LSHTM_analysis/scripts/ml/genes_ml_logs/log_genes_ml.txt

/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
  from pandas import MultiIndex, Int64Index
1.22.4
1.4.1
1.22.4
1.4.1

Gene: pncA 
Drug: pyrazinamide

aaindex_df contains non-numerical data

Total no. of non-numerial columns: 2

Selecting numerical data only

PASS: successfully selected numerical columns only for aaindex_df

Now checking for NA in the remaining aaindex_cols

Counting aaindex_df cols with NA 
ncols with NA: 4 columns 
Dropping these... 
Original ncols: 127

Revised df ncols: 123

Checking NA in revised df...

PASS: cols with NA successfully dropped from aaindex_df 
Proceeding with combining aa_df with other features_df

PASS: ncols match 
Expected ncols: 123 
Got: 123

Total no. of columns in clean aa_df: 123

Proceeding to merge, expected nrows in merged_df: 424

PASS: my_features_df and aa_df successfully combined 
nrows: 424 
ncols: 267
count of NULL values before imputation

or_mychisq          102
log10_or_mychisq    102
dtype: int64
count of NULL values AFTER imputation

mutationinformation    0
or_rawI                0
logorI                 0
dtype: int64

PASS: OR values imputed, data ready for ML

Total no. of features for aaindex: 123

Genomic features being used EXCLUDING odds ratio (n): 5 
These are: ['maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique']

dst column exists

and this is identical to drug column: pyrazinamide

All feature names: ['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss', 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss', 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss', 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss', 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss', 'volumetric_rr', 'volumetric_mm', 'volumetric_ss', 'ligand_distance', 'ligand_affinity_change', 'mmcsm_lig', 'ALTS910101', 'AZAE970101', 'AZAE970102', 'BASU010101', 'BENS940101', 'BENS940102', 'BENS940103', 'BENS940104', 'BETM990101', 'BLAJ010101', 'BONM030101', 'BONM030102', 'BONM030103', 'BONM030104', 'BONM030105', 'BONM030106', 'BRYS930101', 'CROG050101', 'CSEM940101', 'DAYM780301', 'DAYM780302', 'DOSZ010101', 'DOSZ010102', 'DOSZ010103', 'DOSZ010104', 'FEND850101', 'FITW660101', 'GEOD900101', 'GIAG010101', 'GONG920101', 'GRAR740104', 'HENS920101', 'HENS920102', 'HENS920103', 'HENS920104', 'JOHM930101', 'JOND920103', 'JOND940101', 'KANM000101', 'KAPO950101', 'KESO980101', 'KESO980102', 'KOLA920101', 'KOLA930101', 'KOSJ950100_RSA_SST', 'KOSJ950100_SST', 'KOSJ950110_RSA', 'KOSJ950115', 'LEVJ860101', 'LINK010101', 'LIWA970101', 'LUTR910101', 'LUTR910102', 'LUTR910103', 'LUTR910104', 'LUTR910105', 'LUTR910106', 'LUTR910107', 'LUTR910108', 'LUTR910109', 'MCLA710101', 'MCLA720101', 'MEHP950102', 'MICC010101', 'MIRL960101', 'MIYS850102', 'MIYS850103', 'MIYS930101', 'MIYS960101', 'MIYS960102', 'MIYS960103', 'MIYS990106', 'MIYS990107', 'MIYT790101', 'MOHR870101', 'MOOG990101', 'MUET010101', 'MUET020101', 'MUET020102', 'NAOD960101', 'NGPC000101', 'NIEK910101', 'NIEK910102', 'OGAK980101', 'OVEJ920100_RSA', 'OVEJ920101', 'OVEJ920102', 'OVEJ920103', 'PRLA000101', 'PRLA000102', 'QUIB020101', 'QU_C930101', 'QU_C930102', 'QU_C930103', 'RIER950101', 'RISJ880101', 'RUSR970101', 'RUSR970102', 'RUSR970103', 'SIMK990101', 'SIMK990102', 'SIMK990103', 'SIMK990104', 'SIMK990105', 'SKOJ000101', 'SKOJ000102', 'SKOJ970101', 'TANS760101', 'TANS760102', 'THOP960101', 'TOBD000101', 'TOBD000102', 'TUDE900101', 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique', 'dst', 'dst_mode']

PASS: but NOT writing mask file

PASS: But NOT writing processed file

################################################################# 
SUCCESS: Extacted training data for gene: pnca 
Dim of training_df: (424, 173)

This EXCLUDES Odds Ratio 
############################################################

Input params: 
Dim of input df: (424, 173) 
Data type to split: actual 
Split type: 70_30 
target colname: dst_mode

oversampling enabled

PASS: x_features has no target variable and no dst column 
Dropped cols: 2 
These were: dst_mode and dst

No. of cols in input df: 173 
No.of cols dropped: 2 
No. of columns for x_features: 171

------------------------------------------------------------- 
Successfully generated training and test data: 
Data used: actual 
Split type: 70_30 

Total no. of input features: 171 
--------No. of numerical features: 165 
--------No. of categorical features: 6 
=========================== 
 Resampling: NONE 
Baseline 
=========================== 

Total data size: 69 

Train data size: (46, 171) 
y_train numbers: Counter({0: 23, 1: 23}) 

Test data size: (23, 171) 
y_test_numbers: Counter({0: 12, 1: 11}) 

y_train ratio: 1.0 
y_test ratio: 1.0909090909090908 
-------------------------------------------------------------

Simple Random OverSampling
 Counter({0: 23, 1: 23})
(46, 171)

Simple Random UnderSampling
 Counter({0: 23, 1: 23})
(46, 171)

Simple Combined Over and UnderSampling
 Counter({0: 23, 1: 23})
(46, 171)

SMOTE_NC OverSampling
 Counter({0: 23, 1: 23})
(46, 171)

Generated Resampled data as below: 
================================= 
Resampling: Random oversampling 
================================ 

Train data size: (46, 171) 
y_train numbers: 46 

y_train ratio: 1.0 
y_test ratio: 1.0909090909090908 
================================ 
Resampling: Random underampling 
================================ 

Train data size: (46, 171) 
y_train numbers: 46 

y_train ratio: 1.0 
y_test ratio: 1.0909090909090908 
================================ 
Resampling:Combined (over+under) 
================================ 

Train data size: (46, 171) 
y_train numbers: 46 

y_train ratio: 1.0 
y_test ratio: 1.0909090909090908 
============================== 
Resampling: Smote NC 
============================== 

Train data size: (46, 171) 
y_train numbers: 46 

y_train ratio: 1.0 
y_test ratio: 1.0909090909090908 
-------------------------------------------------------------

==============================================================
 
Running several classification models (n): 24 
List of models:
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3))
('Decision Tree', DecisionTreeClassifier(random_state=42))
('Extra Tree', ExtraTreeClassifier(random_state=42))
('Extra Trees', ExtraTreesClassifier(random_state=42))
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
('Gaussian NB', GaussianNB())
('Gaussian Process', GaussianProcessClassifier(random_state=42))
('K-Nearest Neighbors', KNeighborsClassifier())
('LDA', LinearDiscriminantAnalysis())
('Logistic Regression', LogisticRegression(random_state=42))
('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
('MLP', MLPClassifier(max_iter=500, random_state=42))
('Multinomial', MultinomialNB())
('Naive Bayes', BernoulliNB())
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
('QDA', QuadraticDiscriminantAnalysis())
('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42))
('Ridge Classifier', RidgeClassifier(random_state=42))
('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
('SVC', SVC(random_state=42))
('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0))

