465 0.00827217 0.00839257 0.00840402 0.00839138 0.00837803 0.00836492] mean value: 0.00840156078338623 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: [0.00794935 0.00888395 0.00920367 0.00797439 0.00891042 0.00893974 0.00868678 0.00903749 0.00901794 0.00891948] mean value: 0.00875232219696045 key: score_time value: [0.01479959 0.00988865 0.00997066 0.00931573 0.00966287 0.00976324 0.00944853 0.01455832 0.00960279 0.00982571] mean value: 0.010683608055114747 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: [0.01098967 0.01364136 0.01366186 0.01374197 0.01377368 0.01355457 0.01382995 0.01405096 0.01379728 0.01390123] mean value: 0.013494253158569336 key: score_time value: [0.01137424 0.01152468 0.0114975 0.01156473 0.01150799 0.01160431 0.0115006 0.01160264 0.01156759 0.01153708] mean value: 0.0115281343460083 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: [0.02226996 0.01795316 0.01448131 0.01715255 0.01667118 0.01589561 0.01461196 0.01626348 0.01555753 0.01657391] mean value: 0.016743063926696777 key: score_time value: [0.01129746 0.00871801 0.00937819 0.00914931 0.00875235 0.00827265 0.00844073 0.00896955 0.00891781 0.00913954] mean value: 0.00910356044769287 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: [0.14993048 0.1616044 0.152004 0.169487 0.18104076 0.15314579 0.16439247 0.16538048 0.16092014 0.17207479] mean value: 0.16299803256988527 key: score_time value: [0.00850177 0.00943732 0.00930285 0.00938153 0.00937605 0.00874543 0.00849867 0.00855517 0.00860405 0.0086534 ] mean value: 0.008905625343322754 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: [0.24151373 0.31732178 0.20899272 0.2480824 0.23589945 0.24067688 0.24676585 0.24599648 0.23300409 0.33954525] mean value: 0.25577986240386963 key: score_time value: [0.01282716 0.01175332 0.01173353 0.01182961 0.01173878 0.01169705 0.01172638 0.01176333 0.01172805 0.01187062] mean value: 0.01186678409576416 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: [0.01154876 0.01127577 0.00858712 0.00814319 0.00837946 0.00889277 0.00902009 0.00868893 0.00828362 0.00838399] mean value: 0.009120368957519531 key: score_time value: [0.01118398 0.01124835 0.00861812 0.00842786 0.00885057 0.00857306 0.00891972 0.00833225 0.00849366 0.00825143] mean value: 0.009089899063110352 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: [0.00845838 0.00860071 0.00848722 0.00885081 0.00808978 0.00885248 0.00819016 0.00901937 0.00845933 0.00805902] mean value: 0.00850672721862793 key: score_time value: [0.00894594 0.00872087 0.00868893 0.00826478 0.00870943 0.00836611 0.00838709 0.00906491 0.00829411 0.00821567] mean value: 0.008565783500671387 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: [0.00944281 0.00986218 0.00964999 0.00876284 0.00858927 0.00873113 0.00857592 0.00833726 0.00859237 0.0086391 ] mean value: 0.008918285369873047 key: score_time value: [0.00912499 0.00907922 0.00838161 0.00837588 0.00822067 0.00842881 0.00826788 0.00829506 0.0084579 0.00854564] mean value: 0.008517765998840332 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: [0.0084331 0.00824332 0.00820327 0.00829053 0.00904059 0.00834751 0.00831366 0.00825167 0.00869656 0.00836062] mean value: 0.008418083190917969 key: score_time value: [0.00837088 0.00824666 0.00837278 0.00829577 0.00903153 0.00833392 0.0082221 0.00839305 0.00824666 0.00831914] mean value: 0.00838325023651123 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: [0.60021329 0.57720327 0.54249668 0.54380131 0.54201055 0.56307817 0.5312767 0.56808448 0.59204793 0.53911257] mean value: 0.5599324941635132 key: score_time value: [0.18375874 0.14311218 0.16572428 0.13906121 0.1496129 0.14000821 0.14519739 0.13516092 0.16832924 0.17730117] mean value: 0.15472662448883057 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: [0.88413286 0.83431458 0.85372138 0.92493558 0.80129886 0.96354318 0.86790776 0.80547285 0.90535116 0.86681151] mean value: 0.8707489728927612 key: score_time value: [0.18714929 0.14363599 0.18987608 0.16804981 0.18646693 0.21752691 0.18257451 0.16987777 0.17513752 0.19997525] mean value: 0.182027006149292 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: [0.02394223 0.00923705 0.0101018 0.00940156 0.00993848 0.00877523 0.00975657 0.01009393 0.00900698 0.00886273] mean value: 0.010911655426025391 key: score_time value: [0.01595521 0.00951576 0.00970626 0.00916386 0.00844979 0.00879884 0.00896311 0.00875783 0.00836921 0.00870633] mean value: 0.009638619422912598 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: [0.02831769 0.02491283 0.02651954 0.02506971 0.02484965 0.02458382 0.02456856 0.02427793 0.02577114 0.02595329] mean value: 0.0254824161529541 key: score_time value: [0.00875139 0.00857258 0.00846076 0.0084796 0.00839949 0.00886321 0.00845504 0.00846457 0.00845838 0.008497 ] mean value: 0.008540201187133788 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: [0.00965905 0.00967765 0.00953698 0.00963664 0.00914717 0.00982451 0.00912523 0.00886345 0.0094161 0.00933146] mean value: 0.009421825408935547 key: score_time value: [0.00935841 0.00985456 0.00974321 0.00947905 0.00937629 0.00971651 0.0096314 0.00936913 0.00922918 0.00883269] mean value: 0.0094590425491333 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: [0.00848198 0.00838614 0.00836706 0.00840068 0.00844431 0.00905252 0.00888348 0.00917697 0.00973868 0.00835919] mean value: 0.008729100227355957 key: score_time value: [0.00826812 0.00857329 0.00815392 0.00844526 0.00853515 0.00836849 0.00935626 0.00903535 0.00908804 0.00849342] mean value: 0.008631730079650879 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:427: 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:454: 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: [0.03942108 0.0357883 0.03759861 0.03542089 0.0371716 0.03603816 0.03947306 0.20275068 0.03404307 0.03263688] mean value: 0.05303423404693604 key: score_time value: [0.01133871 0.01178408 0.01097012 0.01137185 0.01078272 0.01107693 0.01159072 0.01123071 0.01056361 0.01047015] mean value: 0.011117959022521972 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: 15 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 23 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: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall'] 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: 0.1s 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. 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Building estimator 8 of 9 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 8 for this parallel run (total 100)... 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 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 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 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 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 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 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... q:l3V@/(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 2 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 8 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 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 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 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 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 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 8 for this parallel run (total 100)... 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... 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 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 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 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 3 of 8 for this parallel run (total 100)... 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 5 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 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 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 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 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 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 4 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 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 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 7 of 9 for this parallel run (total 100)... 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 5 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 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 6 of 8 for this parallel run (total 100)... 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... [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. 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 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 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 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 6 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 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 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 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 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 6 of 8 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 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 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 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (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_p8VP|=׍[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: 0.1s 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 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 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 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 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 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 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 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 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 4 of 8 for this parallel run (total 100)... 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 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s 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 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 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 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... 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 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 7 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 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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)... [Parallel(n_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 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 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 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 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 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 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... q@.@.[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 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 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 3 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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)... [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: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [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 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 9 out of 12 | elapsed: 0.3s remaining: 0.1s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 9 for this parallel run (total 100)... 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 1 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... 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 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 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)... [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 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 2 of 8 for this parallel run (total 100)... 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 3 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 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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 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 5 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 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 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 7 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 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 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 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 3 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 5 of 9 for this parallel run (total 100)... 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 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 9 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 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 4 out of 12 | elapsed: 0.3s remaining: 0.5s Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 6 of 9 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 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 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 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 4 of 8 for this parallel run (total 100)... 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 4 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 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 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 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 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 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 6 of 8 for this parallel run (total 100)... 