27210 lines
1.2 MiB
27210 lines
1.2 MiB
/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.
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from pandas import MultiIndex, Int64Index
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1.22.4
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1.4.1
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1.22.4
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1.4.1
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Gene: pncA
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Drug: pyrazinamide
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aaindex_df contains non-numerical data
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Total no. of non-numerial columns: 2
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Selecting numerical data only
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PASS: successfully selected numerical columns only for aaindex_df
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Now checking for NA in the remaining aaindex_cols
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Counting aaindex_df cols with NA
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ncols with NA: 4 columns
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Dropping these...
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Original ncols: 127
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Revised df ncols: 123
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Checking NA in revised df...
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PASS: cols with NA successfully dropped from aaindex_df
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Proceeding with combining aa_df with other features_df
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PASS: ncols match
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Expected ncols: 123
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Got: 123
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Total no. of columns in clean aa_df: 123
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Proceeding to merge, expected nrows in merged_df: 424
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PASS: my_features_df and aa_df successfully combined
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nrows: 424
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ncols: 267
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count of NULL values before imputation
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or_mychisq 102
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log10_or_mychisq 102
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dtype: int64
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count of NULL values AFTER imputation
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mutationinformation 0
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or_rawI 0
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logorI 0
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dtype: int64
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PASS: OR values imputed, data ready for ML
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Total no. of features for aaindex: 123
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Genomic features being used EXCLUDING odds ratio (n): 5
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These are: ['maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique']
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dst column exists
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and this is identical to drug column: pyrazinamide
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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']
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PASS: but NOT writing mask file
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PASS: But NOT writing processed file
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#################################################################
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SUCCESS: Extacted training data for gene: pnca
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Dim of training_df: (424, 173)
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This EXCLUDES Odds Ratio
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############################################################
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Input params:
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Dim of input df: (424, 173)
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Data type to split: actual
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Split type: 70_30
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target colname: dst_mode
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oversampling enabled
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PASS: x_features has no target variable and no dst column
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Dropped cols: 2
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These were: dst_mode and dst
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No. of cols in input df: 173
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No.of cols dropped: 2
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No. of columns for x_features: 171
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-------------------------------------------------------------
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Successfully generated training and test data:
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Data used: actual
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Split type: 70_30
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Total no. of input features: 171
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--------No. of numerical features: 165
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--------No. of categorical features: 6
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===========================
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Resampling: NONE
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Baseline
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===========================
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Total data size: 69
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Train data size: (46, 171)
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y_train numbers: Counter({0: 23, 1: 23})
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Test data size: (23, 171)
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y_test_numbers: Counter({0: 12, 1: 11})
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y_train ratio: 1.0
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y_test ratio: 1.0909090909090908
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-------------------------------------------------------------
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Simple Random OverSampling
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Counter({0: 23, 1: 23})
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(46, 171)
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Simple Random UnderSampling
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Counter({0: 23, 1: 23})
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(46, 171)
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Simple Combined Over and UnderSampling
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Counter({0: 23, 1: 23})
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(46, 171)
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SMOTE_NC OverSampling
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Counter({0: 23, 1: 23})
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(46, 171)
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Generated Resampled data as below:
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=================================
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Resampling: Random oversampling
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================================
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Train data size: (46, 171)
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y_train numbers: 46
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y_train ratio: 1.0
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y_test ratio: 1.0909090909090908
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================================
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Resampling: Random underampling
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================================
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||
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Train data size: (46, 171)
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y_train numbers: 46
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y_train ratio: 1.0
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y_test ratio: 1.0909090909090908
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================================
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Resampling:Combined (over+under)
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================================
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||
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Train data size: (46, 171)
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y_train numbers: 46
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y_train ratio: 1.0
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y_test ratio: 1.0909090909090908
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==============================
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Resampling: Smote NC
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==============================
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Train data size: (46, 171)
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y_train numbers: 46
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y_train ratio: 1.0
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y_test ratio: 1.0909090909090908
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-------------------------------------------------------------
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==============================================================
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Running several classification models (n): 24
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List of models:
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('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
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('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
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verbose=3))
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('Decision Tree', DecisionTreeClassifier(random_state=42))
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('Extra Tree', ExtraTreeClassifier(random_state=42))
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('Extra Trees', ExtraTreesClassifier(random_state=42))
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('Gradient Boosting', GradientBoostingClassifier(random_state=42))
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('Gaussian NB', GaussianNB())
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('Gaussian Process', GaussianProcessClassifier(random_state=42))
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('K-Nearest Neighbors', KNeighborsClassifier())
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('LDA', LinearDiscriminantAnalysis())
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('Logistic Regression', LogisticRegression(random_state=42))
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('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
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('MLP', MLPClassifier(max_iter=500, random_state=42))
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('Multinomial', MultinomialNB())
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('Naive Bayes', BernoulliNB())
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('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
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('QDA', QuadraticDiscriminantAnalysis())
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('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
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('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
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n_estimators=1000, n_jobs=12, oob_score=True,
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random_state=42))
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('Ridge Classifier', RidgeClassifier(random_state=42))
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('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
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('SVC', SVC(random_state=42))
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('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
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/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.