================================================================


Running classifier: 1 
Model_name: AdaBoost Classifier 
Model func: AdaBoostClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', AdaBoostClassifier(random_state=42))])

key: fit_time 
value: [0.06783319 0.06749201 0.06719685 0.06756043 0.06702876 0.06712174
 0.06725192 0.06728387 0.06741643 0.06739259]

mean value: 0.06735777854919434

key: score_time 
value: [0.01468825 0.01459002 0.01462436 0.01456857 0.01446748 0.01512146
 0.01455379 0.01437521 0.01465225 0.01447845]

mean value: 0.01461198329925537

key: test_mcc 
value: [-0.16666667 -0.16666667  0.          0.16666667  0.16666667  0.
  0.57735027  1.          0.          0.57735027]

mean value: 0.21547005383792514

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.4        0.57142857 0.66666667 0.66666667 0.
 0.66666667 1.         0.66666667 0.8       ]

mean value: 0.5838095238095238

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.33333333 0.4        0.66666667 0.66666667 0.
 1.         1.         0.5        0.66666667]

mean value: 0.5566666666666668

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.4  0.6  0.6  0.4  0.75 1.   0.5  0.75]

mean value: 0.58

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.41666667 0.5        0.58333333 0.58333333 0.5
 0.75       1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.25       0.4        0.5        0.5        0.
 0.5        1.         0.5        0.66666667]

mean value: 0.45666666666666667

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[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:    0.3s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[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)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
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)...
<00>?<3F>	0<>{%<25>0<>{%<25>Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
?E?<0F>?<3F>6l?<3F><>?t<>E?<3F><>"?<3F><>"? ?<3F><>J?UU5?UU?;<3B>?O<>D?ى?O<>D?<3F>]	?^<5E>Z?<3F><>?<3F>]	?<3F>U?>
є=<3D>E><3E><>b<0<>??<3F>\?!<21>?<3F><>l?<3F>><3E>>R<>k?<3F>\G?<3F>rx?<3F>.:=J<>'?]tQ?<3F>o<B6>?<3F>=
>O#,?<3F><><84>>><3E><>=ӰQ?<00>?<3F><>T?<3F><>I?Ez<45>><3E>(?<3F>@<40>>ï<>>?@??@?<3F><>?<3F><>?<3F><>?<3F><>?]x	?<3F><>B?L<>(?<06>;?<06><>><3E><>"?<3F>D-?0<>=?<3F>9&?{<7B>:?H<>7?<3F><>L?iE?<3F><>i?<3F>#0?]6?:?Ֆ+?<3F><>?<3F>yh?5i?T<>P?<3F>;7?<3F>[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
dfs successfully combined 
nrows in combined_df_wf: 48 
ncols in combined_df_wf: 8

PASS: proceeding to merge metadata with CV and BT dfs

Adding column: Model_name

========================================================= 
SUCCESS: Ran multiple classifiers 
=======================================================

==============================================================
 
Running several classification models (n): 24 
List of models:
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3))
('Decision Tree', DecisionTreeClassifier(random_state=42))
('Extra Tree', ExtraTreeClassifier(random_state=42))
('Extra Trees', ExtraTreesClassifier(random_state=42))
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
('Gaussian NB', GaussianNB())
('Gaussian Process', GaussianProcessClassifier(random_state=42))
('K-Nearest Neighbors', KNeighborsClassifier())
('LDA', LinearDiscriminantAnalysis())
('Logistic Regression', LogisticRegression(random_state=42))
('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
('MLP', MLPClassifier(max_iter=500, random_state=42))
('Multinomial', MultinomialNB())
('Naive Bayes', BernoulliNB())
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
('QDA', QuadraticDiscriminantAnalysis())
('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42))
('Ridge Classifier', RidgeClassifier(random_state=42))
('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
('SVC', SVC(random_state=42))
('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0))

================================================================


Running classifier: 1 
Model_name: AdaBoost Classifier 
Model func: AdaBoostClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', AdaBoostClassifier(random_state=42))])

key: fit_time 
value: [0.07132602 0.06682777 0.06677747 0.06710386 0.06674194 0.06700015
 0.06825066 0.06934452 0.06717873 0.06770229]

mean value: 0.0678253412246704

key: score_time 
value: [0.01529694 0.01437569 0.0144577  0.01442981 0.01447797 0.01481962
 0.01443458 0.01433897 0.0144999  0.01434636]

mean value: 0.01454775333404541

key: test_mcc 
value: [-0.16666667 -0.16666667  0.          0.16666667  0.16666667  0.
  0.57735027  1.          0.          0.57735027]

mean value: 0.21547005383792514

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.4        0.57142857 0.66666667 0.66666667 0.
 0.66666667 1.         0.66666667 0.8       ]

mean value: 0.5838095238095238

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.33333333 0.4        0.66666667 0.66666667 0.
 1.         1.         0.5        0.66666667]

mean value: 0.5566666666666668

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.4  0.6  0.6  0.4  0.75 1.   0.5  0.75]

mean value: 0.58

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.41666667 0.5        0.58333333 0.58333333 0.5
 0.75       1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.25       0.4        0.5        0.5        0.
 0.5        1.         0.5        0.66666667]

mean value: 0.45666666666666667

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.03

MCC on Training: 0.22

Running classifier: 2 
Model_name: Bagging Classifier 
Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True,
                                   random_state=42, verbose=3))])

key: fit_time 
value: [0.12906003 0.10704207 0.12686491 0.12080836 0.10430932 0.10011482
 0.12024331 0.12664056 0.12828541 0.11073399]

mean value: 0.1174102783203125

key: score_time 
value: [0.03597808 0.05087709 0.05140519 0.05607057 0.03567457 0.07094002
 0.04233408 0.07256484 0.06659627 0.04438996]

mean value: 0.05268306732177734

key: test_mcc 
value: [ 0.16666667 -0.16666667  0.         -0.66666667  1.          0.40824829
  1.          0.57735027  0.57735027  0.57735027]

mean value: 0.3473632431366074

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.4        0.57142857 0.33333333 1.         0.5
 1.         0.66666667 0.8        0.8       ]

mean value: 0.6571428571428571

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.33333333 0.4        0.33333333 1.         1.
 1.         1.         0.66666667 0.66666667]

mean value: 0.6900000000000001

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 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 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 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 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 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 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.2s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.6s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    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
[0.5        0.5        1.         0.33333333 1.         0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.4  0.4  0.2  1.   0.6  1.   0.75 0.75 0.75]

mean value: 0.645

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.41666667 0.5        0.16666667 1.         0.66666667
 1.         0.75       0.75       0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.25       0.4        0.2        1.         0.33333333
 1.         0.5        0.66666667 0.66666667]

mean value: 0.535

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.35

Running classifier: 3 
Model_name: Decision Tree 
Model func: DecisionTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', DecisionTreeClassifier(random_state=42))])

key: fit_time 
value: [0.00995827 0.01006293 0.00977159 0.00960875 0.00932837 0.01003432
 0.01015401 0.01002192 0.01014709 0.01005244]

mean value: 0.009913969039916991

key: score_time 
value: [0.00937915 0.0091207  0.00905037 0.00908828 0.00868917 0.00928426
 0.00929546 0.0092063  0.00931859 0.00913453]

mean value: 0.0091566801071167

key: test_mcc 
value: [ 0.61237244  0.61237244  0.         -0.66666667 -0.40824829  0.40824829
  0.          0.          0.57735027  0.57735027]

mean value: 0.17127787431041744

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 0.57142857 0.33333333 0.57142857 0.5
 0.5        0.5        0.8        0.8       ]

mean value: 0.590952380952381

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         0.4        0.33333333 0.5        1.
 0.5        0.5        0.66666667 0.66666667]

mean value: 0.6566666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  0.4  0.2  0.4  0.6  0.5  0.5  0.75 0.75]

mean value: 0.5700000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       0.5        0.16666667 0.33333333 0.66666667
 0.5        0.5        0.75       0.75      ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        0.4        0.2        0.4        0.33333333
 0.33333333 0.33333333 0.66666667 0.66666667]

mean value: 0.4333333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.21

MCC on Training: 0.17

Running classifier: 4 
Model_name: Extra Tree 
Model func: ExtraTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreeClassifier(random_state=42))])

key: fit_time 
value: [0.00974703 0.00838304 0.00848198 0.00882292 0.00814939 0.00913811
 0.00892091 0.00848198 0.008219   0.00814247]

mean value: 0.008648681640625

key: score_time 
value: [0.00833654 0.00855279 0.00859737 0.00846529 0.00859642 0.00892782
 0.00907707 0.00840664 0.00827885 0.00861287]

mean value: 0.008585166931152344

key: test_mcc 
value: [ 0.61237244 -0.40824829  0.40824829 -0.40824829  0.61237244  0.66666667
  1.          0.57735027  0.          0.        ]

mean value: 0.30605135167840186

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.         0.66666667 0.57142857 0.85714286 0.8
 1.         0.8        0.5        0.5       ]

mean value: 0.6361904761904762

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.         0.5        0.5        0.75       1.
 1.         0.66666667 0.5        0.5       ]

mean value: 0.6416666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.66666667
 1.         1.         0.5        0.5       ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.4  0.6  0.4  0.8  0.8  1.   0.75 0.5  0.5 ]

mean value: 0.655

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.33333333 0.66666667 0.33333333 0.75       0.83333333
 1.         0.75       0.5        0.5       ]

mean value: 0.6416666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.         0.5        0.4        0.75       0.66666667
 1.         0.66666667 0.33333333 0.33333333]

mean value: 0.5149999999999999

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.14

MCC on Training: 0.31

Running classifier: 5 
Model_name: Extra Trees 
Model func: ExtraTreesClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreesClassifier(random_state=42))])