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 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 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 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 2 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 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 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 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 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 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 8 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 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 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 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 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 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 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 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 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 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 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (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 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 6 of 8 for this parallel run (total 100)... Building estimator 6 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 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 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 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 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. [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)]: 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 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)]: 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 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)]: 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)]: 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 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 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 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 Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_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: [0.07088137 0.06979895 0.07155275 0.07130504 0.07216024 0.07068849 0.07368135 0.07089376 0.07142544 0.07148743] mean value: 0.07138748168945312 key: score_time value: [0.01498604 0.01561141 0.01537704 0.01454854 0.01553416 0.01516986 0.0147202 0.01489377 0.01559353 0.01535773] mean value: 0.01517922878265381 key: test_mcc value: [0.16666667 0.16666667 0.66666667 0.16666667 0.61237244 0.40824829 0.57735027 0. 1. 0.57735027] mean value: 0.43419879312055765 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 0.5 0.8 0.5 1. 0.8 ] mean value: 0.6923809523809524 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 1. 0.66666667 0.5 1. 0.66666667] mean value: 0.6916666666666667 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 1. 0.5 1. 1. ] mean value: 0.75 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 0.6 0.75 0.5 1. 0.75] mean value: 0.7 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 0.66666667 0.75 0.5 1. 0.75 ] mean value: 0.7 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 0.33333333 0.66666667 0.33333333 1. 0.66666667] mean value: 0.5583333333333333 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: 0.43 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: [0.0968864 0.11801028 0.09970188 0.12701225 0.14569211 0.10844421 0.14443231 0.11996961 0.10739756 0.11337447] mean value: 0.11809210777282715 key: score_time value: [0.06114459 0.03731775 0.06222534 0.07297015 0.05134463 0.05550885 0.03839612 0.06989408 0.0383203 0.05145597] mean value: 0.05385777950286865 key: test_mcc value: [0.16666667 0.61237244 1. 0.16666667 0.16666667 0.40824829 0.57735027 0. 1. 1. ] mean value: 0.5097970995349284 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.66666667 1. 0.66666667 0.66666667 0.5 0.8 0.66666667 1. 1. ] mean value: 0.7466666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 1. 1. 0.66666667 0.66666667 1. 0.66666667 0.5 1. 1. ] mean value: 0.8 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. 1. 1. 1. ] mean value: 0.7666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.8 1. 0.6 0.6 0.6 0.75 0.5 1. 1. ] mean value: 0.745 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 1. 0.58333333 0.58333333 0.66666667 0.75 0.5 1. 1. ] mean value: 0.7416666666666667 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 0.5 1. 0.5 0.5 0.33333333 0.66666667 0.5 1. 1. ] mean value: 0.6333333333333333 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: 0.51 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: [0.0103035 0.00883818 0.0100503 0.00899768 0.0089047 0.01392531 0.01449227 0.01354194 0.00921726 0.00934529] mean value: 0.010761642456054687 key: score_time value: [0.00900936 0.00859308 0.00837898 0.00829482 0.00833654 0.01341581 0.0134666 0.0091486 0.00860786 0.00855803] mean value: 0.00958096981048584 key: test_mcc value: [ 0.61237244 0.61237244 0.66666667 0.16666667 0.61237244 0.40824829 0.57735027 -0.57735027 1. 0.57735027] mean value: 0.46560492000742065 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 0.8 0.66666667 0.85714286 0.5 0.8 0.4 1. 0.8 ] mean value: 0.7157142857142857 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.66666667 0.66666667 0.75 1. 0.66666667 0.33333333 1. 0.66666667] mean value: 0.775 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 1. 0.5 1. 1. ] mean value: 0.75 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 0.8 0.6 0.8 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 0.75 0.83333333 0.58333333 0.75 0.66666667 0.75 0.25 1. 0.75 ] mean value: 0.7083333333333333 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 0.66666667 0.5 0.75 0.33333333 0.66666667 0.25 1. 0.66666667] mean value: 0.5833333333333333 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: 0.47 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: [0.00927901 0.0085938 0.0082202 0.00806785 0.00863695 0.00819731 0.0090692 0.0092175 0.00897098 0.00883889] mean value: 0.008709168434143067 key: score_time value: [0.00919247 0.00865507 0.00819445 0.00891638 0.00822783 0.00916386 0.00907803 0.00921464 0.00912642 0.0083282 ] mean value: 0.008809733390808105 key: test_mcc value: [ 0.61237244 -0.66666667 1. 0.16666667 0.61237244 -0.40824829 0.57735027 0. 0. -1. ] mean value: 0.0893846850117352 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. 1. 0.66666667 0.85714286 0.57142857 0.8 0.66666667 0.5 0. ] mean value: 0.5728571428571428 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0. 1. 0.66666667 0.75 0.5 0.66666667 0.5 0.5 0. ] mean value: 0.5583333333333333 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. ] 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 0.2 1. 0.6 0.8 0.4 0.75 0.5 0.5 0. ] mean value: 0.555 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 0.16666667 1. 0.58333333 0.75 0.33333333 0.75 0.5 0.5 0. ] mean value: 0.5333333333333333 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. 1. 0.5 0.75 0.4 0.66666667 0.5 0.33333333 0. ] mean value: 0.46499999999999997 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: 0.09 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: [0.08186746 0.07829213 0.07604194 0.07571936 0.07979321 0.07597041 0.0771172 0.07677698 0.07582831 0.07556725] mean value: 0.07729742527008057 key: score_time value: [0.01793885 0.01814055 0.01664257 0.01657104 0.01739144 0.01732755 0.01675653 0.01675177 0.01702929 0.01664209] mean value: 0.017119169235229492 key: test_mcc value: [ 0.16666667 -0.66666667 0.16666667 0.16666667 1. -0.16666667 -0.57735027 0. 1. 0.57735027] mean value: 0.16666666666666666 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. 0.5 0.66666667 1. 0.4 0.4 0.66666667 1. 0.66666667] mean value: 0.58 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 1. 0.5 0.33333333 0.5 1. 1. ] mean value: 0.6 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. 0.5 0.66666667 1. 0.33333333 0.5 1. 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 0.6 0.6 1. 0.4 0.25 0.5 1. 0.75] 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 0.58333333 0.58333333 1. 0.41666667 0.25 0.5 1. 0.75 ] 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. 0.33333333 0.5 1. 0.25 0.25 0.5 1. 0.5 ] 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: -0.15 MCC on Training: 0.17 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: [0.12004709 0.11619353 0.11725736 0.11747646 0.11872649 0.10573745 0.10928178 0.1207552 0.11994958 0.11960483] mean value: 0.11650297641754151 key: score_time value: [0.00889969 0.00885892 0.00928378 0.00892401 0.00893497 0.00892329 0.00901747 0.00891471 0.00900674 0.00896883] mean value: 0.008973240852355957 key: test_mcc value: [ 0.61237244 0.61237244 1. 0.16666667 1. 0.40824829 0.57735027 -0.57735027 1. 0. ] mean value: 0.47996598285221187 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 1. 0.66666667 1. 0.5 0.8 0.4 1. 0.5 ] mean value: 0.72 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.66666667 1. 1. 0.66666667 0.33333333 1. 0.5 ] mean value: 0.8166666666666667 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 1. 0.5 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: [0.8 0.8 1. 0.6 1. 0.6 0.75 0.25 1. 0.5 ] mean value: 0.7300000000000001 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 1. 0.58333333 1. 0.66666667 0.75 0.25 1. 0.5 ] 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: [0.5 0.5 1. 0.5 1. 0.33333333 0.66666667 0.25 1. 0.33333333] mean value: 0.6083333333333333 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: 0.48 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: [0.00813937 0.00863266 0.00883198 0.00876927 0.00880313 0.00876546 0.00889754 0.0079639 0.008147 0.00815392] mean value: 0.008510422706604005 key: score_time value: [0.00908256 0.00896645 0.0085392 0.00891399 0.00834346 0.00852489 0.00860429 0.00880432 0.00860095 0.00899816] mean value: 0.008737826347351074 key: test_mcc value: [0.16666667 0. 0.40824829 0.61237244 0.61237244 0.40824829 0. 0.57735027 0.57735027 0. ] mean value: 0.33626086573652336 key: train_mcc value: [0.61969655 0.62048368 0.53206577 0.56010413 0.59982886 0.63994524 0.58834841 0.54659439 0.56652882 0.56652882] mean value: 0.5840124674124577 key: test_fscore value: [0.5 0.57142857 0.66666667 0.85714286 0.85714286 0.5 0.66666667 0.8 0.8 0.66666667] mean value: 0.6885714285714286 key: train_fscore value: [0.82608696 0.82352941 0.79166667 0.79166667 0.80851064 0.82608696 0.80851064 0.79166667 0.8 0.8 ] mean value: 0.8067724601403929 key: test_precision value: [0.5 0.4 0.5 0.75 0.75 1. 0.5 0.66666667 0.66666667 0.5 ] mean value: 0.6233333333333333 key: train_precision value: [0.76 0.7 0.7037037 0.67857143 0.7037037 0.73076923 0.73076923 0.7037037 0.68965517 0.68965517] mean value: 0.7090531346048587 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. 0.9047619 0.95 0.95 0.95 0.9047619 0.9047619 0.95238095 0.95238095] mean value: 0.9373809523809523 key: test_accuracy value: [0.6 0.4 0.6 0.8 0.8 0.6 0.5 0.75 0.75 0.5 ] mean value: 0.63 key: train_accuracy value: [0.80487805 0.7804878 0.75609756 0.75609756 0.7804878 0.80487805 0.78571429 0.76190476 0.76190476 0.76190476] mean value: 0.7754355400696864 key: test_roc_auc value: [0.58333333 0.5 0.66666667 0.75 0.75 0.66666667 0.5 0.75 0.75 0.5 ] mean value: 0.6416666666666666 key: train_roc_auc value: [0.80238095 0.775 0.75238095 0.76071429 0.78452381 0.80833333 0.78571429 0.76190476 0.76190476 0.76190476] mean value: 0.7754761904761904 key: test_jcc value: [0.33333333 0.4 0.5 0.75 0.75 0.33333333 0.5 0.66666667 0.66666667 0.5 ] mean value: 0.54 key: train_jcc value: [0.7037037 0.7 0.65517241 0.65517241 0.67857143 0.7037037 0.67857143 0.65517241 0.66666667 0.66666667] mean value: 0.6763400839262909 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: [0.01024461 0.01082993 0.00972795 0.01096725 0.00998783 0.00987554 0.01061535 0.01060319 0.01106381 0.01122451] mean value: 0.010513997077941895 key: score_time value: [0.00941777 0.00849223 0.00860977 0.0084219 0.00841165 0.00892353 0.009655 0.00945258 0.0088129 0.00955749] mean value: 0.008975481986999512 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 -1. 1. 0.16666667 1. -0.16666667 -0.57735027 0. 0. 0.57735027] mean value: 0.11666666666666667 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. 0.66666667 1. 0.4 0.4 0.66666667 0.5 0.66666667] mean value: 0.58 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. 0.66666667 1. 0.5 0.33333333 0.5 0.5 1. ] mean value: 0.6 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. 1. 0.6 1. 0.4 0.25 0.5 0.5 0.75] mean value: 0.5599999999999999 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. 1. 0.58333333 1. 0.41666667 0.25 0.5 0.5 0.75 ] mean value: 0.5583333333333333 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. 0.5 1. 0.25 0.25 0.5 0.33333333 0.5 ] 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: 0.04 MCC on Training: 0.12 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: [0.00882387 0.00779724 0.00790095 0.00829983 0.00905085 0.0081563 0.0088253 0.00841641 0.0089922 0.00852966] mean value: 0.00847926139831543 key: score_time value: [0.0096581 0.0095911 0.00939512 0.00957799 0.00974464 0.0098207 0.00983071 0.01009345 0.00980425 0.00920081] mean value: 0.009671688079833984 key: test_mcc value: [ 0.16666667 -0.61237244 0.40824829 0.16666667 -0.40824829 0. -0.57735027 0. 0.57735027 0. ] mean value: -0.02790391023624612 key: train_mcc value: [0.31655495 0.47439956 0.51320273 0.51551459 0.46428571 0.56836003 0.57735027 0.43656413 0.47673129 0.52380952] mean value: 0.4866772800860473 key: test_fscore value: [0.5 0.33333333 0.66666667 0.66666667 0.57142857 0. 0.4 0.66666667 0.8 0.5 ] mean value: 0.5104761904761904 key: train_fscore value: [0.68181818 0.76595745 0.77272727 0.76190476 0.73170732 0.79069767 0.8 0.73913043 0.74418605 0.76190476] mean value: 0.7550033897949502 key: test_precision value: [0.5 0.25 0.5 0.66666667 0.5 0. 