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_warn_prf(average, modifier, msg_start, len(result))
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('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
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colsample_bynode=None, colsample_bytree=None,
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enable_categorical=False, gamma=None, gpu_id=None,
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importance_type=None, interaction_constraints=None,
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learning_rate=None, max_delta_step=None, max_depth=None,
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min_child_weight=None, missing=nan, monotone_constraints=None,
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n_estimators=100, n_jobs=12, num_parallel_tree=None,
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predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
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scale_pos_weight=None, subsample=None, tree_method=None,
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use_label_encoder=False, validate_parameters=None, verbosity=0))
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================================================================
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Running classifier: 1
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Model_name: AdaBoost Classifier
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Model func: AdaBoostClassifier(random_state=42)
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Running model pipeline: Pipeline(steps=[('prep',
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ColumnTransformer(remainder='passthrough',
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transformers=[('num', MinMaxScaler(),
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Index(['consurf_score', 'snap2_score', 'provean_score',
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'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
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'contacts', 'electro_rr', 'electro_mm',
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...
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'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
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'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
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'lineage_count_unique'],
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dtype='object', length=165)),
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('cat', OneHotEncoder(),
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Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
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'polarity_change', 'water_change', 'active_site'],
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dtype='object'))])),
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('model', AdaBoostClassifier(random_state=42))])
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key: fit_time
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value: [0.06783319 0.06749201 0.06719685 0.06756043 0.06702876 0.06712174
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0.06725192 0.06728387 0.06741643 0.06739259]
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mean value: 0.06735777854919434
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||
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key: score_time
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value: [0.01468825 0.01459002 0.01462436 0.01456857 0.01446748 0.01512146
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0.01455379 0.01437521 0.01465225 0.01447845]
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mean value: 0.01461198329925537
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||
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key: test_mcc
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value: [-0.16666667 -0.16666667 0. 0.16666667 0.16666667 0.
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0.57735027 1. 0. 0.57735027]
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mean value: 0.21547005383792514
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||
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key: train_mcc
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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mean value: 1.0
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||
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key: test_fscore
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value: [0.4 0.4 0.57142857 0.66666667 0.66666667 0.
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0.66666667 1. 0.66666667 0.8 ]
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mean value: 0.5838095238095238
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||
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key: train_fscore
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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||
|
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mean value: 1.0
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||
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key: test_precision
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value: [0.33333333 0.33333333 0.4 0.66666667 0.66666667 0.
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1. 1. 0.5 0.66666667]
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||
|
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mean value: 0.5566666666666668
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||
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key: train_precision
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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mean value: 1.0
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||
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key: test_recall
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value: [0.5 0.5 1. 0.66666667 0.66666667 0.
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0.5 1. 1. 1. ]
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mean value: 0.6833333333333333
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||
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key: train_recall
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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mean value: 1.0
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||
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key: test_accuracy
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value: [0.4 0.4 0.4 0.6 0.6 0.4 0.75 1. 0.5 0.75]
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mean value: 0.58
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||
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key: train_accuracy
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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mean value: 1.0
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||
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key: test_roc_auc
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value: [0.41666667 0.41666667 0.5 0.58333333 0.58333333 0.5
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0.75 1. 0.5 0.75 ]
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mean value: 0.6
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||
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key: train_roc_auc
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value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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mean value: 1.0
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||
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key: test_jcc
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value: [0.25 0.25 0.4 0.5 0.5 0.
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0.5 1. 0.5 0.66666667]
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||
|
||
mean value: 0.45666666666666667
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||
|
||
key: train_jcc
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||
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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||
|
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mean value: 1.0
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||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
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|
||
[Parallel(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: 0.1s
|
||
[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: 0.1s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[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: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s
|
||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished
|
||
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
|
||
[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s
|
||
[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 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 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
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||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
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
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s
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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s
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||
[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished
|
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[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
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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s
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||
[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)...
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||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
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||
Building estimator 5 of 9 for this parallel run (total 100)...
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||
Building estimator 6 of 9 for this parallel run (total 100)...
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||
Building estimator 7 of 9 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)...
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||
Building estimator 1 of 8 for this parallel run (total 100)...
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||
Building estimator 2 of 8 for this parallel run (total 100)...
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||
Building estimator 3 of 8 for this parallel run (total 100)...
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||
Building estimator 4 of 8 for this parallel run (total 100)...
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||
Building estimator 5 of 8 for this parallel run (total 100)...
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||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 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 1 of 8 for this parallel run (total 100)...
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||
Building estimator 2 of 8 for this parallel run (total 100)...
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||
Building estimator 3 of 8 for this parallel run (total 100)...
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||
Building estimator 4 of 8 for this parallel run (total 100)...
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||
Building estimator 5 of 8 for this parallel run (total 100)...
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||
Building estimator 6 of 8 for this parallel run (total 100)...
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||
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 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
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||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
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||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
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)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
Building estimator 8 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 6 of 9 for this parallel run (total 100)...
|
||
Building estimator 7 of 9 for this parallel run (total 100)...
|
||
Building estimator 8 of 9 for this parallel run (total 100)...
|
||
Building estimator 9 of 9 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 6 of 8 for this parallel run (total 100)...
|
||
Building estimator 7 of 8 for this parallel run (total 100)...
|
||
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 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 9 for this parallel run (total 100)...
|
||
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 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 1 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 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 5 of 8 for this parallel run (total 100)...
|
||
Building estimator 2 of 8 for this parallel run (total 100)...
|
||
Building estimator 5 of 9 for this parallel run (total 100)...
|
||
Building estimator 4 of 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 4 of 9 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 3 of 8 for this parallel run (total 100)...
|
||
Building estimator 4 of 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 3 of 8 for this parallel run (total 100)...
|
||
[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)]: 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 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)...
|
||
|