key: fit_time 
value: [0.076051   0.08050394 0.07719064 0.08136129 0.07856488 0.08007026
 0.08113217 0.08063841 0.08339477 0.08117771]

mean value: 0.08000850677490234

key: score_time 
value: [0.01741648 0.01722956 0.01732183 0.01721096 0.01760364 0.01814914
 0.01826596 0.01826811 0.01760626 0.01790762]

mean value: 0.017697954177856447

key: test_mcc 
value: [ 0.16666667 -0.66666667  0.66666667 -0.40824829  0.61237244  0.40824829
  0.57735027  0.57735027  0.57735027 -0.57735027]

mean value: 0.19337396407417132

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.8        0.57142857 0.85714286 0.5
 0.66666667 0.66666667 0.8        0.4       ]

mean value: 0.5761904761904761

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.66666667 0.5        0.75       1.
 1.         1.         0.66666667 0.33333333]

mean value: 0.6416666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        0.5        1.         0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.8  0.4  0.8  0.6  0.75 0.75 0.75 0.25]

mean value: 0.5900000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.16666667 0.83333333 0.33333333 0.75       0.66666667
 0.75       0.75       0.75       0.25      ]

mean value: 0.5833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.66666667 0.4        0.75       0.33333333
 0.5        0.5        0.66666667 0.25      ]

mean value: 0.43999999999999995

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.15

MCC on Training: 0.19

Running classifier: 6 
Model_name: Gradient Boosting 
Model func: GradientBoostingClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GradientBoostingClassifier(random_state=42))])

key: fit_time 
value: [0.13448787 0.12742686 0.11794376 0.1357913  0.12634826 0.11400104
 0.12737584 0.12645459 0.13348007 0.11710906]

mean value: 0.12604186534881592

key: score_time 
value: [0.00984979 0.00985265 0.01020741 0.0097754  0.00925779 0.00990605
 0.0094161  0.00989556 0.00962567 0.00884724]

mean value: 0.00966336727142334

key: test_mcc 
value: [0.61237244 0.61237244 0.         0.16666667 0.16666667 0.40824829
 0.57735027 0.         0.57735027 0.57735027]

mean value: 0.36983773027576633

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 0.57142857 0.66666667 0.66666667 0.5
 0.66666667 0.5        0.8        0.8       ]

mean value: 0.6504761904761904

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         0.4        0.66666667 0.66666667 1.
 1.         0.5        0.66666667 0.66666667]

mean value: 0.7566666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  0.4  0.6  0.6  0.6  0.75 0.5  0.75 0.75]

mean value: 0.655

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       0.5        0.58333333 0.58333333 0.66666667
 0.75       0.5        0.75       0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        0.4        0.5        0.5        0.33333333
 0.5        0.33333333 0.66666667 0.66666667]

mean value: 0.49000000000000005

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.48

MCC on Training: 0.37

Running classifier: 7 
Model_name: Gaussian NB 
Model func: GaussianNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianNB())])

key: fit_time 
value: [0.00802517 0.00784039 0.00806189 0.00785351 0.00780725 0.00784564
 0.00790715 0.00786304 0.00797224 0.00807905]

mean value: 0.00792553424835205

key: score_time 
value: [0.00857687 0.00819349 0.00826335 0.00834155 0.00818014 0.00822473
 0.00831079 0.00825834 0.00824285 0.00824022]

mean value: 0.008283233642578125

key: test_mcc 
value: [-0.16666667  0.          0.          0.61237244  0.          0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.34313043286826167

key: train_mcc 
value: [0.57570364 0.698212   0.63496528 0.49692935 0.59982886 0.63994524
 0.56652882 0.52704628 0.54659439 0.60609153]

mean value: 0.5891845382894122

key: test_fscore 
value: [0.4        0.57142857 0.57142857 0.85714286 0.75       0.5
 1.         1.         0.66666667 0.8       ]

mean value: 0.7116666666666667

key: train_fscore 
value: [0.80851064 0.85714286 0.83333333 0.76595745 0.80851064 0.82608696
 0.8        0.78431373 0.79166667 0.81632653]

mean value: 0.8091848793171292

key: test_precision 
value: [0.33333333 0.4        0.4        0.75       0.6        1.
 1.         1.         0.5        0.66666667]

mean value: 0.665

key: train_precision 
value: [0.73076923 0.75       0.74074074 0.66666667 0.7037037  0.73076923
 0.68965517 0.66666667 0.7037037  0.71428571]

mean value: 0.7096960829719451

key: test_recall 
value: [0.5        1.         1.         1.         1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.8833333333333332

key: train_recall 
value: [0.9047619  1.         0.95238095 0.9        0.95       0.95
 0.95238095 0.95238095 0.9047619  0.95238095]

mean value: 0.9419047619047619

key: test_accuracy 
value: [0.4  0.4  0.4  0.8  0.6  0.6  1.   1.   0.5  0.75]

mean value: 0.645

key: train_accuracy 
value: [0.7804878  0.82926829 0.80487805 0.73170732 0.7804878  0.80487805
 0.76190476 0.73809524 0.76190476 0.78571429]

mean value: 0.7779326364692218

key: test_roc_auc 
value: [0.41666667 0.5        0.5        0.75       0.5        0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [0.77738095 0.825      0.80119048 0.73571429 0.78452381 0.80833333
 0.76190476 0.73809524 0.76190476 0.78571429]

mean value: 0.7779761904761904

key: test_jcc 
value: [0.25       0.4        0.4        0.75       0.6        0.33333333
 1.         1.         0.5        0.66666667]

mean value: 0.5900000000000001

key: train_jcc 
value: [0.67857143 0.75       0.71428571 0.62068966 0.67857143 0.7037037
 0.66666667 0.64516129 0.65517241 0.68965517]

mean value: 0.6802477473500833

MCC on Blind test: -0.03

MCC on Training: 0.34

Running classifier: 8 
Model_name: Gaussian Process 
Model func: GaussianProcessClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianProcessClassifier(random_state=42))])

key: fit_time 
value: [0.00984263 0.00996685 0.01091576 0.01114798 0.00994277 0.00994539
 0.00990534 0.01119637 0.01126266 0.01121879]

mean value: 0.010534453392028808

key: score_time 
value: [0.00853729 0.00851989 0.00944638 0.00902963 0.00861883 0.00865293
 0.00894928 0.00960135 0.00959802 0.00960469]

mean value: 0.009055829048156739

key: test_mcc 
value: [ 0.16666667 -0.66666667  0.66666667 -0.40824829  1.          0.40824829
  0.57735027  1.         -0.57735027 -0.57735027]

mean value: 0.15893163974770408

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.8        0.57142857 1.         0.5
 0.66666667 1.         0.4        0.4       ]

mean value: 0.5838095238095239

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.66666667 0.5        1.         1.
 1.         1.         0.33333333 0.33333333]

mean value: 0.6333333333333332

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.8  0.4  1.   0.6  0.75 1.   0.25 0.25]

mean value: 0.585

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.16666667 0.83333333 0.33333333 1.         0.66666667
 0.75       1.         0.25       0.25      ]

mean value: 0.5833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.66666667 0.4        1.         0.33333333
 0.5        1.         0.25       0.25      ]

mean value: 0.4733333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.16

Running classifier: 9 
Model_name: K-Nearest Neighbors 
Model func: KNeighborsClassifier()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', KNeighborsClassifier())])

key: fit_time 
value: [0.00772309 0.00805616 0.00851703 0.00791001 0.00859571 0.00777578
 0.00866675 0.00818062 0.00799799 0.00829411]

mean value: 0.008171725273132324

key: score_time 
value: [0.01391387 0.00988483 0.00986409 0.00877953 0.00973701 0.00899768
 0.00963449 0.00877929 0.0090239  0.009588  ]

mean value: 0.009820270538330077

key: test_mcc 
value: [ 0.16666667 -0.16666667  0.40824829 -0.40824829  0.16666667 -0.61237244
  0.          0.57735027  0.          0.57735027]

mean value: 0.07089947693501238

key: train_mcc 
value: [0.36718832 0.56527676 0.56527676 0.56086079 0.56086079 0.52420964
 0.43656413 0.43052839 0.4472136  0.47673129]

mean value: 0.49347104597079217

key: test_fscore 
value: [0.5        0.4        0.66666667 0.57142857 0.66666667 0.
 0.5        0.8        0.66666667 0.8       ]

mean value: 0.5571428571428572

key: train_fscore 
value: [0.71111111 0.8        0.8        0.76923077 0.76923077 0.77272727
 0.73913043 0.72727273 0.75       0.74418605]