0.33333333 0.5 0.66666667 0.5 ] mean value: 0.4416666666666666 key: train_precision value: [0.65217391 0.69230769 0.73913043 0.72727273 0.71428571 0.73913043 0.75 0.68 0.72727273 0.76190476] mean value: 0.7183478405652319 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [0.71428571 0.85714286 0.80952381 0.8 0.75 0.85 0.85714286 0.80952381 0.76190476 0.76190476] mean value: 0.7971428571428572 key: test_accuracy value: [0.6 0.2 0.6 0.6 0.4 0.4 0.25 0.5 0.75 0.5 ] mean value: 0.48 key: train_accuracy value: [0.65853659 0.73170732 0.75609756 0.75609756 0.73170732 0.7804878 0.78571429 0.71428571 0.73809524 0.76190476] mean value: 0.7414634146341463 key: test_roc_auc value: [0.58333333 0.25 0.66666667 0.58333333 0.33333333 0.5 0.25 0.5 0.75 0.5 ] mean value: 0.4916666666666667 key: train_roc_auc value: [0.65714286 0.72857143 0.7547619 0.75714286 0.73214286 0.78214286 0.78571429 0.71428571 0.73809524 0.76190476] mean value: 0.7411904761904762 key: test_jcc value: [0.33333333 0.2 0.5 0.5 0.4 0. 0.25 0.5 0.66666667 0.33333333] mean value: 0.3683333333333333 key: train_jcc value: [0.51724138 0.62068966 0.62962963 0.61538462 0.57692308 0.65384615 0.66666667 0.5862069 0.59259259 0.61538462] mean value: 0.6074565281461833 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: [0.01167679 0.01826382 0.01448941 0.01492167 0.01469254 0.01517916 0.01869845 0.01910591 0.01449609 0.0146513 ] mean value: 0.01561751365661621 key: score_time value: [0.01538086 0.01228762 0.01230597 0.01232481 0.01150227 0.01221585 0.01167464 0.01225209 0.01155663 0.01207185] mean value: 0.012357258796691894 key: test_mcc value: [-0.61237244 0.16666667 1. -0.61237244 -0.66666667 0.66666667 0.57735027 0.57735027 1. -0.57735027] mean value: 0.15192720644647034 key: train_mcc value: [0.8547619 0.75714286 0.90238095 0.90238095 0.90238095 0.8047619 0.9047619 0.85811633 0.9047619 0.9047619 ] mean value: 0.8696211568416272 key: test_fscore value: [0.33333333 0.5 1. 0. 0.33333333 0.8 0.8 0.66666667 1. 0.4 ] mean value: 0.5833333333333334 key: train_fscore value: [0.92682927 0.87804878 0.95238095 0.95 0.95 0.9 0.95238095 0.92682927 0.95238095 0.95238095] mean value: 0.934123112659698 key: test_precision value: [0.25 0.5 1. 0. 0.33333333 1. 0.66666667 1. 1. 0.33333333] mean value: 0.6083333333333333 key: train_precision value: [0.95 0.9 0.95238095 0.95 0.95 0.9 0.95238095 0.95 0.95238095 0.95238095] mean value: 0.940952380952381 key: test_recall value: [0.5 0.5 1. 0. 0.33333333 0.66666667 1. 0.5 1. 0.5 ] mean value: 0.6 key: train_recall value: [0.9047619 0.85714286 0.95238095 0.95 0.95 0.9 0.95238095 0.9047619 0.95238095 0.95238095] mean value: 0.9276190476190477 key: test_accuracy value: [0.2 0.6 1. 0.2 0.2 0.8 0.75 0.75 1. 0.25] mean value: 0.575 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)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/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)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( train_accuracy value: [0.92682927 0.87804878 0.95121951 0.95121951 0.95121951 0.90243902 0.95238095 0.92857143 0.95238095 0.95238095] mean value: 0.9346689895470384 key: test_roc_auc value: [0.25 0.58333333 1. 0.25 0.16666667 0.83333333 0.75 0.75 1. 0.25 ] mean value: 0.5833333333333334 key: train_roc_auc value: [0.92738095 0.87857143 0.95119048 0.95119048 0.95119048 0.90238095 0.95238095 0.92857143 0.95238095 0.95238095] mean value: 0.9347619047619047 key: test_jcc value: [0.2 0.33333333 1. 0. 0.2 0.66666667 0.66666667 0.5 1. 0.25 ] mean value: 0.48166666666666663 key: train_jcc value: [0.86363636 0.7826087 0.90909091 0.9047619 0.9047619 0.81818182 0.90909091 0.86363636 0.90909091 0.90909091] mean value: 0.8773950686994165 MCC on Blind test: -0.3 MCC on Training: 0.15 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: [0.02232027 0.01768303 0.01362634 0.01618624 0.01690435 0.01509452 0.01629186 0.0160079 0.01721382 0.01615214] mean value: 0.016748046875 key: score_time value: [0.01149535 0.00892115 0.0090301 0.00848508 0.00858998 0.00863743 0.00846815 0.00854588 0.00891304 0.00888133] mean value: 0.00899674892425537 key: test_mcc value: [-0.66666667 0.16666667 0.61237244 0.16666667 0.61237244 0. 0.57735027 0. 1. 0. ] mean value: 0.24687618072478817 key: train_mcc value: [0.90238095 0.95238095 0.8547619 1. 0.90238095 0.90649828 0.80952381 0.9047619 0.85811633 0.95346259] mean value: 0.9044267675090705 key: test_fscore value: [0. 0.5 0.66666667 0.66666667 0.85714286 0. 0.8 0.66666667 1. 0.66666667] mean value: 0.5823809523809524 key: train_fscore value: [0.95238095 0.97560976 0.92682927 1. 0.95 0.94736842 0.9047619 0.95238095 0.92682927 0.97674419] mean value: 0.951290470930588 key: test_precision value: [0. 0.5 1. 0.66666667 0.75 0. 0.66666667 0.5 1. 0.5 ] mean value: 0.5583333333333333 key: train_precision value: [0.95238095 1. 0.95 1. 0.95 1. 0.9047619 0.95238095 0.95 0.95454545] mean value: 0.9614069264069263 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 0.9047619 1. 0.95 0.9 0.9047619 0.95238095 0.9047619 1. ] mean value: 0.9421428571428571 key: test_accuracy value: [0.2 0.6 0.8 0.6 0.8 0.4 0.75 0.5 1. 0.5 ] mean value: 0.615 key: train_accuracy value: [0.95121951 0.97560976 0.92682927 1. 0.95121951 0.95121951 0.9047619 0.95238095 0.92857143 0.97619048] mean value: 0.9518002322880372 key: test_roc_auc value: [0.16666667 0.58333333 0.75 0.58333333 0.75 0.5 0.75 0.5 1. 0.5 ] mean value: 0.6083333333333333 key: train_roc_auc value: [0.95119048 0.97619048 0.92738095 1. 0.95119048 0.95 0.9047619 0.95238095 0.92857143 0.97619048] mean value: 0.9517857142857142 key: test_jcc value: [0. 0.33333333 0.5 0.5 0.75 0. 0.66666667 0.5 1. 0.5 ] mean value: 0.475 key: train_jcc value: [0.90909091 0.95238095 0.86363636 1. 0.9047619 0.9 0.82608696 0.90909091 0.86363636 0.95454545] mean value: 0.9083229813664596 MCC on Blind test: 0.03 MCC on Training: 0.25 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: [0.15982699 0.18013906 0.17114401 0.16270256 0.18830061 0.18011808 0.17512369 0.18504333 0.1956327 0.18354774] mean value: 0.17815787792205812 key: score_time value: [0.00964713 0.00978851 0.00865817 0.00940061 0.00867319 0.00906944 0.0096128 0.00865793 0.00980377 0.0095191 ] mean value: 0.009283065795898438 key: test_mcc value: [-0.66666667 0.16666667 0.61237244 0.16666667 0. 0.40824829 0.57735027 0. 1. 0. ] mean value: 0.226463766201595 key: train_mcc value: [0.8047619 1. 0.70714286 1. 0. 1. 0.76277007 1. 1. 1. ] mean value: 0.8274674833301235 key: test_fscore value: [0. 0.5 0.66666667 0.66666667 0. 0.5 0.8 0.66666667 1. 0.66666667] mean value: 0.5466666666666666 key: train_fscore value: [0.9047619 1. 0.85714286 1. 0. 1. 0.87804878 1. 1. 1. ] mean value: 0.8639953542392567 key: test_precision value: [0. 0.5 1. 0.66666667 0. 1. 0.66666667 0.5 1. 0.5 ] mean value: 0.5833333333333333 key: train_precision value: [0.9047619 1. 0.85714286 1. 0. 1. 0.9 1. 1. 1. ] mean value: 0.8661904761904762 key: test_recall value: [0. 0.5 0.5 0.66666667 0. 0.33333333 1. 1. 1. 1. ] mean value: 0.6 key: train_recall value: [0.9047619 1. 0.85714286 1. 0. 1. 0.85714286 1. 1. 1. ] mean value: 0.8619047619047618 key: test_accuracy value: [0.2 0.6 0.8 0.6 0.4 0.6 0.75 0.5 1. 0.5 ] mean value: 0.595 key: train_accuracy value: [0.90243902 1. 0.85365854 1. 0.51219512 1. 0.88095238 1. 1. 1. ] mean value: 0.914924506387921 key: test_roc_auc value: [0.16666667 0.58333333 0.75 0.58333333 0.5 0.66666667 0.75 0.5 1. 0.5 ] mean value: 0.6 key: train_roc_auc value: [0.90238095 1. 0.85357143 1. 0.5 1. 0.88095238 1. 1. 1. ] mean value: 0.9136904761904763 key: test_jcc value: [0. 0.33333333 0.5 0.5 0. 0.33333333 0.66666667 0.5 1. 0.5 ] mean value: 0.4333333333333333 key: train_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/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)) [0.82608696 1. 0.75 1. 0. 1. 0.7826087 1. 1. 1. ] mean value: 0.8358695652173914 MCC on Blind test: -0.23 MCC on Training: 0.23 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: [0.22335672 0.23056054 0.23545814 0.23782778 0.33668995 0.23442483 0.24139619 0.23602247 0.24432111 0.23437071] mean value: 0.24544284343719483 key: score_time value: [0.01173234 0.01174593 0.0117445 0.02407169 0.01178074 0.011729 0.01169252 0.01177955 0.01169324 0.01172042] mean value: 0.012968993186950684 key: test_mcc value: [-0.40824829 0.16666667 -0.40824829 0.16666667 0.40824829 0.40824829 1. 0. 0.57735027 0. ] mean value: 0.19106836025229593 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.5 0. 0.66666667 0.5 0.5 1. 0.66666667 0.66666667 0.66666667] mean value: 0.5166666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.5 0. 0.66666667 1. 1. 1. 0.5 1. 0.5 ] mean value: 0.6166666666666666 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. 0.66666667 0.33333333 0.33333333 1. 1. 0.5 1. ] mean value: 0.5333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.6 0.4 0.6 0.6 0.6 1. 0.5 0.75 0.5 ] 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: [0.33333333 0.58333333 0.33333333 0.58333333 0.66666667 0.66666667 1. 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. 0.33333333 0. 0.5 0.33333333 0.33333333 1. 0.5 0.5 0.5 ] mean value: 0.4 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: 0.19 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: [0.01112938 0.01100969 0.00846815 0.00803924 0.00810933 0.00823998 0.00898314 0.00798368 0.00822973 0.00795078] mean value: 0.008814311027526856 key: score_time value: [0.01108384 0.01106858 0.00879216 0.00833678 0.00837255 0.00889945 0.00877881 0.00815177 0.00817919 0.0082829 ] mean value: 0.008994603157043457 key: test_mcc value: [-0.40824829 0.40824829 -0.16666667 0.16666667 0.16666667 0. 0. 0. 0.57735027 0. ] mean value: 0.07440169358562924 key: train_mcc value: [0.2681441 0.65871309 0.41487884 0.41487884 0.51190476 0.41428571 0.42857143 0.42857143 0.38138504 0.52620136] mean value: 0.44475346110468605 key: test_fscore value: [0. 0.66666667 0.4 0.66666667 0.66666667 0. 0.66666667 0.66666667 0.8 0.5 ] mean value: 0.5033333333333333 key: train_fscore value: [0.66666667 0.8372093 0.72727273 0.68421053 0.75 0.7 0.71428571 0.71428571 0.69767442 0.77272727] mean value: 0.7264332342484117 key: test_precision value: [0. 0.5 0.33333333 0.66666667 0.66666667 0. 0.5 0.5 0.66666667 0.5 ] mean value: 0.4333333333333333 key: train_precision value: [0.625 0.81818182 0.69565217 0.72222222 0.75 0.7 0.71428571 0.71428571 0.68181818 0.73913043] mean value: 0.7160576259489303 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 0.85714286 0.76190476 0.65 0.75 0.7 0.71428571 0.71428571 0.71428571 0.80952381] mean value: 0.7385714285714287 key: test_accuracy value: [0.4 0.6 0.4 0.6 0.6 0.4 0.5 0.5 0.75 0.5 ] mean value: 0.525 key: train_accuracy value: [0.63414634 0.82926829 0.70731707 0.70731707 0.75609756 0.70731707 0.71428571 0.71428571 0.69047619 0.76190476] mean value: 0.7222415795586528 key: test_roc_auc value: [0.33333333 0.66666667 0.41666667 0.58333333 0.58333333 0.5 0.5 0.5 0.75 0.5 ] mean value: 0.5333333333333333 key: train_roc_auc value: [0.63214286 0.82857143 0.70595238 0.70595238 0.75595238 0.70714286 0.71428571 0.71428571 0.69047619 0.76190476] mean value: 0.7216666666666667 key: test_jcc value: [0. 0.5 0.25 0.5 0.5 0. 0.5 0.5 0.66666667 0.33333333] mean value: 0.375 key: train_jcc value: [0.5 0.72 0.57142857 0.52 0.6 0.53846154 0.55555556 0.55555556 0.53571429 0.62962963] mean value: 0.5726345136345137 MCC on Blind test: 0.23 MCC on Training: 0.07 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: [0.00891638 0.00885677 0.00823188 0.00822496 0.00908804 0.00876379 0.00821567 0.00836968 0.00843811 0.00831771] mean value: 0.008542299270629883 key: score_time /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") value: [0.00907683 0.00833702 0.00835824 0.00836325 0.0092988 0.00829434 0.00833154 0.00847101 0.00832009 0.00835919] mean value: 0.008521032333374024 key: test_mcc value: [ 0.61237244 -0.16666667 -0.16666667 0.16666667 -0.61237244 0.40824829 0.57735027 -0.57735027 0.57735027 -0.57735027] mean value: 0.02415816237971965 key: train_mcc value: [0.78072006 0.7633652 0.62325386 0.73786479 0.73786479 0.73786479 0.82462113 0.78446454 0.72760688 0.78446454] mean value: 0.7502090562083469 key: test_fscore value: [0.66666667 0.4 0.4 0.66666667 0. 0.5 0.66666667 0. 0.8 0.4 ] mean value: 0.45 key: train_fscore value: [0.86486486 0.88888889 0.78947368 0.82352941 0.82352941 0.82352941 0.89473684 0.86486486 0.84210526 0.86486486] mean value: 0.8480387508251285 key: test_precision value: [1. 0.33333333 0.33333333 0.66666667 0. 1. 1. 0. 0.66666667 0.33333333] mean value: 0.5333333333333333 key: train_precision value: [1. 0.83333333 0.88235294 1. 1. 1. 1. 1. 0.94117647 1. ] mean value: 0.9656862745098038 key: test_recall value: [0.5 0.5 0.5 0.66666667 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.45 key: train_recall value: [0.76190476 0.95238095 0.71428571 0.7 0.7 0.7 0.80952381 0.76190476 0.76190476 0.76190476] mean value: 0.7623809523809524 key: test_accuracy value: [0.8 0.4 0.4 0.6 0.2 0.6 0.75 0.25 0.75 0.25] mean value: 0.5 key: train_accuracy value: [0.87804878 0.87804878 0.80487805 0.85365854 0.85365854 0.85365854 0.9047619 0.88095238 0.85714286 0.88095238] mean value: 0.8645760743321718 key: test_roc_auc value: [0.75 0.41666667 0.41666667 0.58333333 0.25 0.66666667 0.75 0.25 0.75 0.25 ] mean value: 0.5083333333333333 key: train_roc_auc value: [0.88095238 0.87619048 0.80714286 0.85 0.85 0.85 0.9047619 0.88095238 0.85714286 0.88095238] mean value: 0.8638095238095238 key: test_jcc value: [0.5 0.25 0.25 0.5 0. 0.33333333 0.5 0. 0.66666667 0.25 ] mean value: 0.32499999999999996 key: train_jcc value: [0.76190476 0.8 0.65217391 0.7 0.7 0.7 0.80952381 0.76190476 0.72727273 0.76190476] mean value: 0.7374684735554301 MCC on Blind test: -0.07 MCC on Training: 0.02 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: [0.0114274 0.00852418 0.00853562 0.00821495 0.00862932 0.00857162 0.00912762 0.00930691 0.00964165 0.00927424] mean value: 0.009125351905822754 key: score_time value: [0.0082233 0.00810313 0.00813222 0.00810933 0.00816798 0.00892162 0.00892925 0.00901985 0.00906062 0.00914264] mean value: 0.008580994606018067 key: test_mcc value: [-0.61237244 -0.16666667 0.40824829 0.16666667 0. 0. 1. 0.57735027 1. 0. ] mean value: 0.2373226123957694 key: train_mcc value: [0.77831178 0.90649828 0.698212 0.86333169 0.8213423 0.90649828 0.82462113 0.78446454 0.82462113 0.95346259] mean value: 0.8361363718506268 key: test_fscore value: [0.33333333 0.4 0.66666667 0.66666667 0.75 0. 1. 0.8 1. 0.66666667] mean value: 0.6283333333333333 key: train_fscore value: [0.89361702 0.95454545 0.85714286 0.93023256 0.90909091 0.94736842 0.89473684 0.86486486 0.91304348 0.97674419] mean value: 0.9141386592525492 key: test_precision value: [0.25 0.33333333 0.5 0.66666667 0.6 0. 1. 0.66666667 1. 0.5 ] mean value: 0.5516666666666666 key: train_precision value: [0.80769231 0.91304348 0.75 0.86956522 0.83333333 1. 1. 1. 0.84 0.95454545] mean value: 0.896817979122327 key: test_recall value: [0.5 0.5 1. 0.66666667 1. 0. 1. 1. 1. 1. ] mean value: 0.7666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 0.9 0.80952381 0.76190476 1. 1. ] mean value: 0.9471428571428572 key: test_accuracy value: [0.2 0.4 0.6 0.6 0.6 0.4 1. 0.75 1. 0.5 ] mean value: 0.605 key: train_accuracy value: [0.87804878 0.95121951 0.82926829 0.92682927 0.90243902 0.95121951 0.9047619 0.88095238 0.9047619 0.97619048] mean value: 0.9105691056910569 key: test_roc_auc value: [0.25 0.41666667 0.66666667 0.58333333 0.5 0.5 1. 0.75 1. 0.5 ] mean value: 0.6166666666666666 key: train_roc_auc value: [0.875 0.95 0.825 0.92857143 0.9047619 0.95 0.9047619 0.88095238 0.9047619 0.97619048] mean value: 0.9099999999999999 key: test_jcc value: [0.2 0.25 0.5 0.5 0.6 0. 1. 0.66666667 1. 0.5 ] mean value: 0.5216666666666667 key: train_jcc value: [0.80769231 0.91304348 0.75 0.86956522 0.83333333 0.9 0.80952381 0.76190476 0.84 0.95454545] mean value: 0.843960836265184 MCC on Blind test: 0.01 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: [0.00907946 0.00956416 0.00959086 0.00946903 0.00860596 0.00879407 0.00890636 0.00897217 0.00866079 0.00853682] mean value: 0.00901796817779541 key: score_time value: [0.00881743 0.00922155 0.00925684 0.00932598 0.00868034 0.00835371 0.00860643 0.00837111 0.00851178 0.00883555] mean value: 0.00879807472229004 key: test_mcc value: [-0.16666667 -0.61237244 0.61237244 -0.40824829 -0.16666667 -0.66666667 0.57735027 0. -1. 0. ] mean value: -0.1830898021274237 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.33333333 0.66666667 0.57142857 0.4 0.33333333 0.8 0.66666667 0. 0.5 ] mean value: 0.4671428571428572 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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/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/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/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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 0.25 1. 0.5 0.5 0.33333333 0.66666667 0.5 0. 0.5 ] mean value: 0.4583333333333333 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. 0.5 ] mean value: 0.5333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.2 0.8 0.4 0.4 0.2 0.75 0.5 0. 0.5 ] mean value: 0.41500000000000004 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 0.25 0.75 0.33333333 0.41666667 0.16666667 0.75 0.5 0. 0.5 ] mean value: 0.4083333333333333 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 0.2 0.5 0.4 0.25 0.2 0.66666667 0.5 0. 0.33333333] mean value: 0.33 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.39 MCC on Training: -0.18 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: [0.6046989 0.5468781 0.5721488 0.52542901 0.56998491 0.55421329 0.53914094 0.54499841 0.56526446 0.5692544 ] mean value: 0.5592011213302612 key: score_time value: [0.11945629 0.18391323 0.1769104 0.14987516 0.16597605 0.19020319 0.15206194 0.1613009 0.19221568 0.15517879] mean value: 0.16470916271209718 key: test_mcc value: [0.16666667 0. 1. 0.16666667 0.61237244 0.40824829 0. 0. 1. 0.57735027] mean value: 0.39313043286826166 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. 0.66666667 0.85714286 0.5 0.66666667 0.66666667 1. 0.66666667] mean value: 0.6523809523809525 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. 0.66666667 0.75 1. 0.5 0.5 1. 1. ] mean value: 0.6916666666666667 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 1. 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 1. 0.6 0.8 0.6 0.5 0.5 1. 0.75] mean value: 0.695 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 0.5 1. 0.58333333 0.75 0.66666667 0.5 0.5 1. 0.75 ] mean value: 0.6833333333333333 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. 0.5 0.75 0.33333333 0.5 0.5 1. 0.5 ] mean value: 0.5416666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.3 MCC on Training: 0.39 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: [0.83173156 0.87211442 0.86492038 0.81923556 0.89267898 0.93334937 0.88286328 0.85631347 0.8562398 0.84969449] mean value: 0.8659141302108765 key: score_time value: [0.19917798 0.18953228 0.17793465 0.16570473 0.18589878 0.21631145 0.18925476 0.2202661 0.18394113 0.17896175] mean value: 0.19069836139678956 key: test_mcc value: [0.16666667 0.61237244 0.66666667 0.16666667 1. 0.40824829 0. 0. 0.57735027 0.57735027] mean value: 0.41753212645389093 key: train_mcc value: [0.8047619 0.90692382 0.8047619 0.90238095 0.8047619 0.8047619 0.80952381 0.85811633 0.80952381 0.80952381] mean value: 0.8315040154305618 key: test_fscore value: [0.5 0.66666667 0.8 0.66666667 1. 0.5 0.66666667 0.66666667 0.8 0.66666667] mean value: 0.6933333333333334 key: train_fscore value: [0.9047619 0.95 0.9047619 0.95 0.9 0.9 0.9047619 0.92682927 0.9047619 0.9047619 ] mean value: 0.9150638792102207 key: test_precision value: [0.5 1. 0.66666667 0.66666667 1. 1. 0.5 0.5 0.66666667 1. ] mean value: 0.75 key: train_precision value: [0.9047619 1. 0.9047619 0.95 0.9 0.9 0.9047619 0.95 0.9047619 0.9047619] mean value: 0.9223809523809525 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 0.9047619 0.9047619 0.95 0.9 0.9 0.9047619 0.9047619 0.9047619 0.9047619] mean value: 0.9083333333333334 key: test_accuracy value: [0.6 0.8 0.8 0.6 1. 0.6 0.5 0.5 0.75 0.75] mean value: 0.6900000000000001 key: train_accuracy value: [0.90243902 0.95121951 0.90243902 0.95121951 0.90243902 0.90243902 0.9047619 0.92857143 0.9047619 0.9047619 ] mean value: 0.9155052264808363 key: test_roc_auc value: [0.58333333 0.75 0.83333333 0.58333333 1. 0.66666667 0.5 0.5 0.75 0.75 ] mean value: 0.6916666666666667 key: train_roc_auc value: [0.90238095 0.95238095 0.90238095 0.95119048 0.90238095 0.90238095 0.9047619 0.92857143 0.9047619 0.9047619 ] mean value: 0.915595238095238 key: test_jcc value: [0.33333333 0.5 0.66666667 0.5 1. 0.33333333 0.5 0.5 0.66666667 0.5 ] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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)) mean value: 0.55 key: train_jcc value: [0.82608696 0.9047619 0.82608696 0.9047619 0.81818182 0.81818182 0.82608696 0.86363636 0.82608696 0.82608696] mean value: 0.8439958592132506 MCC on Blind test: -0.05 MCC on Training: 0.42 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: [0.00993299 0.01000404 0.00990272 0.00939608 0.00977564 0.00943708 0.00933385 0.00914884 0.00901127 0.00894332] mean value: 0.009488582611083984 key: score_time value: [0.00900126 0.00870061 0.00940204 0.00922155 0.00895357 0.00903201 0.00875378 0.00931644 0.00902128 0.00850487] mean value: 0.008990740776062012 key: test_mcc value: [-0.40824829 -0.16666667 0.16666667 -0.16666667 0.66666667 0.40824829 1. 0. 1. 0. ] mean value: 0.25 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 0.5 0.4 0.8 0.5 1. 0.66666667 1. 0.66666667] mean value: 0.5933333333333334 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 0.5 0.5 1. 1. 1. 0.5 1. 0.5 ] mean value: 0.6333333333333333 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: [0.4 0.4 0.6 0.4 0.8 0.6 1. 0.5 1. 0.5] 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: [0.33333333 0.41666667 0.58333333 0.41666667 0.83333333 0.66666667 1. 0.5 1. 0.5 ] 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. 0.25 0.33333333 0.25 0.66666667 0.33333333 1. 0.5 1. 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: -0.31 MCC on Training: 0.25 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: [0.02450752 0.0244503 0.02426744 0.02479362 0.02454066 0.02436495 0.02435231 0.02432179 0.02435064 0.02467346] mean value: 0.024462270736694335 key: score_time value: [0.00837088 0.00856066 0.00850224 0.00854135 0.00844646 0.00846434 0.00847292 0.00845075 0.0084188 0.00848889] mean value: 0.00847172737121582 key: test_mcc value: [-0.66666667 -0.16666667 0.16666667 -0.16666667 0.61237244 0.40824829 1. 0. 1. 0. ] mean value: 0.21872873928263248 key: train_mcc value: [0.85441771 1. 1. 1. 0.85441771 1. 1. 1. 1. 1. ] mean value: 0.9708835415718322 key: test_fscore value: [0. 0.4 0.5 0.4 0.85714286 0.5 1. 0.66666667 1. 0.66666667] mean value: 0.599047619047619 key: train_fscore value: [0.93023256 1. 1. 1. 0.92307692 1. 1. 1. 1. 1. ] mean value: 0.9853309481216458 key: test_precision value: [0. 0.33333333 0.5 0.5 0.75 1. 1. 0.5 1. 0.5 ] mean value: 0.6083333333333333 key: train_precision value: [0.90909091 1. 1. 1. 0.94736842 1. 1. 1. 1. 1. ] mean value: 0.985645933014354 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: [0.95238095 1. 1. 1. 0.9 1. 1. 1. 1. 1. ] mean value: 0.9852380952380952 key: test_accuracy value: [0.2 0.4 0.6 0.4 0.8 0.6 1. 0.5 1. 0.5] mean value: 0.6 key: train_accuracy value: [0.92682927 1. 1. 1. 0.92682927 1. 1. 1. 1. 1. ] mean value: 0.9853658536585366 key: test_roc_auc value: [0.16666667 0.41666667 0.58333333 0.41666667 0.75 0.66666667 1. 0.5 1. 0.5 ] mean value: 0.6 key: train_roc_auc value: [0.92619048 1. 1. 1. 0.92619048 1. 1. 1. 1. 1. ] mean value: 0.9852380952380952 key: test_jcc value: [0. 0.25 0.33333333 0.25 0.75 0.33333333 1. 0.5 1. 0.5 ] mean value: 0.4916666666666666 key: train_jcc value: [0.86956522 1. 1. 1. 0.85714286 1. 1. 1. 1. 1. ] mean value: 0.9726708074534163 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: [0.00820303 0.00800443 0.00808978 0.00802493 0.00798249 0.00805926 0.00806236 0.00807571 0.00816321 0.00828719] 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/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)) 0.008095240592956543 key: score_time value: [0.00824666 0.00841951 0.00829244 0.0082736 0.00825191 0.00831676 0.00824118 0.008219 0.00826025 0.00825834] mean value: 0.008277964591979981 key: test_mcc value: [-0.40824829 -0.61237244 0.16666667 -0.16666667 0.66666667 0. 0. -0.57735027 0. 0.57735027] mean value: -0.035395405949299096 key: train_mcc value: [0.7565654 0.8047619 0.65952381 0.7633652 0.7565654 0.7565654 0.71428571 0.67357531 0.66742381 0.76277007] mean value: 0.73154020250403 key: test_fscore value: [0. 0.33333333 0.5 0.4 0.8 0. 0.66666667 0.4 0.5 0.66666667] mean value: 0.42666666666666664 key: train_fscore value: [0.88372093 0.9047619 0.82926829 0.86486486 0.87179487 0.87179487 0.85714286 0.82051282 0.8372093 0.88372093] mean value: 0.8624791646345814 key: test_precision value: [0. 0.25 0.5 0.5 1. 0. 0.5 0.33333333 0.5 1. ] mean value: 0.4583333333333333 key: train_precision value: [0.86363636 0.9047619 0.85 0.94117647 0.89473684 0.89473684 0.85714286 0.88888889 0.81818182 0.86363636] mean value: 0.8776898351046958 key: test_recall value: [0. 0.5 0.5 0.33333333 0.66666667 0. 1. 0.5 0.5 0.5 ] mean value: 0.45 key: train_recall value: [0.9047619 0.9047619 0.80952381 0.8 0.85 0.85 0.85714286 0.76190476 0.85714286 0.9047619 ] mean value: 0.85 key: test_accuracy value: [0.4 0.2 0.6 0.4 0.8 0.4 0.5 0.25 0.5 0.75] mean value: 0.4800000000000001 key: train_accuracy value: [0.87804878 0.90243902 0.82926829 0.87804878 0.87804878 0.87804878 0.85714286 0.83333333 0.83333333 0.88095238] mean value: 0.8648664343786294 key: test_roc_auc value: [0.33333333 0.25 0.58333333 0.41666667 0.83333333 0.5 0.5 0.25 0.5 0.75 ] mean value: 0.4916666666666666 key: train_roc_auc value: [0.87738095 0.90238095 0.8297619 0.87619048 0.87738095 0.87738095 0.85714286 0.83333333 0.83333333 0.88095238] mean value: 0.8645238095238096 key: test_jcc value: [0. 0.2 0.33333333 0.25 0.66666667 0. 0.5 0.25 0.33333333 0.5 ] mean value: 0.30333333333333334 key: train_jcc value: [0.79166667 0.82608696 0.70833333 0.76190476 0.77272727 0.77272727 0.75 0.69565217 0.72 0.79166667] mean value: 0.7590765104460757 MCC on Blind test: 0.12 MCC on Training: -0.04 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: [0.00881124 0.00913858 0.00944018 0.0088892 0.00848913 0.00884056 0.00910306 0.00832677 0.00881267 0.00922275] mean value: 0.008907413482666016 key: score_time value: [0.00928545 0.00922966 0.00934339 0.0086813 0.00818038 0.00930667 0.00825071 0.00884247 0.00897217 0.00909185] mean value: 0.008918404579162598 key: test_mcc value: [-0.66666667 0.16666667 0. 0.16666667 -0.16666667 0. 1. 0. 0.57735027 0. ] mean value: 0.10773502691896257 key: train_mcc value: [1. 0.95238095 0.59093684 0.90649828 0.90238095 0.73786479 0.81322028 1. 0.90889326 0.90889326] mean value: 0.8721068614545577 key: test_fscore value: [0. 0.5 0. 0.66666667 0.4 0. 1. 0.66666667 0.66666667 0.66666667] mean value: 0.45666666666666667 key: train_fscore value: [1. 0.97560976 0.6875 0.94736842 0.95 0.82352941 0.9 1. 0.95 0.95 ] mean value: 0.9184007588914896 key: test_precision value: [0. 0.5 0. 0.66666667 0.5 0. 1. 0.5 1. 0.5 ] mean value: 0.4666666666666666 key: train_precision value: [1. 1. 1. 1. 0.95 1. 0.94736842 1. 1. 1. ] mean value: 0.9897368421052631 key: test_recall value: [0. 0.5 0. 0.66666667 0.33333333 0. 1. 1. 0.5 1. ] mean value: 0.5 key: train_recall value: [1. 0.95238095 0.52380952 0.9 0.95 0.7 0.85714286 1. 0.9047619 0.9047619 ] mean value: 0.8692857142857143 key: test_accuracy value: [0.2 0.6 0.6 0.6 0.4 0.4 1. 0.5 0.75 0.5 ] mean value: 0.5549999999999999 key: train_accuracy value: [1. 0.97560976 0.75609756 0.95121951 0.95121951 0.85365854 0.9047619 1. 0.95238095 0.95238095] mean value: 0.9297328687572589 key: test_roc_auc value: [0.16666667 0.58333333 0.5 0.58333333 0.41666667 0.5 1. 0.5 0.75 0.5 ] mean value: 0.55 key: train_roc_auc value: [1. 0.97619048 0.76190476 0.95 0.95119048 0.85 0.9047619 1. 0.95238095 0.95238095] mean value: 0.9298809523809524 key: test_jcc value: [0. 0.33333333 0. 0.5 0.25 0. 1. 0.5 0.5 0.5 ] mean value: 0.3583333333333333 key: train_jcc value: [1. 0.95238095 0.52380952 0.9 0.9047619 0.7 0.81818182 1. 