mean value: 0.7582889130866886

key: test_precision 
value: [0.5        0.33333333 0.5        0.5        0.66666667 0.
 0.5        0.66666667 0.5        0.66666667]

mean value: 0.4833333333333333

key: train_precision 
value: [0.66666667 0.75       0.75       0.78947368 0.78947368 0.70833333
 0.68       0.69565217 0.66666667 0.72727273]

mean value: 0.722353893627349

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [0.76190476 0.85714286 0.85714286 0.75       0.75       0.85
 0.80952381 0.76190476 0.85714286 0.76190476]

mean value: 0.8016666666666665

key: test_accuracy 
value: [0.6  0.4  0.6  0.4  0.6  0.2  0.5  0.75 0.5  0.75]

mean value: 0.53

key: train_accuracy 
value: [0.68292683 0.7804878  0.7804878  0.7804878  0.7804878  0.75609756
 0.71428571 0.71428571 0.71428571 0.73809524]

mean value: 0.7441927990708479

key: test_roc_auc 
value: [0.58333333 0.41666667 0.66666667 0.33333333 0.58333333 0.25
 0.5        0.75       0.5        0.75      ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [0.68095238 0.77857143 0.77857143 0.7797619  0.7797619  0.75833333
 0.71428571 0.71428571 0.71428571 0.73809524]

mean value: 0.7436904761904762

key: test_jcc 
value: [0.33333333 0.25       0.5        0.4        0.5        0.
 0.33333333 0.66666667 0.5        0.66666667]

mean value: 0.41500000000000004

key: train_jcc 
value: [0.55172414 0.66666667 0.66666667 0.625      0.625      0.62962963
 0.5862069  0.57142857 0.6        0.59259259]

mean value: 0.6114915161466885

MCC on Blind test: 0.21

MCC on Training: 0.07

Running classifier: 10 
Model_name: LDA 
Model func: LinearDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LinearDiscriminantAnalysis())])

key: fit_time 
value: [0.01078725 0.01337886 0.01345515 0.01345873 0.01352358 0.0134542
 0.01386166 0.01371312 0.01357365 0.01348424]

mean value: 0.01326904296875

key: score_time 
value: [0.01115131 0.01128483 0.01133561 0.01127672 0.0112915  0.01131248
 0.01135087 0.01136351 0.01129174 0.01131058]

mean value: 0.011296916007995605

key: test_mcc 
value: [ 0.16666667 -0.16666667 -0.61237244 -0.40824829  0.40824829 -0.16666667
 -0.57735027  1.          1.          0.57735027]

mean value: 0.12209608976375388

key: train_mcc 
value: [0.8547619  0.75714286 0.95227002 0.90238095 0.90238095 0.85441771
 0.9047619  0.9047619  0.76277007 0.9047619 ]

mean value: 0.8700410175391831

key: test_fscore 
value: [0.5        0.4        0.33333333 0.57142857 0.5        0.4
 0.4        1.         1.         0.66666667]

mean value: 0.5771428571428572

key: train_fscore 
value: [0.92682927 0.87804878 0.97674419 0.95       0.95       0.92307692
 0.95238095 0.95238095 0.87804878 0.95238095]

mean value: 0.9339890795534584

key: test_precision 
value: [0.5        0.33333333 0.25       0.5        1.         0.5
 0.33333333 1.         1.         1.        ]

mean value: 0.6416666666666666

key: train_precision 
value: [0.95       0.9        0.95454545 0.95       0.95       0.94736842
 0.95238095 0.95238095 0.9        0.95238095]

mean value: 0.9409056732740944

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.33333333 0.33333333
 0.5        1.         1.         0.5       ]

mean value: 0.5833333333333333

key: train_recall 
value: [0.9047619  0.85714286 1.         0.95       0.95       0.9
 0.95238095 0.95238095 0.85714286 0.95238095]

mean value: 0.9276190476190477

key: test_accuracy 
value: [0.6  0.4  0.2  0.4  0.6  0.4  0.25 1.   1.   0.75]

mean value: 0.5599999999999999

key: train_accuracy 
value: [0.92682927 0.87804878 0.97560976 0.95121951 0.95121951 0.92682927
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9347851335656214

key: test_roc_auc 
value: [0.58333333 0.41666667 0.25       0.33333333 0.66666667 0.41666667
 0.25       1.         1.         0.75      ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [0.92738095 0.87857143 0.975      0.95119048 0.95119048 0.92619048
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9347619047619047

key: test_jcc 
value: [0.33333333 0.25       0.2        0.4        0.33333333 0.25
 0.25       1.         1.         0.5       ]

mean value: 0.45166666666666666

key: train_jcc 
value: [0.86363636 0.7826087  0.95454545 0.9047619  0.9047619  0.85714286
 0.90909091 0.90909091 0.7826087  0.90909091]

mean value: 0.8777338603425558

MCC on Blind test: -0.3

MCC on Training: 0.12

Running classifier: 11 
Model_name: Logistic Regression 
Model func: LogisticRegression(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegression(random_state=42))])

key: fit_time 
value: [0.02276278 0.01777101 0.01533675 0.01758695 0.01738787 0.01787591
 0.01559329 0.01682639 0.01681209 0.01718521]

mean value: 0.01751382350921631

key: score_time 
value: [0.01165128 0.00993705 0.00950909 0.0094161  0.00942922 0.00944781
 0.00945854 0.00943589 0.00949621 0.00946188]

mean value: 0.009724307060241699

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829  0.61237244 -0.61237244
  1.          1.          0.          1.        ]

mean value: 0.09587585476806848

key: train_mcc 
value: [0.85441771 0.95238095 1.         0.90238095 0.90238095 0.90649828
 0.85811633 0.85811633 0.85811633 0.9047619 ]

mean value: 0.8997169740060625

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.57142857 0.85714286 0.
 1.         1.         0.5        1.        ]

mean value: 0.5595238095238095

key: train_fscore 
value: [0.93023256 0.97560976 1.         0.95       0.95       0.94736842
 0.92682927 0.92682927 0.92682927 0.95238095]

mean value: 0.9486079492548729

key: test_precision 
value: [0.   0.25 0.25 0.5  0.75 0.   1.   1.   0.5  1.  ]

mean value: 0.525

key: train_precision 
value: [0.90909091 1.         1.         0.95       0.95       1.
 0.95       0.95       0.95       0.95238095]

mean value: 0.9611471861471861

key: test_recall 
value: [0.         0.5        0.5        0.66666667 1.         0.
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_recall 
value: [0.95238095 0.95238095 1.         0.95       0.95       0.9
 0.9047619  0.9047619  0.9047619  0.95238095]

mean value: 0.9371428571428572

key: test_accuracy 
value: [0.4 0.2 0.2 0.4 0.8 0.2 1.  1.  0.5 1. ]

mean value: 0.5700000000000001

key: train_accuracy 
value: [0.92682927 0.97560976 1.         0.95121951 0.95121951 0.95121951
 0.92857143 0.92857143 0.92857143 0.95238095]

mean value: 0.9494192799070849

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.33333333 0.75       0.25
 1.         1.         0.5        1.        ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [0.92619048 0.97619048 1.         0.95119048 0.95119048 0.95
 0.92857143 0.92857143 0.92857143 0.95238095]

mean value: 0.9492857142857142

key: test_jcc 
value: [0.         0.2        0.2        0.4        0.75       0.
 1.         1.         0.33333333 1.        ]

mean value: 0.4883333333333333

key: train_jcc 
value: [0.86956522 0.95238095 1.         0.9047619  0.9047619  0.9
 0.86363636 0.86363636 0.86363636 0.90909091]

mean value: 0.9031469979296066

MCC on Blind test: 0.03

MCC on Training: 0.1

Running classifier: 12 
Model_name: Logistic RegressionCV 
Model func: LogisticRegressionCV(cv=3, random_state=42)