0.9047619 0.9047619 ] mean value: 0.860865800865801 MCC on Blind test: 0.15 MCC on Training: 0.11 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:427: 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:454: 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: [0.03750682 0.03452182 0.03570294 0.03578782 0.03548884 0.03390956 0.03578544 0.03628993 0.03644991 0.0362761 ] mean value: 0.03577191829681396 key: score_time value: [0.01046443 0.01038051 0.01065159 0.01118374 0.01060486 0.01112819 0.01139307 0.01042986 0.01071906 0.01041365] mean value: 0.010736894607543946 key: test_mcc value: [ 0.61237244 0.16666667 1. -0.16666667 0.16666667 0.40824829 0.57735027 -0.57735027 1. 1. ] mean value: 0.4187287392826324 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 1. 0.4 0.66666667 0.5 0.8 0.4 1. 1. ] mean value: 0.6933333333333334 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 1. 0.5 0.66666667 1. 0.66666667 0.33333333 1. 1. ] mean value: 0.7666666666666666 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 1. 0.5 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: [0.8 0.6 1. 0.4 0.6 0.6 0.75 0.25 1. 1. ] mean value: 0.7 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 1. 0.41666667 0.58333333 0.66666667 0.75 0.25 1. 1. ] mean value: 0.7 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 1. 0.25 0.5 0.33333333 0.66666667 0.25 1. 1. ] mean value: 0.5833333333333333 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: 0.42 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 15 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 23 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: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall'] 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: [0.07621503 0.07360363 0.07417893 0.07430983 0.07171845 0.07407093 0.07249784 0.06705332 0.068156 0.0687573 ] mean value: 0.07205612659454345 key: score_time value: [0.01585674 0.0160594 0.01581192 0.0159893 0.01554942 0.01597142 0.01444983 0.01414967 0.01507425 0.01533413] mean value: 0.015424609184265137 key: test_mcc value: [-0.61237244 0.16666667 0.66666667 0.40824829 1. -0.16666667 0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.3771943598193238 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: [Parallel(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: 0.1s 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 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 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 6 of 8 for this parallel run (total 100)... 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 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 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 1 of 8 for this parallel run (total 100)... 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 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 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 9 for this parallel run (total 100)... 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 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 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 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 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 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 7 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 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 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 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 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 8 for this parallel run (total 100)... 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (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 Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 7 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 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 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 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 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 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 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 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 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 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 2 of 8 for this parallel run (total 100)... 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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 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 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 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 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 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 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 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 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 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 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 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 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 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 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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: 0.0s [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: 0.1s [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: 0.0s [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: 0.1s 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 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: 0.0s [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 [Parallel(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 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 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 1 of 8 for this parallel run (total 100)... 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 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 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 9 for this parallel run (total 100)... 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 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 4 of 8 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 4 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 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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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 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 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 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 3 of 8 for this parallel run (total 100)... 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 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 7 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 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 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 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 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 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 3 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 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)... [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 4 out of 12 | elapsed: 0.2s remaining: 0.4s [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 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 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 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 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 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)]: 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)]: 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)]: 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)]: 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 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)]: 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 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 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: 0.0s [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 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 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 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 test_fscore value: [0.33333333 0.5 0.8 0.5 1. 0.4 0.8 0.8 0.8 0.8 ] mean value: 0.6733333333333332 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.25 0.5 0.66666667 1. 1. 0.5 0.66666667 0.66666667 0.66666667 0.66666667] mean value: 0.6583333333333334 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. 1. 1. 1. ] mean value: 0.7666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.2 0.6 0.8 0.6 1. 0.4 0.75 0.75 0.75 0.75] mean value: 0.6599999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.25 0.58333333 0.83333333 0.66666667 1. 0.41666667 0.75 0.75 0.75 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: [0.2 0.33333333 0.66666667 0.33333333 1. 0.25 0.66666667 0.66666667 0.66666667 0.66666667] mean value: 0.545 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: 0.38 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: [0.10585761 0.10880518 0.11610079 0.11265016 0.11422777 0.109828 0.12987709 0.12740016 0.10178447 0.17598081] mean value: 0.12025120258331298 key: score_time value: [0.04848433 0.05669498 0.06805182 0.0736239 0.04690218 0.07367539 0.07672715 0.03672194 0.07234097 0.07809186] mean value: 0.06313145160675049 key: test_mcc value: [-0.61237244 0.16666667 0.66666667 0.16666667 0.16666667 -0.16666667 0.57735027 0.57735027 0.57735027 1. ] mean value: 0.3119678371873083 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.33333333 0.5 0.8 0.66666667 0.66666667 0.4 0.8 0.66666667 0.8 1. ] mean value: 0.6633333333333333 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.25 0.5 0.66666667 0.66666667 0.66666667 0.5 0.66666667 1. 0.66666667 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: [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: [0.2 0.6 0.8 0.6 0.6 0.4 0.75 0.75 0.75 1. ] 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: [0.25 0.58333333 0.83333333 0.58333333 0.58333333 0.41666667 0.75 0.75 0.75 1. ] 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: [0.2 0.33333333 0.66666667 0.5 0.5 0.25 0.66666667 0.5 0.66666667 1. ] mean value: 0.5283333333333333 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: 0.31 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: [0.02258229 0.00880694 0.00900722 0.00884438 0.00875759 0.00896645 0.00875711 0.00936079 0.0086937 0.00957966] mean value: 0.010335612297058105 key: score_time value: [0.00884223 0.00870609 0.00825596 0.00897503 0.00818658 0.00845528 0.00824118 0.00823307 0.00876999 0.00825143] mean value: 0.00849168300628662 key: test_mcc value: [-0.61237244 0.61237244 0.66666667 0.66666667 0.66666667 -0.16666667 0.57735027 0.57735027 0. 0.57735027] mean value: 0.3565384140902211 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.33333333 0.66666667 0.8 0.8 0.8 0.4 0.8 0.66666667 0.5 0.8 ] mean value: 0.6566666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.25 1. 0.66666667 1. 1. 0.5 0.66666667 1. 0.5 0.66666667] mean value: 0.725 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 0.5 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: [0.2 0.8 0.8 0.8 0.8 0.4 0.75 0.75 0.5 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: [0.25 0.75 0.83333333 0.83333333 0.83333333 0.41666667 0.75 0.75 0.5 0.75 ] mean value: 0.6666666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.2 0.5 0.66666667 0.66666667 0.66666667 0.25 0.66666667 0.5 0.33333333 0.66666667] mean value: 0.5116666666666666 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: 0.36 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(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/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', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.010041 0.00920272 0.00931287 0.00932002 0.00854254 0.00927401 0.00925541 0.0091722 0.00911927 0.0091598 ] mean value: 0.009239983558654786 key: score_time value: [0.00951338 0.00924969 0.0091536 0.00929809 0.00920486 0.00916767 0.00916123 0.00914097 0.00921082 0.00898218] mean value: 0.009208250045776366 key: test_mcc value: [-0.16666667 -0.40824829 1. -0.40824829 0.16666667 -0.40824829 1. 0. -0.57735027 0. ] mean value: 0.01979048594187849 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. 1. 0.57142857 0.66666667 0.57142857 1. 0. 0.4 0.5 ] mean value: 0.510952380952381 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. 1. 0.5 0.66666667 0.5 1. 0. 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: [0.5 0. 1. 0.66666667 0.66666667 0.66666667 1. 0. 0.5 0.5 ] mean value: 0.55 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 1. 0.4 0.6 0.4 1. 0.5 0.25 0.5 ] mean value: 0.545 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 0.33333333 1. 0.33333333 0.58333333 0.33333333 1. 0.5 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: [0.25 0. 1. 0.4 0.5 0.4 1. 0. 0.25 0.33333333] mean value: 0.4133333333333333 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: 0.02 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: [0.0820632 0.07688308 0.07963014 0.07842422 0.07723594 0.07713103 0.08013487 0.07813239 0.07798409 0.0784924 ] mean value: 0.07861113548278809 key: score_time value: [0.01680231 0.01802921 0.01681995 0.01691961 0.01716518 0.01819587 0.01705599 0.01786733 0.0177505 0.0167017 ] mean value: 0.01733076572418213 key: test_mcc value: [ 0.16666667 0. 0.66666667 0.16666667 1. -0.66666667 0. 0.57735027 0.57735027 0. ] mean value: 0.2488033871712585 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 1. 0.33333333 0.5 0.66666667 0.8 0.5 ] mean value: 0.5766666666666667 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 1. 0.33333333 0.5 1. 0.66666667 0.5 ] mean value: 0.5833333333333333 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 0.6 0.8 0.6 1. 0.2 0.5 0.75 0.75 0.5 ] mean value: 0.63 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 0.5 0.83333333 0.58333333 1. 0.16666667 0.5 0.75 0.75 0.5 ] mean value: 0.6166666666666667 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 1. 0.2 0.33333333 0.5 0.66666667 0.33333333] mean value: 0.4533333333333333 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: 0.25 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: [0.12404656 0.11600733 0.11816883 0.12366486 0.11906648 0.10522175 0.10942721 0.12072515 0.11076951 0.12015915] mean value: 0.11672568321228027 key: score_time value: [0.00864172 0.00862288 0.0088222 0.00874257 0.00877643 0.00863934 0.0090692 0.00877285 0.00887442 0.008744 ] mean value: 0.008770561218261719 key: test_mcc value: [-0.16666667 0.61237244 1. 0.66666667 0.66666667 0.40824829 0.57735027 0.57735027 0.57735027 0. ] mean value: 0.49193382003952024 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 1. 0.8 0.8 0.5 0.8 0.66666667 0.8 0.5 ] mean value: 0.6933333333333332 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. 1. 1. 1. 1. 0.66666667 1. 0.66666667 0.5 ] mean value: 0.8166666666666668 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. 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: [0.4 0.8 1. 0.8 0.8 0.6 0.75 0.75 0.75 0.5 ] 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: [0.41666667 0.75 1. 0.83333333 0.83333333 0.66666667 0.75 0.75 0.75 0.5 ] 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: [0.25 0.5 1. 0.66666667 0.66666667 0.33333333 0.66666667 0.5 0.66666667 0.33333333] mean value: 0.5583333333333333 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: 0.49 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: [0.00915074 0.00822377 0.00828218 0.0084331 0.0093925 0.0093267 0.00845575 0.00869799 0.00905776 0.00892186] mean value: 0.008794236183166503 key: score_time value: [0.00872087 0.00841856 0.00854039 0.00893021 0.0093658 0.00918722 0.00909758 0.00853658 0.00864816 0.00879574] mean value: 0.00882411003112793 key: test_mcc value: [-0.61237244 0.66666667 0.66666667 0.61237244 0. -0.16666667 0.57735027 0.57735027 0. 0.57735027] mean value: 0.2898717474235544 key: train_mcc value: [0.61969655 0.5519099 0.48849265 0.59093684 0.58066054 0.63994524 0.60609153 0.52704628 0.58834841 0.52704628] mean value: 0.5720174209906098 key: test_fscore value: [0.33333333 0.8 0.8 0.85714286 0.75 0.4 0.8 0.8 0.66666667 0.8 ] mean value: 0.7007142857142857 key: train_fscore value: [0.82608696 0.8 0.7755102 0.8 0.8 0.82608696 0.81632653 0.78431373 0.80851064 0.78431373] mean value: 0.802114873701562 key: test_precision value: [0.25 0.66666667 0.66666667 0.75 0.6 0.5 0.66666667 0.66666667 0.5 0.66666667] mean value: 0.5933333333333334 key: train_precision value: [0.76 0.68965517 0.67857143 0.66666667 0.72 0.73076923 0.71428571 0.66666667 0.73076923 0.66666667] mean value: 0.7024050776809398 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 0.95238095 0.9047619 1. 0.9 0.95 0.95238095 0.95238095 0.9047619 0.95238095] mean value: 0.9373809523809523 key: test_accuracy value: [0.2 0.8 0.8 0.8 0.6 0.4 0.75 0.75 0.5 0.75] mean value: 0.635 key: train_accuracy value: [0.80487805 0.75609756 0.73170732 0.75609756 0.7804878 0.80487805 0.78571429 0.73809524 0.78571429 0.73809524] mean value: 0.7681765389082462 key: test_roc_auc value: [0.25 0.83333333 0.83333333 0.75 0.5 0.41666667 0.75 0.75 0.5 0.75 ] mean value: 0.6333333333333334 key: train_roc_auc value: [0.80238095 0.75119048 0.72738095 0.76190476 0.78333333 0.80833333 0.78571429 0.73809524 0.78571429 0.73809524] mean value: 0.7682142857142857 key: test_jcc value: [0.2 0.66666667 0.66666667 0.75 0.6 0.25 0.66666667 0.66666667 0.5 0.66666667] mean value: 0.5633333333333334 key: train_jcc value: [0.7037037 0.66666667 0.63333333 0.66666667 0.66666667 0.7037037 0.68965517 0.64516129 0.67857143 0.64516129] mean value: 0.6699289922371123 MCC on Blind test: -0.03 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: [0.0100069 0.00988388 0.00976467 0.01003194 0.0100348 0.01075625 0.01098824 0.01043177 0.0102098 0.00995898] mean value: 0.0102067232131958 key: score_time value: [0.00851536 0.00850487 0.00874519 0.00857115 0.00878572 0.00922728 0.00910878 0.00863576 0.00853658 0.00946045] mean value: 0.008809113502502441 key: test_mcc value: [ 0.16666667 -0.40824829 1. -0.40824829 0. -0.66666667 0. 1. 0. 0.57735027] mean value: 0.12608536882618998 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. 0.57142857 0.75 0.33333333 0.5 1. 0.5 0.66666667] mean value: 0.5821428571428572 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 0.6 0.33333333 0.5 1. 0.5 1. ] mean value: 0.5933333333333334 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 0.4 1. 0.4 0.6 0.2 0.5 1. 0.5 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: [0.58333333 0.33333333 1. 0.33333333 0.5 0.16666667 0.5 1. 0.5 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: [0.33333333 0. 1. 0.4 0.6 0.2 0.33333333 1. 0.33333333 0.5 ] mean value: 0.4699999999999999 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: 0.13 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: [0.00818205 0.00851893 0.00917649 0.00848556 0.00831485 0.00897002 0.00830007 0.00902057 0.00891423 0.00867987] mean value: 0.00865626335144043 key: score_time value: [0.01083255 0.00954747 0.01015925 0.00933123 0.00915051 0.01006079 0.0101018 0.00983715 0.00978494 0.00884175] mean value: 0.009764742851257325 key: test_mcc value: [-0.16666667 0.16666667 0.66666667 -0.40824829 -0.40824829 -1. 0. 1. 0.57735027 -0.57735027] mean value: -0.014982991426105957 key: train_mcc value: [0.37309549 0.46623254 0.46623254 0.56190476 0.56836003 0.6133669 0.53357838 0.43656413 0.52620136 0.43052839] mean value: 0.4976064514538302 key: test_fscore value: [0.4 0.5 0.8 0.57142857 0.57142857 0. 0.5 1. 0.8 0.4 ] mean value: 0.5542857142857144 key: train_fscore value: [0.72340426 0.75555556 0.75555556 0.7804878 0.79069767 0.80952381 0.7826087 0.73913043 0.77272727 0.72727273] mean value: 0.7636963785685505 key: test_precision value: [0.33333333 0.5 0.66666667 0.5 0.5 0. 0.5 1. 0.66666667 0.33333333] mean value: 0.5 key: train_precision value: [0.65384615 0.70833333 0.70833333 0.76190476 0.73913043 0.77272727 0.72 0.68 0.73913043 0.69565217] mean value: 0.7179057898623116 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 0.5 1. 1. 0.5 ] mean value: 0.6333333333333333 key: train_recall value: [0.80952381 0.80952381 0.80952381 0.8 0.85 0.85 0.85714286 0.80952381 0.80952381 0.76190476] mean value: 0.8166666666666667 key: test_accuracy value: [0.4 0.6 0.8 0.4 0.4 0. 0.5 1. 0.75 0.25] mean value: 0.51 key: train_accuracy value: [0.68292683 0.73170732 0.73170732 0.7804878 0.7804878 0.80487805 0.76190476 0.71428571 0.76190476 0.71428571] mean value: 0.7464576074332172 key: test_roc_auc value: [0.41666667 0.58333333 0.83333333 0.33333333 0.33333333 0. 0.5 1. 0.75 0.25 ] mean value: 0.5 key: train_roc_auc value: [0.6797619 0.7297619 0.7297619 0.78095238 0.78214286 0.80595238 0.76190476 0.71428571 0.76190476 0.71428571] mean value: 0.7460714285714285 key: test_jcc value: [0.25 0.33333333 0.66666667 0.4 0.4 0. 0.33333333 1. 0.66666667 0.25 ] mean value: 0.43 key: train_jcc value: [0.56666667 0.60714286 0.60714286 0.64 0.65384615 0.68 0.64285714 0.5862069 0.62962963 0.57142857] mean value: 0.6184920775265603 MCC on Blind test: 0.21 MCC on Training: -0.01 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: [0.00984979 0.01346278 0.01353693 0.01350045 0.01351118 0.01342678 0.01358747 0.01378918 0.01363397 0.0136826 ] mean value: 0.013198113441467286 key: score_time value: [0.01135111 0.01145077 0.01126647 0.0113349 0.01132107 0.01151824 0.01132131 0.01134777 0.01139069 0.01145697] mean value: 0.011375927925109863 key: test_mcc value: [-1. 0.66666667 0.66666667 0.66666667 -0.16666667 -0.40824829 0. -0.57735027 0.57735027 -0.57735027] mean value: -0.015226522632015571 key: train_mcc value: [1. 0.71121921 0.8547619 0.90238095 0.90238095 0.85441771 0.9047619 0.9047619 0.76277007 0.9047619 ] mean value: 0.8702216512400438 key: test_fscore value: [0. 0.8 0.8 0.8 0.4 0.57142857 0.66666667 0. 0.8 0.4 ] mean value: 0.5238095238095238 key: train_fscore value: [1. 0.85 0.92682927 0.95 0.95 0.92307692 0.95238095 0.95238095 0.88372093 0.95238095] mean value: 0.934076997874502 key: test_precision value: [0. 0.66666667 0.66666667 1. 0.5 0.5 0.5 0. 0.66666667 0.33333333] mean value: 0.4833333333333333 key: train_precision value: [1. 0.89473684 0.95 0.95 0.95 0.94736842 0.95238095 0.95238095 0.86363636 0.95238095] mean value: 0.9412884483937116 key: test_recall value: [0. 1. 1. 0.66666667 0.33333333 0.66666667 1. 0. 1. 0.5 ] mean value: 0.6166666666666666 key: train_recall value: [1. 0.80952381 0.9047619 0.95 0.95 0.9 0.95238095 0.95238095 0.9047619 0.95238095] mean value: 0.9276190476190477 key: test_accuracy value: [0. 0.8 0.8 0.8 0.4 0.4 0.5 0.25 0.75 0.25] mean value: 0.495 key: train_accuracy value: [1. 0.85365854 0.92682927 0.95121951 0.95121951 0.92682927 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9347851335656214 key: test_roc_auc value: [0. 0.83333333 0.83333333 0.83333333 0.41666667 0.33333333 0.5 0.25 0.75 0.25 ] mean value: 0.5 key: train_roc_auc value: [1. 0.8547619 0.92738095 0.95119048 0.95119048 0.92619048 0.95238095 0.95238095 0.88095238 0.95238095] mean value: 0.9348809523809523 key: test_jcc value: [0. 0.66666667 0.66666667 0.66666667 0.25 0.4 0.5 0. 0.66666667 0.25 ] mean value: 0.4066666666666666 key: train_jcc value: [1. 0.73913043 0.86363636 0.9047619 0.9047619 0.85714286 0.90909091 0.90909091 0.79166667 0.90909091] mean value: 0.8788372859025033 MCC on Blind test: -0.3 MCC on Training: -0.02 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(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( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_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: [0.02167916 0.01727057 0.01534629 0.01548314 0.01699781 0.01598907 0.01678061 0.01377487 0.01523089 0.01571107] mean value: 0.016426348686218263 key: score_time value: [0.01209736 0.00889635 0.00891614 0.00930095 0.00926661 0.00888228 0.00913954 0.00879836 0.00855303 0.00841308] mean value: 0.009226369857788085 key: test_mcc value: [-0.16666667 0.16666667 0.61237244 0.66666667 1. -0.61237244 1. 1. 0. 0.57735027] mean value: 0.42440169358562924 key: train_mcc value: [1. 0.95238095 0.8547619 0.95238095 0.90238095 0.90238095 0.85811633 0.85811633 0.95346259 0.85811633] mean value: 0.9092097294494407 key: test_fscore value: [0.4 0.5 0.66666667 0.8 1. 0. 1. 1. 0.66666667 0.8 ] mean value: 0.6833333333333333 key: train_fscore value: [1. 0.97560976 0.92682927 0.97560976 0.95 0.95 0.92682927 0.92682927 0.97560976 0.92682927] mean value: 0.9534146341463415 key: test_precision value: [0.33333333 0.5 1. 1. 1. 0. 1. 1. 0.5 0.66666667] mean value: 0.7 key: train_precision value: [1. 1. 0.95 0.95238095 0.95 0.95 0.95 0.95 1. 0.95 ] mean value: 0.9652380952380952 key: test_recall value: [0.5 0.5 0.5 0.66666667 1. 0. 1. 1. 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 0.95238095 0.9047619 1. 0.95 0.95 0.9047619 0.9047619 0.95238095 0.9047619 ] mean value: 0.9423809523809524 key: test_accuracy value: [0.4 0.6 0.8 0.8 1. 0.2 1. 1. 0.5 0.75] mean value: 0.7050000000000001 key: train_accuracy value: [1. 0.97560976 0.92682927 0.97560976 0.95121951 0.95121951 0.92857143 0.92857143 0.97619048 0.92857143] mean value: 0.954239256678281 key: test_roc_auc value: [0.41666667 0.58333333 0.75 0.83333333 1. 0.25 1. 1. 0.5 0.75 ] mean value: 0.7083333333333333 key: train_roc_auc value: [1. 0.97619048 0.92738095 0.97619048 0.95119048 0.95119048 0.92857143 0.92857143 0.97619048 0.92857143] mean value: 0.954404761904762 key: test_jcc value: [0.25 0.33333333 0.5 0.66666667 1. 0. 1. 1. 0.5 0.66666667] mean value: 0.5916666666666667 key: train_jcc value: [1. 0.95238095 0.86363636 0.95238095 0.9047619 0.9047619 0.86363636 0.86363636 0.95238095 0.86363636] mean value: 0.9121212121212121 MCC on Blind test: 0.03 MCC on Training: 0.42 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: [0.15119743 0.17550659 0.1849103 0.17043042 0.17547202 0.18751788 0.18874145 0.18453479 0.16989779 0.18126726] mean value: 0.17694759368896484 key: score_time value: [0.00947404 0.00928736 0.00936866 0.00957561 0.00939107 0.0097065 0.00903535 0.00937462 0.00960827 0.0092833 ] mean value: 0.009410476684570313 key: test_mcc value: [-0.16666667 -0.16666667 0.61237244 0.16666667 0.66666667 -0.16666667 1. 1. 0. 0.57735027] mean value: 0.35230560382187537 key: train_mcc value: [1. 1. 0.75714286 1. 0.95238095 1. 1. 1. 0.76277007 0.80952381] mean value: 0.9281817690444093 key: test_fscore value: [0.4 0.4 0.66666667 0.66666667 0.8 0.4 1. 1. 0.66666667 0.8 ] mean value: 0.68 key: train_fscore value: [1. 1. 0.87804878 1. 0.97560976 1. 1. 1. 0.87804878 0.9047619 ] mean value: 0.9636469221835074 key: test_precision value: [0.33333333 0.33333333 1. 0.66666667 1. 0.5 1. 1. 0.5 0.66666667] mean value: 0.7 key: train_precision value: [1. 1. 0.9 1. 0.95238095 1. 1. 1. 0.9 0.9047619 ] mean value: 0.9657142857142859 key: test_recall value: [0.5 0.5 0.5 0.66666667 0.66666667 0.33333333 1. 1. 1. 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 1. 0.85714286 1. 1. 1. 1. 1. 0.85714286 0.9047619 ] mean value: 0.961904761904762 key: test_accuracy value: [0.4 0.4 0.8 0.6 0.8 0.4 1. 1. 0.5 0.75] mean value: 0.665 key: train_accuracy value: [1. 1. 0.87804878 1. 0.97560976 1. 1. 1. 0.88095238 0.9047619 ] mean value: 0.9639372822299652 key: test_roc_auc value: [0.41666667 0.41666667 0.75 0.58333333 0.83333333 0.41666667 1. 1. 0.5 0.75 ] mean value: 0.6666666666666666 key: train_roc_auc value: [1. 1. 0.87857143 1. 0.97619048 1. 1. 1. 0.88095238 0.9047619 ] mean value: 0.964047619047619 key: test_jcc value: [0.25 0.25 0.5 0.5 0.66666667 0.25 1. 1. 0.5 0.66666667] mean value: 0.5583333333333333 key: train_jcc value: [1. 1. 0.7826087 1. 0.95238095 1. 1. 1. 0.7826087 0.82608696] mean value: 0.9343685300207039 MCC on Blind test: -0.07 MCC on Training: 0.35 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, 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/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', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.21241379 0.21670055 0.24280739 0.23050094 0.24434733 0.36418724 0.24961042 0.24872684 0.23138309 0.25354838] mean value: 0.24942259788513182 key: score_time value: [0.0118506 0.01177287 0.01247406 0.0120101 0.01199889 0.01177573 0.01179838 0.01185966 0.01180196 0.01221299] mean value: 0.011955523490905761 key: test_mcc value: [-0.16666667 -0.16666667 0. 0.16666667 0.40824829 -0.16666667 1. 1. 0. 0.57735027] mean value: 0.26522652263201557 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. 0.66666667 0.5 0.4 1. 