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegressionCV(cv=3, random_state=42))])

key: fit_time 
value: [0.14756513 0.15518308 0.14987803 0.15751052 0.1684587  0.1498332
 0.16689873 0.1696198  0.16432261 0.17528272]

mean value: 0.16045525074005126

key: score_time 
value: [0.00851631 0.00870752 0.00872874 0.00852203 0.00867891 0.00866747
 0.00855327 0.00857377 0.00856733 0.00858259]

mean value: 0.008609795570373535

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829  0.16666667  0.
  1.          1.          0.          0.        ]

mean value: 0.012542521434735132

key: train_mcc 
value: [0.41487884 1.         0.7098505  1.         1.         0.95227002
 0.85811633 1.         1.         1.        ]

mean value: 0.8935115692085429

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.57142857 0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4904761904761905

key: train_fscore 
value: [0.72727273 1.         0.86363636 1.         1.         0.97435897
 0.92682927 1.         1.         1.        ]

mean value: 0.9492097333560748

key: test_precision 
value: [0.         0.25       0.25       0.5        0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4666666666666666

key: train_precision 
value: [0.69565217 1.         0.82608696 1.         1.         1.
 0.95       1.         1.         1.        ]

mean value: 0.9471739130434782

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.5333333333333333

key: train_recall 
value: [0.76190476 1.         0.9047619  1.         1.         0.95
 0.9047619  1.         1.         1.        ]

mean value: 0.9521428571428571

key: test_accuracy 
value: [0.4 0.2 0.2 0.4 0.6 0.4 1.  1.  0.5 0.5]

mean value: 0.52

key: train_accuracy 
value: [0.70731707 1.         0.85365854 1.         1.         0.97560976
 0.92857143 1.         1.         1.        ]

mean value: 0.9465156794425088

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.33333333 0.58333333 0.5
 1.         1.         0.5        0.5       ]

mean value: 0.525

key: train_roc_auc 
value: [0.70595238 1.         0.85238095 1.         1.         0.975
 0.92857143 1.         1.         1.        ]

mean value: 0.9461904761904762

key: test_jcc 
value: [0.         0.2        0.2        0.4        0.5        0.
 1.         1.         0.33333333 0.33333333]

mean value: 0.39666666666666667

key: train_jcc 
value: [0.57142857 1.         0.76       1.         1.         0.95
 0.86363636 1.         1.         1.        ]

mean value: 0.9145064935064935

MCC on Blind test: -0.05

MCC on Training: 0.01

Running classifier: 13 
Model_name: MLP 
Model func: MLPClassifier(max_iter=500, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MLPClassifier(max_iter=500, random_state=42))])

key: fit_time 
value: [0.26154828 0.32903314 0.21032548 0.23672295 0.2415185  0.25381923
 0.26724911 0.24981642 0.23853779 0.35887623]

mean value: 0.2647447109222412

key: score_time 
value: [0.01267409 0.01199841 0.01201987 0.01393771 0.01174808 0.01212859
 0.01208258 0.01208258 0.01205397 0.01210666]

mean value: 0.012283253669738769

key: test_mcc 
value: [ 0.         -0.61237244 -0.16666667 -0.40824829  0.16666667  0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.19649778334938311

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.33333333 0.4        0.57142857 0.66666667 0.5
 1.         1.         0.5        0.8       ]

mean value: 0.5771428571428572

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.25       0.33333333 0.5        0.66666667 1.
 1.         1.         0.5        0.66666667]

mean value: 0.5916666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.33333333
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.4  0.4  0.6  0.6  1.   1.   0.5  0.75]

mean value: 0.6050000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.5        0.25       0.41666667 0.33333333 0.58333333 0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.2        0.25       0.4        0.5        0.33333333
 1.         1.         0.33333333 0.66666667]

mean value: 0.4683333333333334

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.2

Running classifier: 14 
Model_name: Multinomial 
Model func: MultinomialNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MultinomialNB())])

key: fit_time 
value: [0.01118302 0.01106453 0.00836563 0.00830483 0.00809264 0.00797749
 0.00792384 0.00803685 0.00835824 0.00805259]

mean value: 0.008735966682434083

key: score_time 
value: [0.01111627 0.01106596 0.00859427 0.00837588 0.00841665 0.00832319
 0.00818777 0.00810027 0.00839496 0.00814033]

mean value: 0.00887155532836914

key: test_mcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[-0.40824829  0.         -0.61237244 -0.40824829  0.16666667  0.
  1.          0.57735027  0.57735027  0.        ]

mean value: 0.08924981884223976

key: train_mcc 
value: [0.36515617 0.31666667 0.46623254 0.46300848 0.46300848 0.41428571
 0.43052839 0.38138504 0.42857143 0.43052839]

mean value: 0.415937128657245

key: test_fscore 
value: [0.         0.57142857 0.33333333 0.57142857 0.66666667 0.
 1.         0.8        0.8        0.5       ]

mean value: 0.5242857142857142

key: train_fscore 
value: [0.69767442 0.66666667 0.75555556 0.71794872 0.71794872 0.7
 0.72727273 0.69767442 0.71428571 0.72727273]

mean value: 0.712229966416013

key: test_precision 
value: [0.         0.4        0.25       0.5        0.66666667 0.
 1.         0.66666667 0.66666667 0.5       ]

mean value: 0.46499999999999997

key: train_precision 
value: [0.68181818 0.66666667 0.70833333 0.73684211 0.73684211 0.7
 0.69565217 0.68181818 0.71428571 0.69565217]

mean value: 0.701791063627448

key: test_recall 
value: [0.         1.         0.5        0.66666667 0.66666667 0.
 1.         1.         1.         0.5       ]

mean value: 0.6333333333333333

key: train_recall 
value: [0.71428571 0.66666667 0.80952381 0.7        0.7        0.7
 0.76190476 0.71428571 0.71428571 0.76190476]

mean value: 0.7242857142857143

key: test_accuracy 
value: [0.4  0.4  0.2  0.4  0.6  0.4  1.   0.75 0.75 0.5 ]

mean value: 0.54

key: train_accuracy 
value: [0.68292683 0.65853659 0.73170732 0.73170732 0.73170732 0.70731707
 0.71428571 0.69047619 0.71428571 0.71428571]

mean value: 0.7077235772357724

key: test_roc_auc 
value: [0.33333333 0.5        0.25       0.33333333 0.58333333 0.5
 1.         0.75       0.75       0.5       ]

mean value: 0.55

key: train_roc_auc 
value: [0.68214286 0.65833333 0.7297619  0.73095238 0.73095238 0.70714286
 0.71428571 0.69047619 0.71428571 0.71428571]

mean value: 0.7072619047619048

key: test_jcc 
value: [0.         0.4        0.2        0.4        0.5        0.
 1.         0.66666667 0.66666667 0.33333333]

mean value: 0.41666666666666663

key: train_jcc 
value: [0.53571429 0.5        0.60714286 0.56       0.56       0.53846154
 0.57142857 0.53571429 0.55555556 0.57142857]

mean value: 0.5535445665445665

MCC on Blind test: 0.23

MCC on Training: 0.09

Running classifier: 15 
Model_name: Naive Bayes 
Model func: BernoulliNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', BernoulliNB())])

key: fit_time 
value: [0.00921559 0.00892997 0.00834703 0.00904179 0.00921392 0.00909472
 0.00917387 0.00914264 0.00951838 0.00931478]

mean value: 0.009099268913269043

key: score_time 
value: [0.00903344 0.00910711 0.00899053 0.00905228 0.00956821 0.00909376
 0.00895715 0.00923181 0.00912786 0.0089848 ]

mean value: 0.009114694595336915

key: test_mcc 
value: [ 0.61237244 -0.16666667 -0.61237244  1.         -0.61237244  0.40824829
  0.57735027  0.          0.57735027  0.57735027]

mean value: 0.2361259995670279

key: train_mcc 
value: [0.65952381 0.78072006 0.78072006 0.698212   0.65915306 0.81975606
 0.67357531 0.78446454 0.81322028 0.78446454]

mean value: 0.7453809730969173

key: test_fscore 
value: [0.66666667 0.4        0.33333333 1.         0.         0.5
 0.66666667 0.         0.8        0.66666667]

mean value: 0.5033333333333333

key: train_fscore 
value: [0.82926829 0.86486486 0.86486486 0.78787879 0.75       0.88888889
 0.82051282 0.86486486 0.9        0.86486486]

mean value: 0.8436008249422884

key: test_precision 
value: [1.         0.33333333 0.25       1.         0.         1.
 1.         0.         0.66666667 1.        ]

mean value: 0.625

key: train_precision 
value: [0.85       1.         1.         1.         1.         1.
 0.88888889 1.         0.94736842 1.        ]

mean value: 0.9686257309941521

key: test_recall 
value: [0.5        0.5        0.5        1.         0.         0.33333333
 0.5        0.         1.         0.5       ]

mean value: 0.4833333333333333

key: train_recall 
value: [0.80952381 0.76190476 0.76190476 0.65       0.6        0.8
 0.76190476 0.76190476 0.85714286 0.76190476]

mean value: 0.7526190476190475

key: test_accuracy 
value: [0.8  0.4  0.2  1.   0.2  0.6  0.75 0.5  0.75 0.75]

mean value: 0.595

key: train_accuracy 
value: [0.82926829 0.87804878 0.87804878 0.82926829 0.80487805 0.90243902
 0.83333333 0.88095238 0.9047619  0.88095238]