1. 0.66666667 0.8 ] mean value: 0.5833333333333334 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. 0.66666667 1. 0.5 1. 1. 0.5 0.66666667] mean value: 0.6 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. 0.66666667 0.33333333 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: [0.4 0.4 0.6 0.6 0.6 0.4 1. 1. 0.5 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: [0.41666667 0.41666667 0.5 0.58333333 0.66666667 0.41666667 1. 1. 0.5 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: [0.25 0.25 0. 0.5 0.33333333 0.25 1. 1. 0.5 0.66666667] mean value: 0.475 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: 0.27 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: [0.01140952 0.01130176 0.00922251 0.00951958 0.00954652 0.00928497 0.00912905 0.00929046 0.00946569 0.00906324] mean value: 0.009723329544067382 key: score_time value: [0.01129246 0.01127911 0.00967002 0.00933909 0.00947571 0.0094676 0.00899863 0.00900888 0.00985169 0.0090816 ] mean value: 0.009746479988098144 key: test_mcc value: [-0.40824829 0.66666667 0.16666667 0.66666667 0.16666667 -1. 0.57735027 0.57735027 0. 0.57735027] mean value: 0.1990469183771681 key: train_mcc value: [0.41487884 0.56086079 0.41487884 0.61152662 0.36718832 0.41428571 0.43052839 0.43052839 0.42857143 0.43052839] mean value: 0.4503775710325074 key: test_fscore value: [0. 0.8 0.5 0.8 0.66666667 0. 0.8 0.66666667 0.66666667 0.66666667] mean value: 0.5566666666666668 key: train_fscore value: [0.72727273 0.79069767 0.72727273 0.78947368 0.64864865 0.7 0.72727273 0.72727273 0.71428571 0.72727273] mean value: 0.727946935792713 key: test_precision value: [0. 0.66666667 0.5 1. 0.66666667 0. 0.66666667 1. 0.5 1. ] mean value: 0.6 key: train_precision value: [0.69565217 0.77272727 0.69565217 0.83333333 0.70588235 0.7 0.69565217 0.69565217 0.71428571 0.69565217] mean value: 0.7204489542852714 key: test_recall value: [0. 1. 0.5 0.66666667 0.66666667 0. 1. 0.5 1. 0.5 ] mean value: 0.5833333333333333 key: train_recall value: [0.76190476 0.80952381 0.76190476 0.75 0.6 0.7 0.76190476 0.76190476 0.71428571 0.76190476] mean value: 0.7383333333333333 key: test_accuracy value: [0.4 0.8 0.6 0.8 0.6 0. 0.75 0.75 0.5 0.75] mean value: 0.595 key: train_accuracy value: [0.70731707 0.7804878 0.70731707 0.80487805 0.68292683 0.70731707 0.71428571 0.71428571 0.71428571 0.71428571] mean value: 0.7247386759581882 key: test_roc_auc value: [0.33333333 0.83333333 0.58333333 0.83333333 0.58333333 0. 0.75 0.75 0.5 0.75 ] mean value: 0.5916666666666666 key: train_roc_auc value: [0.70595238 0.7797619 0.70595238 0.80357143 0.68095238 0.70714286 0.71428571 0.71428571 0.71428571 0.71428571] mean value: 0.724047619047619 key: test_jcc value: [0. 0.66666667 0.33333333 0.66666667 0.5 0. 0.66666667 0.5 0.5 0.5 ] mean value: 0.4333333333333333 key: train_jcc value: [0.57142857 0.65384615 0.57142857 0.65217391 0.48 0.53846154 0.57142857 0.57142857 0.55555556 0.57142857] mean value: 0.5737180018049582 MCC on Blind test: 0.23 MCC on Training: 0.2 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: [0.00921226 0.00931311 0.00936246 0.00890613 0.00910234 0.0093894 0.00952625 0.00883627 0.00905919 0.00919628] mean value: 0.00919036865234375 key: score_time value: [0.00950003 0.00875306 0.0090425 0.008811 0.00926733 0.00926375 0.00929689 0.0096004 0.00891113 0.00872374] mean value: 0.00911698341369629 key: test_mcc value: [-0.61237244 0.16666667 0.61237244 0.66666667 0. -0.16666667 0. 0. 0.57735027 0. ] mean value: 0.12440169358562925 key: train_mcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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") value: [0.71121921 0.8213423 0.6133669 0.77831178 0.63496528 0.86240942 0.62187434 0.78446454 0.78446454 0.78446454] mean value: 0.739688284295276 key: test_fscore value: [0.33333333 0.5 0.66666667 0.8 0. 0.4 0.5 0. 0.8 0.5 ] mean value: 0.45 key: train_fscore value: [0.85 0.89473684 0.8 0.85714286 0.76470588 0.91891892 0.8 0.86486486 0.86486486 0.86486486] mean value: 0.8480099095114575 key: test_precision value: [0.25 0.5 1. 1. 0. 0.5 0.5 0. 0.66666667 0.5 ] mean value: 0.4916666666666667 key: train_precision value: [0.89473684 1. 0.84210526 1. 0.92857143 1. 0.84210526 1. 1. 1. ] mean value: 0.9507518796992482 key: test_recall value: [0.5 0.5 0.5 0.66666667 0. 0.33333333 0.5 0. 1. 0.5 ] mean value: 0.45 key: train_recall value: [0.80952381 0.80952381 0.76190476 0.75 0.65 0.85 0.76190476 0.76190476 0.76190476 0.76190476] mean value: 0.7678571428571429 key: test_accuracy value: [0.2 0.6 0.8 0.8 0.4 0.4 0.5 0.5 0.75 0.5 ] mean value: 0.545 key: train_accuracy value: [0.85365854 0.90243902 0.80487805 0.87804878 0.80487805 0.92682927 0.80952381 0.88095238 0.88095238 0.88095238] mean value: 0.8623112659698027 key: test_roc_auc value: [0.25 0.58333333 0.75 0.83333333 0.5 0.41666667 0.5 0.5 0.75 0.5 ] mean value: 0.5583333333333333 key: train_roc_auc value: [0.8547619 0.9047619 0.80595238 0.875 0.80119048 0.925 0.80952381 0.88095238 0.88095238 0.88095238] mean value: 0.8619047619047621 key: test_jcc value: [0.2 0.33333333 0.5 0.66666667 0. 0.25 0.33333333 0. 0.66666667 0.33333333] mean value: 0.3283333333333333 key: train_jcc value: [0.73913043 0.80952381 0.66666667 0.75 0.61904762 0.85 0.66666667 0.76190476 0.76190476 0.76190476] mean value: 0.7386749482401657 MCC on Blind test: -0.07 MCC on Training: 0.12 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: [0.00955272 0.00897717 0.0087564 0.00835252 0.00891256 0.0086596 0.00892782 0.00896478 0.00927091 0.00905704] mean value: 0.00894315242767334 key: score_time value: [0.00858188 0.00854778 0.00825453 0.00844264 0.00822306 0.00890517 0.0086174 0.00894809 0.00845265 0.00848794] mean value: 0.008546113967895508 key: test_mcc value: [-0.16666667 0.16666667 0.66666667 -0.40824829 0.66666667 -0.61237244 0.57735027 1. 0. 0.57735027] mean value: 0.24674131455529272 key: train_mcc value: [0.95227002 0.90692382 0.50452498 0.62776482 0.95238095 0.95227002 0.78446454 0.8660254 1. 0.95346259] mean value: 0.850008713999979 key: test_fscore value: [0.4 0.5 0.8 0.57142857 0.8 0. 0.8 1. 0.66666667 0.8 ] mean value: 0.6338095238095238 key: train_fscore value: [0.97674419 0.95 0.77777778 0.81632653 0.97560976 0.97435897 0.89361702 0.93333333 1. 0.97674419] mean value: 0.927451176554951 key: test_precision value: [0.33333333 0.5 0.66666667 0.5 1. 0. 0.66666667 1. 0.5 0.66666667] mean value: 0.5833333333333333 key: train_precision value: [0.95454545 1. 0.63636364 0.68965517 0.95238095 1. 0.80769231 0.875 1. 0.95454545] mean value: 0.88701829779416 key: test_recall value: [0.5 0.5 1. 0.66666667 0.66666667 0. 1. 1. 1. 1. ] mean value: 0.7333333333333333 key: train_recall value: [1. 0.9047619 1. 1. 1. 0.95 1. 1. 1. 1. ] mean value: 0.9854761904761904 key: test_accuracy value: [0.4 0.6 0.8 0.4 0.8 0.2 0.75 1. 0.5 0.75] mean value: 0.62 key: train_accuracy value: [0.97560976 0.95121951 0.70731707 0.7804878 0.97560976 0.97560976 0.88095238 0.92857143 1. 0.97619048] mean value: 0.9151567944250871 key: test_roc_auc value: [0.41666667 0.58333333 0.83333333 0.33333333 0.83333333 0.25 0.75 1. 0.5 0.75 ] mean value: 0.625 key: train_roc_auc value: [0.975 0.95238095 0.7 0.78571429 0.97619048 0.975 0.88095238 0.92857143 1. 0.97619048] mean value: 0.915 key: test_jcc value: [0.25 0.33333333 0.66666667 0.4 0.66666667 0. 0.66666667 1. 0.5 0.66666667] mean value: 0.5149999999999999 key: train_jcc value: [0.95454545 0.9047619 0.63636364 0.68965517 0.95238095 0.95 0.80769231 0.875 1. 0.95454545] mean value: 0.8724944882703504 MCC on Blind test: -0.05 MCC on Training: 0.25 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: [0.0129261 0.00976014 0.00970173 0.00997019 0.00931287 0.00911713 0.00895834 0.00990105 0.01032019 0.00951266] mean value: 0.009948039054870605 key: score_time value: [0.00934935 0.00955534 0.00973749 0.00854111 0.00894237 0.00947976 0.00929594 0.00918293 0.00990033 0.00897884] mean value: 0.009296345710754394 key: test_mcc value: [ 0. 0.16666667 0.16666667 0.16666667 0.16666667 -0.66666667 -0.57735027 0.57735027 0. 0. ] mean value: 0.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.5 0.5 0.66666667 0.66666667 0.33333333 0.4 0.8 0.5 0.66666667] mean value: 0.5604761904761906 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.5 0.5 0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 0.5 0.5 ] mean value: 0.5066666666666666 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 0.5 0.66666667 0.66666667 0.33333333 0.5 1. 0.5 1. ] mean value: 0.6666666666666666 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( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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( train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.6 0.6 0.6 0.6 0.2 0.25 0.75 0.5 0.5 ] mean value: 0.5 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 0.58333333 0.58333333 0.58333333 0.58333333 0.16666667 0.25 0.75 0.5 0.5 ] mean value: 0.5 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.33333333 0.33333333 0.5 0.5 0.2 0.25 0.66666667 0.33333333 0.5 ] mean value: 0.4016666666666667 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: 0.0 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: [0.55287409 0.54379725 0.61343741 0.55234051 0.57824874 0.53843427 0.58962297 0.59904552 0.57388043 0.50798321] mean value: 0.5649664402008057 key: score_time value: [0.11841536 0.16420984 0.15634918 0.14242959 0.1531136 0.12256479 0.16733861 0.17010379 0.17674661 0.12807941] mean value: 0.14993507862091066 key: test_mcc value: [ 0.16666667 -0.40824829 1. -0.40824829 0.61237244 -0.66666667 0.57735027 1. 0. 0.57735027] mean value: 0.24505763931473198 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. 0.57142857 0.85714286 0.33333333 0.8 1. 0.66666667 0.66666667] mean value: 0.6395238095238096 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 0.75 0.33333333 0.66666667 1. 0.5 1. ] 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. 1. 0.66666667 1. 0.33333333 1. 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 0.4 1. 0.4 0.8 0.2 0.75 1. 0.5 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: [0.58333333 0.33333333 1. 0.33333333 0.75 0.16666667 0.75 1. 0.5 0.75 ] mean value: 0.6166666666666666 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. 0.4 0.75 0.2 0.66666667 1. 0.5 0.5 ] mean value: 0.5349999999999999 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: 0.25 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: [0.84869552 0.9469893 0.85308099 0.84434319 0.85773826 0.85807896 0.85562038 0.89091301 0.8702352 0.84725904] mean value: 0.8672953844070435 key: score_time value: [0.19435334 0.18948293 0.18052363 0.17982292 0.2137394 0.16985703 0.17996979 0.17333531 0.25137925 0.13845086] mean value: 0.18709144592285157 key: test_mcc value: [ 0.16666667 0.61237244 1. 0.16666667 0.61237244 -0.66666667 0.57735027 1. 0. 0.57735027] mean value: 0.4046112076437508 key: train_mcc value: [0.8047619 0.90692382 0.8047619 0.90238095 0.85441771 0.90238095 0.85811633 0.85811633 0.81322028 0.80952381] mean value: 0.8514604000794735 key: test_fscore value: [0.5 0.66666667 1. 0.66666667 0.85714286 0.33333333 0.8 1. 0.66666667 0.66666667] mean value: 0.7157142857142857 key: train_fscore value: [0.9047619 0.95 0.9047619 0.95 0.92307692 0.95 0.93023256 0.92682927 0.9 0.9047619 ] mean value: 0.9244424463794856 key: test_precision value: [0.5 1. 1. 0.66666667 0.75 0.33333333 0.66666667 1. 0.5 1. ] mean value: 0.7416666666666666 key: train_precision value: [0.9047619 1. 0.9047619 0.95 0.94736842 0.95 0.90909091 0.95 0.94736842 0.9047619 ] mean value: 0.9368113465481887 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 0.9047619 0.9047619 0.95 0.9 0.95 0.95238095 0.9047619 0.85714286 0.9047619 ] mean value: 0.9133333333333334 key: test_accuracy value: [0.6 0.8 1. 0.6 0.8 0.2 0.75 1. 0.5 0.75] mean value: 0.7 key: train_accuracy value: [0.90243902 0.95121951 0.90243902 0.95121951 0.92682927 0.95121951 0.92857143 0.92857143 0.9047619 0.9047619 ] mean value: 0.9252032520325203 key: test_roc_auc value: [0.58333333 0.75 1. 0.58333333 0.75 0.16666667 0.75 1. 0.5 0.75 ] mean value: 0.6833333333333333 key: train_roc_auc value: [0.90238095 0.95238095 0.90238095 0.95119048 0.92619048 0.95119048 0.92857143 0.92857143 0.9047619 0.9047619 ] mean value: 0.9252380952380953 key: test_jcc value: [0.33333333 0.5 1. 0.5 0.75 0.2 0.66666667 1. 0.5 0.5 ] mean value: 0.595 key: train_jcc value: [0.82608696 0.9047619 0.82608696 0.9047619 0.85714286 0.9047619 0.86956522 0.86363636 0.81818182 0.82608696] mean value: 0.8601072840203274 MCC on Blind test: -0.13 MCC on Training: 0.4 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: [0.02401781 0.00959349 0.00884938 0.0091269 0.0089922 0.00978351 0.01024103 0.01020026 0.00907898 0.01002502] mean value: 0.01099085807800293 key: score_time value: [0.01877618 0.00848961 0.00889301 0.00845647 0.00908351 0.00914979 0.0093224 0.00906873 0.00942087 0.00936842] mean value: 0.010002899169921874 key: test_mcc value: [-0.16666667 -0.16666667 0.61237244 -0.16666667 0.66666667 -0.66666667 1. 1. 0. 0.57735027] mean value: 0.26897227048854205 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 0.66666667 0.4 0.8 0.33333333 1. 1. 0.66666667 0.8 ] mean value: 0.6466666666666667 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 1. 0.5 1. 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.6666666666666667 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 0.33333333 0.66666667 0.33333333 1. 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 0.8 0.4 0.8 0.2 1. 1. 0.5 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: [0.41666667 0.41666667 0.75 0.41666667 0.83333333 0.16666667 1. 1. 0.5 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: [0.25 0.25 0.5 0.25 0.66666667 0.2 1. 1. 0.5 0.66666667] mean value: 0.5283333333333334 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: 0.27 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: [0.02835059 0.02416348 0.02409005 0.02419853 0.02421737 0.02447224 0.02423191 0.02439237 0.02421069 0.02482486] mean value: 0.024715209007263185 key: score_time value: [0.00838375 0.00844097 0.00840139 0.00842237 0.00849485 0.00844979 0.00832534 0.00857782 0.00840831 0.00849867] mean value: 0.008440327644348145 key: test_mcc value: [-0.16666667 -0.16666667 0.61237244 -0.16666667 0.66666667 -0.66666667 1. 