mean value: 0.8621951219512196

key: test_roc_auc 
value: [0.75       0.41666667 0.25       1.         0.25       0.66666667
 0.75       0.5        0.75       0.75      ]

mean value: 0.6083333333333333

key: train_roc_auc 
value: [0.8297619  0.88095238 0.88095238 0.825      0.8        0.9
 0.83333333 0.88095238 0.9047619  0.88095238]

mean value: 0.8616666666666667

key: test_jcc 
value: [0.5        0.25       0.2        1.         0.         0.33333333
 0.5        0.         0.66666667 0.5       ]

mean value: 0.39499999999999996

key: train_jcc 
value: [0.70833333 0.76190476 0.76190476 0.65       0.6        0.8
 0.69565217 0.76190476 0.81818182 0.76190476]

mean value: 0.7319786373047242

MCC on Blind test: -0.07

MCC on Training: 0.24

Running classifier: 16 
Model_name: Passive Aggresive 
Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 PassiveAggressiveClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00911546 0.00983477 0.00927544 0.00967193 0.00957656 0.00978923
 0.00937676 0.00916934 0.00889277 0.00861812]

mean value: 0.009332036972045899

key: score_time 
value: [0.00904799 0.00918436 0.00818276 0.0091207  0.00896502 0.0089519
 0.00935125 0.00835657 0.00825596 0.00836968]

mean value: 0.008778619766235351

key: test_mcc 
value: [-0.66666667 -0.16666667 -0.61237244 -0.40824829  0.16666667  0.
  1.          1.          0.          1.        ]

mean value: 0.13127126071736755

key: train_mcc 
value: [0.77831178 0.90692382 1.         0.74124932 0.95238095 0.95227002
 0.80952381 0.81322028 0.8660254  0.78446454]

mean value: 0.860436992906499

key: test_fscore 
value: [0.         0.4        0.33333333 0.57142857 0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.5471428571428572

key: train_fscore 
value: [0.89361702 0.95       1.         0.86956522 0.97560976 0.97435897
 0.9047619  0.90909091 0.92307692 0.86486486]

mean value: 0.9264945570919038

key: test_precision 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
[0.         0.33333333 0.25       0.5        0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.525

key: train_precision 
value: [0.80769231 1.         1.         0.76923077 0.95238095 1.
 0.9047619  0.86956522 1.         1.        ]

mean value: 0.9303631151457239

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.5833333333333333

key: train_recall 
value: [1.         0.9047619  1.         1.         1.         0.95
 0.9047619  0.95238095 0.85714286 0.76190476]

mean value: 0.9330952380952382

key: test_accuracy 
value: [0.2 0.4 0.2 0.4 0.6 0.4 1.  1.  0.5 1. ]

mean value: 0.5700000000000001

key: train_accuracy 
value: [0.87804878 0.95121951 1.         0.85365854 0.97560976 0.97560976
 0.9047619  0.9047619  0.92857143 0.88095238]

mean value: 0.9253193960511034

key: test_roc_auc 
value: [0.16666667 0.41666667 0.25       0.33333333 0.58333333 0.5
 1.         1.         0.5        1.        ]

mean value: 0.575

key: train_roc_auc 
value: [0.875      0.95238095 1.         0.85714286 0.97619048 0.975
 0.9047619  0.9047619  0.92857143 0.88095238]

mean value: 0.9254761904761905

key: test_jcc 
value: [0.         0.25       0.2        0.4        0.5        0.
 1.         1.         0.33333333 1.        ]

mean value: 0.4683333333333334

key: train_jcc 
value: [0.80769231 0.9047619  1.         0.76923077 0.95238095 0.95
 0.82608696 0.83333333 0.85714286 0.76190476]

mean value: 0.8662533842968625

MCC on Blind test: 0.05

MCC on Training: 0.13

Running classifier: 17 
Model_name: QDA 
Model func: QuadraticDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', QuadraticDiscriminantAnalysis())])

key: fit_time 
value: [0.0092628  0.00901246 0.00931168 0.00920653 0.00939894 0.00953746
 0.00833154 0.0083909  0.00859189 0.00833082]

mean value: 0.008937501907348632

key: score_time 
value: [0.00874949 0.00910711 0.00965714 0.00918436 0.00914836 0.008636
 0.00839758 0.00832081 0.00838661 0.00837803]

mean value: 0.00879654884338379

key: test_mcc 
value: [-0.40824829 -0.40824829  0.          0.16666667  0.66666667  0.40824829
 -0.57735027  1.         -0.57735027  0.        ]

mean value: 0.027038450449021856

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.         0.         0.66666667 0.8        0.5
 0.4        1.         0.4        0.5       ]

mean value: 0.42666666666666664

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.         0.         0.66666667 1.         1.
 0.33333333 1.         0.33333333 0.5       ]

mean value: 0.4833333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.         0.         0.66666667 0.66666667 0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.41666666666666663

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.6  0.6  0.8  0.6  0.25 1.   0.25 0.5 ]

mean value: 0.54

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.33333333 0.5        0.58333333 0.83333333 0.66666667
 0.25       1.         0.25       0.5       ]

mean value: 0.525

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.         0.         0.5        0.66666667 0.33333333
 0.25       1.         0.25       0.33333333]

mean value: 0.33333333333333337

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.07

MCC on Training: 0.03

Running classifier: 18 
Model_name: Random Forest 
Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(n_estimators=1000, n_jobs=12,
                                        random_state=42))])

key: fit_time 
value: [0.55358386 0.62967014 0.5608449  0.59313846 0.55381012 0.58645582
 0.52134705 0.54654431 0.60201859 0.5584178 ]

mean value: 0.5705831050872803

key: score_time 
value: [0.13278913 0.12353516 0.16475773 0.17462468 0.14252162 0.18101025
 0.19316149 0.14391518 0.23816967 0.18874288]

mean value: 0.16832277774810792

key: test_mcc 
value: [ 0.16666667 -1.          0.40824829 -0.40824829  1.          0.40824829
  0.57735027  1.          0.57735027 -0.57735027]

mean value: 0.21522652263201553

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.66666667 0.57142857 1.         0.5
 0.66666667 1.         0.8        0.4       ]

mean value: 0.6104761904761905

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.5        0.5        1.         1.
 1.         1.         0.66666667 0.33333333]

mean value: 0.65

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         1.         0.5       ]

mean value: 0.65

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.   0.6  0.4  1.   0.6  0.75 1.   0.75 0.25]

mean value: 0.595

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.         0.66666667 0.33333333 1.         0.66666667
 0.75       1.         0.75       0.25      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.5        0.4        1.         0.33333333
 0.5        1.         0.66666667 0.25      ]

mean value: 0.49833333333333335

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.22

Running classifier: 19 
Model_name: Random Forest2 
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42)

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...age_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                                        n_estimators=1000, n_jobs=12,
                                        oob_score=True, random_state=42))])

key: fit_time 
value: [0.90452671 0.8731966  0.91812658 0.84927154 0.94887924 0.84053969
 0.85708642 0.87757754 0.82549334 0.93582559]

mean value: 0.8830523252487182

key: score_time 
value: [0.16090035 0.16618824 0.19430923 0.20362377 0.14965487 0.22289467
 0.19717145 0.18432331 0.18372726 0.18409944]

mean value: 0.18468925952911378

key: test_mcc 
value: [ 0.16666667 -1.          0.40824829 -0.40824829  0.61237244  0.40824829
  1.          1.          0.57735027  0.        ]

mean value: 0.276463766201595

key: train_mcc 
value: [0.7565654  0.90692382 0.8047619  0.90238095 0.8547619  0.85441771
 0.9047619  0.80952381 0.81322028 0.80952381]

mean value: 0.841684150259194

key: test_fscore 
value: [0.5        0.         0.66666667 0.57142857 0.85714286 0.5
 1.         1.         0.8        0.5       ]

mean value: 0.6395238095238095

key: train_fscore 
value: [0.88372093 0.95       0.9047619  0.95       0.92682927 0.92307692
 0.95238095 0.9047619  0.9        0.9047619 ]

mean value: 0.9200293788268832

key: test_precision 
value: [0.5        0.         0.5        0.5        0.75       1.
 1.         1.         0.66666667 0.5       ]

mean value: 0.6416666666666667

key: train_precision 
value: [0.86363636 1.         0.9047619  0.95       0.9047619  0.94736842
 0.95238095 0.9047619  0.94736842 0.9047619 ]

mean value: 0.9279801777170199

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.7

key: train_recall 
value: [0.9047619  0.9047619  0.9047619  0.95       0.95       0.9
 0.95238095 0.9047619  0.85714286 0.9047619 ]

mean value: 0.9133333333333334

key: test_accuracy 
value: [0.6  0.   0.6  0.4  0.8  0.6  1.   1.   0.75 0.5 ]

mean value: 0.625

key: train_accuracy 
value: [0.87804878 0.95121951 0.90243902 0.95121951 0.92682927 0.92682927
 0.95238095 0.9047619  0.9047619  0.9047619 ]

mean value: 0.9203252032520325

key: test_roc_auc 
value: [0.58333333 0.         0.66666667 0.33333333 0.75       0.66666667
 1.         1.         0.75       0.5       ]

mean value: 0.625

key: train_roc_auc 
value: [0.87738095 0.95238095 0.90238095 0.95119048 0.92738095 0.92619048
 0.95238095 0.9047619  0.9047619  0.9047619 ]

mean value: 0.9203571428571428

key: test_jcc 
value: [0.33333333 0.         0.5        0.4        0.75       0.33333333
 1.         1.         0.66666667 0.33333333]

mean value: 0.5316666666666666

key: train_jcc 
value: [0.79166667 0.9047619  0.82608696 0.9047619  0.86363636 0.85714286
 0.90909091 0.82608696 0.81818182 0.82608696]

mean value: 0.8527503293807641

MCC on Blind test: -0.13

MCC on Training: 0.28

Running classifier: 20 
Model_name: Ridge Classifier 
Model func: RidgeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifier(random_state=42))])

key: fit_time 
value: [0.01039004 0.00990462 0.00964117 0.00976992 0.00990582 0.00934601
 0.01006198 0.01024485 0.01005363 0.01038742]

mean value: 0.009970545768737793

key: score_time 
value: [0.00966334 0.00948763 0.00927234 0.0087626  0.00877118 0.00932288
 0.00956345 0.00921845 0.00949717 0.00895643]

mean value: 0.009251546859741212

key: test_mcc 
value: [-0.40824829 -0.16666667 -0.61237244 -0.66666667  0.61237244  0.
  1.          1.          0.          1.        ]

mean value: 0.17584183762028038

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.4        0.33333333 0.33333333 0.85714286 0.
 1.         1.         0.5        1.        ]

mean value: 0.5423809523809524

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.33333333 0.25       0.33333333 0.75       0.
 1.         1.         0.5        1.        ]

mean value: 0.5166666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.5        0.33333333 1.         0.
 1.         1.         0.5        1.        ]

mean value: 0.5833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4 0.4 0.2 0.2 0.8 0.4 1.  1.  0.5 1. ]

mean value: 0.5900000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.41666667 0.25       0.16666667 0.75       0.5
 1.         1.         0.5        1.        ]

mean value: 0.5916666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.25       0.2        0.2        0.75       0.
 1.         1.         0.33333333 1.        ]

mean value: 0.47333333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.31

MCC on Training: 0.18

Running classifier: 21 
Model_name: Ridge ClassifierCV 
Model func: RidgeClassifierCV(cv=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifierCV(cv=3))])

key: fit_time 
value: [0.02538943 0.02745485 0.02640367 0.0266459  0.02600169 0.0269928
 0.02637315 0.02653122 0.02626705 0.02588439]

mean value: 0.026394414901733398

key: score_time 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.00916243 0.00952506 0.00921535 0.00920939 0.0092051  0.00893593
 0.00936699 0.00993633 0.0092423  0.00926065]

mean value: 0.009305953979492188

key: test_mcc 
value: [ 0.         -0.16666667 -0.61237244 -0.66666667  0.61237244  0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.21522652263201558

key: train_mcc 
value: [1.         1.         0.8547619  1.         1.         1.
 0.85811633 1.         1.         1.        ]

mean value: 0.9712878235082938

key: test_fscore 
value: [0.         0.4        0.33333333 0.33333333 0.85714286 0.5
 1.         1.         0.5        0.66666667]

mean value: 0.5590476190476191

key: train_fscore 
value: [1.         1.         0.92682927 1.         1.         1.
 0.92682927 1.         1.         1.        ]

mean value: 0.9853658536585366

key: test_precision 
value: [0.         0.33333333 0.25       0.33333333 0.75       1.
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_precision 
value: [1.   1.   0.95 1.   1.   1.   0.95 1.   1.   1.  ]

mean value: 0.99

key: test_recall 
value: [0.         0.5        0.5        0.33333333 1.         0.33333333
 1.         1.         0.5        0.5       ]

mean value: 0.5666666666666667

key: train_recall 
value: [1.        1.        0.9047619 1.        1.        1.        0.9047619
 1.        1.        1.       ]

mean value: 0.980952380952381

key: test_accuracy 
value: [0.6  0.4  0.2  0.2  0.8  0.6  1.   1.   0.5  0.75]

mean value: 0.605

key: train_accuracy 
value: [1.         1.         0.92682927 1.         1.         1.
 0.92857143 1.         1.         1.        ]

mean value: 0.9855400696864113

key: test_roc_auc 
value: [0.5        0.41666667 0.25       0.16666667 0.75       0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1.         1.         0.92738095 1.         1.         1.
 0.92857143 1.         1.         1.        ]

mean value: 0.9855952380952381

key: test_jcc 
value: [0.         0.25       0.2        0.2        0.75       0.33333333
 1.         1.         0.33333333 0.5       ]

mean value: 0.45666666666666667

key: train_jcc 
value: [1.         1.         0.86363636 1.         1.         1.
 0.86363636 1.         1.         1.        ]

mean value: 0.9727272727272727

MCC on Blind test: -0.31

MCC on Training: 0.22

Running classifier: 22 
Model_name: SVC 
Model func: SVC(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SVC(random_state=42))])

key: fit_time 
value: [0.00931573 0.00952458 0.00878596 0.00907946 0.00951433 0.00869846
 0.00847411 0.00910711 0.0096128  0.00942516]

mean value: 0.00915377140045166

key: score_time 
value: [0.00917435 0.00869799 0.00853705 0.00877333 0.00908065 0.00860214
 0.00900173 0.00927615 0.00915623 0.00883126]

mean value: 0.008913087844848632

key: test_mcc 
value: [-0.40824829 -0.16666667 -0.16666667 -0.40824829  0.16666667  0.
  1.          0.57735027  0.          0.        ]

mean value: 0.059418702159523294

key: train_mcc 
value: [0.65952381 0.8547619  0.75714286 0.7633652  0.65871309 0.85441771
 0.76980036 0.62187434 0.71428571 0.71754731]

mean value: 0.7371432287703027

key: test_fscore 
value: [0.         0.4        0.4        0.57142857 0.66666667 0.
 1.         0.66666667 0.5        0.5       ]

mean value: 0.4704761904761904

key: train_fscore 
value: [0.82926829 0.92682927 0.87804878 0.86486486 0.82051282 0.92307692
 0.87179487 0.8        0.85714286 0.86363636]

mean value: 0.8635175042492115

key: test_precision 
value: [0.         0.33333333 0.33333333 0.5        0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4833333333333333

key: train_precision 
value: [0.85       0.95       0.9        0.94117647 0.84210526 0.94736842
 0.94444444 0.84210526 0.85714286 0.82608696]

mean value: 0.8900429676065696

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         0.5        0.5        0.5       ]

mean value: 0.4833333333333333

key: train_recall 
value: [0.80952381 0.9047619  0.85714286 0.8        0.8        0.9
 0.80952381 0.76190476 0.85714286 0.9047619 ]

mean value: 0.8404761904761904

key: test_accuracy 
value: [0.4  0.4  0.4  0.4  0.6  0.4  1.   0.75 0.5  0.5 ]

mean value: 0.5349999999999999

key: train_accuracy 
value: [0.82926829 0.92682927 0.87804878 0.87804878 0.82926829 0.92682927
 0.88095238 0.80952381 0.85714286 0.85714286]

mean value: 0.8673054587688733

key: test_roc_auc 
value: [0.33333333 0.41666667 0.41666667 0.33333333 0.58333333 0.5
 1.         0.75       0.5        0.5       ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [0.8297619  0.92738095 0.87857143 0.87619048 0.82857143 0.92619048
 0.88095238 0.80952381 0.85714286 0.85714286]

mean value: 0.8671428571428572

key: test_jcc 
value: [0.         0.25       0.25       0.4        0.5        0.
 1.         0.5        0.33333333 0.33333333]

mean value: 0.3566666666666667

key: train_jcc 
value: [0.70833333 0.86363636 0.7826087  0.76190476 0.69565217 0.85714286
 0.77272727 0.66666667 0.75       0.76      ]