1. 0. 0.57735027] mean value: 0.26897227048854205 key: train_mcc value: [1. 1. 0.8047619 1. 1. 1. 1. 1. 1. 0.80952381] mean value: 0.9614285714285714 key: test_fscore value: [0.4 0.4 0.66666667 0.4 0.8 0.33333333 1. 1. 0.66666667 0.8 ] mean value: 0.6466666666666667 key: train_fscore value: [1. 1. 0.9047619 1. 1. 1. 1. 1. 1. 0.9047619] mean value: 0.980952380952381 key: test_precision value: [0.33333333 0.33333333 1. 0.5 1. 0.33333333 1. 1. 0.5 0.66666667] mean value: 0.6666666666666667 key: train_precision value: [1. 1. 0.9047619 1. 1. 1. 1. 1. 1. 0.9047619] mean value: 0.980952380952381 key: test_recall value: [0.5 0.5 0.5 0.33333333 0.66666667 0.33333333 1. 1. 1. 1. ] mean value: 0.6833333333333333 key: train_recall value: [1. 1. 0.9047619 1. 1. 1. 1. 1. 1. 0.9047619] mean value: 0.980952380952381 key: test_accuracy value: [0.4 0.4 0.8 0.4 0.8 0.2 1. 1. 0.5 0.75] mean value: 0.625 key: train_accuracy value: [1. 1. 0.90243902 1. 1. 1. 1. 1. 1. 0.9047619 ] mean value: 0.9807200929152149 key: test_roc_auc value: [0.41666667 0.41666667 0.75 0.41666667 0.83333333 0.16666667 1. 1. 0.5 0.75 ] mean value: 0.625 key: train_roc_auc value: [1. 1. 0.90238095 1. 1. 1. 1. 1. 1. 0.9047619 ] mean value: 0.9807142857142856 key: test_jcc value: [0.25 0.25 0.5 0.25 0.66666667 0.2 1. 1. 0.5 0.66666667] mean value: 0.5283333333333334 key: train_jcc value: [1. 1. 0.82608696 1. 1. 1. 1. 1. 1. 0.82608696] mean value: 0.9652173913043477 MCC on Blind test: -0.31 MCC on Training: 0.27 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: [0.008358 0.00831342 0.0082972 0.00830984 0.00820947 0.00865245 0.00870299 0.00864148 0.00915098 0.00915289] mean value: 0.008578872680664063 key: score_time value: [0.0083375 0.00837874 0.00833702 0.00835991 0.00863719 0.00874734 0.00867629 0.00871253 0.00896692 0.00917864] mean value: 0.00863320827484131 key: test_mcc value: [-0.40824829 0.16666667 0.16666667 0.66666667 0.16666667 -1. 0.57735027 0.57735027 0.57735027 0.57735027] mean value: 0.20678194529613067 key: train_mcc value: [0.70714286 0.90692382 0.65952381 0.80817439 0.7098505 0.75714286 0.71428571 0.72760688 0.71428571 0.76980036] mean value: 0.7474736906738404 key: test_fscore value: [0. 0.5 0.5 0.8 0.66666667 0. 0.8 0.66666667 0.8 0.66666667] mean value: 0.54 key: train_fscore value: [0.85714286 0.95 0.82926829 0.89473684 0.84210526 0.87804878 0.85714286 0.84210526 0.85714286 0.88888889] mean value: 0.8696581901909244 key: test_precision value: [0. 0.5 0.5 1. 0.66666667 0. 0.66666667 1. 0.66666667 1. ] mean value: 0.6 key: train_precision value: [0.85714286 1. 0.85 0.94444444 0.88888889 0.85714286 0.85714286 0.94117647 0.85714286 0.83333333] mean value: 0.8886414565826332 key: test_recall value: [0. 0.5 0.5 0.66666667 0.66666667 0. 1. 0.5 1. 0.5 ] mean value: 0.5333333333333333 key: train_recall value: [0.85714286 0.9047619 0.80952381 0.85 0.8 0.9 0.85714286 0.76190476 0.85714286 0.95238095] mean value: 0.8549999999999999 key: test_accuracy value: [0.4 0.6 0.6 0.8 0.6 0. 0.75 0.75 0.75 0.75] mean value: 0.6 key: train_accuracy value: [0.85365854 0.95121951 0.82926829 0.90243902 0.85365854 0.87804878 0.85714286 0.85714286 0.85714286 0.88095238] mean value: 0.8720673635307781 key: test_roc_auc value: [0.33333333 0.58333333 0.58333333 0.83333333 0.58333333 0. 0.75 0.75 0.75 0.75 ] mean value: 0.5916666666666666 key: train_roc_auc value: [0.85357143 0.95238095 0.8297619 0.90119048 0.85238095 0.87857143 0.85714286 0.85714286 0.85714286 0.88095238] mean value: 0.8720238095238096 key: test_jcc value: [0. 0.33333333 0.33333333 0.66666667 0.5 0. 0.66666667 0.5 0.66666667 0.5 ] mean value: 0.41666666666666663 key: train_jcc value: [0.75 0.9047619 0.70833333 0.80952381 0.72727273 0.7826087 0.75 0.72727273 0.75 0.8 ] mean value: 0.7709773197816676 MCC on Blind test: 0.12 MCC on Training: 0.21 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: [0.00836658 0.00825715 0.00891399 0.00957584 0.00861096 0.00823355 0.00815821 0.00837421 0.00826669 0.00890207] mean value: 0.008565926551818847 key: score_time value: [0.00836182 0.00851631 0.00847912 0.00961375 0.00854182 0.00819373 0.00820446 0.00824189 0.00828218 0.0082612 ] mean value: 0.008469629287719726 key: test_mcc value: [-0.16666667 0. 1. 0.66666667 0.66666667 -1. 0. 1. 0.57735027 0.57735027] mean value: 0.3321367205045918 key: train_mcc value: [1. 0.23204774 0.62048368 0.95227002 0.95238095 0.80907152 0.2773501 1. 0.90889326 0.90889326] mean value: 0.7661390523375757 key: test_fscore value: [0.4 0.57142857 1. 0.8 0.8 0. 0.66666667 1. 0.8 0.8 ] mean value: 0.6838095238095238 key: train_fscore value: [1. 0.7 0.82352941 0.97435897 0.97560976 0.9047619 0.7 1. 0.95 0.95454545] mean value: 0.8982805501528601 key: test_precision value: [0.33333333 0.4 1. 1. 1. 0. 0.5 1. 0.66666667 0.66666667] mean value: 0.6566666666666667 key: train_precision value: [1. 0.53846154 0.7 1. 0.95238095 0.86363636 0.53846154 1. 1. 0.91304348] mean value: 0.8505983871201263 key: test_recall value: [0.5 1. 1. 0.66666667 0.66666667 0. 1. 1. 1. 1. ] mean value: 0.7833333333333333 key: train_recall value: [1. 1. 1. 0.95 1. 0.95 1. 1. 0.9047619 1. ] mean value: 0.9804761904761905 key: test_accuracy value: [0.4 0.4 1. 0.8 0.8 0. 0.5 1. 0.75 0.75] mean value: 0.64 key: train_accuracy value: [1. 0.56097561 0.7804878 0.97560976 0.97560976 0.90243902 0.57142857 1. 0.95238095 0.95238095] mean value: 0.8671312427409988 key: test_roc_auc value: [0.41666667 0.5 1. 0.83333333 0.83333333 0. 0.5 1. 0.75 0.75 ] mean value: 0.6583333333333333 key: train_roc_auc value: [1. 0.55 0.775 0.975 0.97619048 0.90357143 0.57142857 1. 0.95238095 0.95238095] mean value: 0.865595238095238 key: test_jcc value: [0.25 0.4 1. 0.66666667 0.66666667 0. 0.5 1. 0.66666667 0.66666667] mean value: 0.5816666666666667 key: train_jcc value: [1. 0.53846154 0.7 0.95 0.95238095 0.82608696 0.53846154 1. 0.9047619 0.91304348] mean value: 0.8323196368848542 MCC on Blind test: 0.04 MCC on Training: 0.33 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: [0.07816386 0.03662848 0.03439021 0.033813 0.03872108 0.05464959 0.03403544 0.03438354 0.03463316 0.03474498] mean value: 0.041416335105896 key: score_time value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:427: 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:454: 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' [0.01122093 0.01033044 0.00993037 0.00991368 0.00994444 0.01005077 0.01007438 0.01027513 0.01007533 0.01037931] mean value: 0.010219478607177734 key: test_mcc value: [-0.16666667 0.16666667 1. 0.66666667 0.16666667 0.40824829 0.57735027 0. 0. 1. ] mean value: 0.3818931892986822 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.5 1. 0.8 0.66666667 0.5 0.8 0.5 0.66666667 1. ] mean value: 0.6833333333333333 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.5 1. 1. 0.66666667 1. 0.66666667 0.5 0.5 1. ] 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 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: [0.4 0.6 1. 0.8 0.6 0.6 0.75 0.5 0.5 1. ] mean value: 0.675 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 0.58333333 1. 0.83333333 0.58333333 0.66666667 0.75 0.5 0.5 1. ] mean value: 0.6833333333333333 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 0.33333333 1. 0.66666667 0.5 0.33333333 0.66666667 0.33333333 0.5 1. ] mean value: 0.5583333333333333 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: 15 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 23 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: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall'] 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 ======================================================= Traceback (most recent call last): File "/home/tanu/git/LSHTM_analysis/scripts/ml/./ml_iterator.py", line 107, in out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+ out_filename), index = False) File "/home/tanu/.local/lib/python3.9/site-packages/pandas/core/generic.py", line 3563, in to_csv return DataFrameRenderer(formatter).to_csv( File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/formats/format.py", line 1180, in to_csv csv_formatter.save() File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/formats/csvs.py", line 241, in save with get_handle( File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/common.py", line 697, in get_handle check_parent_directory(str(handle)) File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/common.py", line 571, in check_parent_directory raise OSError(fr"Cannot save file into a non-existent directory: '{parent}'") OSError: Cannot save file into a non-existent directory: '/home/tanu/git/Data/ml_combined/genes/home/tanu/git/LSHTM_ML/output/genes' Building estimator 8 of 8 for this parallel run (total 100)... 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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 6 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 2 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 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 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 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 6 of 8 for this parallel run (total 100)... 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 3 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 2 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 4 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 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 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 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 4 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 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 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 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)... V?0*D?xqZ|¿y]#bJ$?]3f?-C6z?[ |#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 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 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 6 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 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 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 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 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 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 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 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 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 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 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 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 7 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 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 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 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 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 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 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 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 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 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 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 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 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 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 6 of 9 for this parallel run (total 100)... 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 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 2 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 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 6 of 8 for this parallel run (total 100)... 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 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 3 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 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 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 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 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (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 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 8 for this parallel run (total 100)... 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 9 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 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 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 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)... RfjURfjURfjU0~;iU`~;iUp~;iU:jU:jU:jUSfjU0SfjU@SfjU `RfjUL"?27=<=p `p};iUૢPhUBuilding estimator 3 of 9 for this parallel run (total 100)... 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 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 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 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 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 4 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 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 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 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 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 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)... 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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 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 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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 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 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 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 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 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 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 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 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 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 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)...