mean value: 0.7618672124976473

MCC on Blind test: 0.12

MCC on Training: 0.06

Running classifier: 23 
Model_name: Stochastic GDescent 
Model func: SGDClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SGDClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.008991   0.00889349 0.00859237 0.00896955 0.00868869 0.00950837
 0.00857234 0.00952172 0.00874186 0.00952697]

mean value: 0.009000635147094727

key: score_time 
value: [0.00860119 0.00874877 0.00874877 0.00896454 0.00894547 0.00921011
 0.00875974 0.00933814 0.00897598 0.00920892]

mean value: 0.008950161933898925

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244  0.16666667  0.          0.
  1.          0.57735027  0.          0.57735027]

mean value: 0.0688374043190466

key: train_mcc 
value: [0.90649828 1.         1.         0.95227002 0.70272837 1.
 0.55901699 0.40824829 0.36760731 0.8660254 ]

mean value: 0.7762394663113877

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.66666667 0.75       0.
 1.         0.66666667 0.66666667 0.66666667]

mean value: 0.5083333333333334

key: train_fscore 
value: [0.95454545 1.         1.         0.97435897 0.85106383 1.
 0.64516129 0.44444444 0.72413793 0.92307692]

mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
  from pandas import MultiIndex, Int64Index
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
  from pandas import MultiIndex, Int64Index
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:427: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_CV['source_data'] = 'CV'
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:454: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_BT['source_data'] = 'BT'
0.8516788847570094

key: test_precision 
value: [0.         0.25       0.25       0.66666667 0.6        0.
 1.         1.         0.5        1.        ]

mean value: 0.5266666666666666

key: train_precision 
value: [0.91304348 1.         1.         1.         0.74074074 1.
 1.         1.         0.56756757 1.        ]

mean value: 0.9221351786569176

key: test_recall 
value: [0.         0.5        0.5        0.66666667 1.         0.
 1.         0.5        1.         0.5       ]

mean value: 0.5666666666666667

key: train_recall 
value: [1.         1.         1.         0.95       1.         1.
 0.47619048 0.28571429 1.         0.85714286]

mean value: 0.856904761904762

key: test_accuracy 
value: [0.4  0.2  0.2  0.6  0.6  0.4  1.   0.75 0.5  0.75]

mean value: 0.54

key: train_accuracy 
value: [0.95121951 1.         1.         0.97560976 0.82926829 1.
 0.73809524 0.64285714 0.61904762 0.92857143]

mean value: 0.8684668989547039

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.58333333 0.5        0.5
 1.         0.75       0.5        0.75      ]

mean value: 0.5416666666666666

key: train_roc_auc 
value: [0.95       1.         1.         0.975      0.83333333 1.
 0.73809524 0.64285714 0.61904762 0.92857143]

mean value: 0.8686904761904761

key: test_jcc 
value: [0.  0.2 0.2 0.5 0.6 0.  1.  0.5 0.5 0.5]

mean value: 0.4

key: train_jcc 
value: [0.91304348 1.         1.         0.95       0.74074074 1.
 0.47619048 0.28571429 0.56756757 0.85714286]

mean value: 0.7790399405616796

MCC on Blind test: 0.0

MCC on Training: 0.07

Running classifier: 24 
Model_name: XGBoost 
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...
                               interaction_constraints=None, learning_rate=None,
                               max_delta_step=None, max_depth=None,
                               min_child_weight=None, missing=nan,
                               monotone_constraints=None, n_estimators=100,
                               n_jobs=12, num_parallel_tree=None,
                               predictor=None, random_state=42, reg_alpha=None,
                               reg_lambda=None, scale_pos_weight=None,
                               subsample=None, tree_method=None,
                               use_label_encoder=False,
                               validate_parameters=None, verbosity=0))])

key: fit_time 
value: [0.0475421  0.03474045 0.03474998 0.03431797 0.03458786 0.03289366
 0.03387642 0.03446102 0.03263259 0.03263879]

mean value: 0.035244083404541014

key: score_time 
value: [0.01011229 0.0104084  0.01022196 0.01030111 0.01056361 0.01001573
 0.00998259 0.01012969 0.01034617 0.01025987]

mean value: 0.01023414134979248

key: test_mcc 
value: [ 0.61237244 -0.16666667  0.         -0.66666667  0.66666667  0.40824829
  0.57735027  0.57735027  0.57735027  0.57735027]

mean value: 0.31633551362514944

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.4        0.57142857 0.33333333 0.8        0.5
 0.66666667 0.66666667 0.8        0.8       ]

mean value: 0.6204761904761904

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.33333333 0.4        0.33333333 1.         1.
 1.         1.         0.66666667 0.66666667]

mean value: 0.74

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.4  0.4  0.2  0.8  0.6  0.75 0.75 0.75 0.75]

mean value: 0.62

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.41666667 0.5        0.16666667 0.83333333 0.66666667
 0.75       0.75       0.75       0.75      ]

mean value: 0.6333333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.25       0.4        0.2        0.66666667 0.33333333
 0.5        0.5        0.66666667 0.66666667]

mean value: 0.4683333333333334

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.22

MCC on Training: 0.32

Extracting tts_split_name: 70_30

Total cols in each df: 
CV df: 8 
metaDF: 15

Adding column: Model_name

Total cols in bts df: 
BT_df: 8

First proceeding to rowbind CV and BT dfs:

Final output should have: 23 columns

Combinig 2 using pd.concat by row ~ rowbind 
Checking Dims of df to combine: 
Dim of CV: (24, 8) 
Dim of BT: (24, 8)
8

Number of Common columns: 8 
These are: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall']

Concatenating dfs with different resampling methods [WF]: 
Split type: 70_30 
No. of dfs combining: 2

PASS: 2 dfs successfully combined 
nrows in combined_df_wf: 48 
ncols in combined_df_wf: 8

PASS: proceeding to merge metadata with CV and BT dfs

Adding column: Model_name

========================================================= 
SUCCESS: Ran multiple classifiers 
=======================================================

==============================================================
 
Running several classification models (n): 24 
List of models:
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3))
('Decision Tree', DecisionTreeClassifier(random_state=42))
('Extra Tree', ExtraTreeClassifier(random_state=42))
('Extra Trees', ExtraTreesClassifier(random_state=42))
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
('Gaussian NB', GaussianNB())
('Gaussian Process', GaussianProcessClassifier(random_state=42))
('K-Nearest Neighbors', KNeighborsClassifier())
('LDA', LinearDiscriminantAnalysis())
('Logistic Regression', LogisticRegression(random_state=42))
('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
('MLP', MLPClassifier(max_iter=500, random_state=42))
('Multinomial', MultinomialNB())
('Naive Bayes', BernoulliNB())
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
('QDA', QuadraticDiscriminantAnalysis())
('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42))
('Ridge Classifier', RidgeClassifier(random_state=42))
('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
('SVC', SVC(random_state=42))
('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 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 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 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 3 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 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 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...















P
<00>3@6@ @4@*@ @@@<00>?@@&@@0$<00><>
x(AA<00>
x(P<>
x(<00>D@!<00>
x(<00>x(<00>$ <20>
x(`<60>x(4<00>d
x(Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
*@<40><><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FF><FE><FF><FF><FF><FF><FF><00>
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0))

================================================================


Running classifier: 1 
Model_name: AdaBoost Classifier 
Model func: AdaBoostClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', AdaBoostClassifier(random_state=42))])

key: fit_time 
value: [0.0706737  0.06998253 0.07171822 0.07156992 0.06753039 0.07276559
 0.07164121 0.07182813 0.06849694 0.07091856]

mean value: 0.0707125186920166

key: score_time 
value: [0.01465726 0.01629305 0.01603532 0.01576757 0.01595688 0.01618648
 0.01597619 0.01579046 0.01553869 0.01503062]

mean value: 0.015723252296447755

key: test_mcc 
value: [-0.16666667 -0.16666667  0.          0.16666667  0.16666667  0.
  0.57735027  1.          0.          0.57735027]

mean value: 0.21547005383792514

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.4        0.57142857 0.66666667 0.66666667 0.
 0.66666667 1.         0.66666667 0.8       ]

mean value: 0.5838095238095238

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.33333333 0.4        0.66666667 0.66666667 0.
 1.         1.         0.5        0.66666667]

mean value: 0.5566666666666668

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.4  0.6  0.6  0.4  0.75 1.   0.5  0.75]

mean value: 0.58

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.41666667 0.5        0.58333333 0.58333333 0.5
 0.75       1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.25       0.4        0.5        0.5        0.
 0.5        1.         0.5        0.66666667]

mean value: 0.45666666666666667

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.03

MCC on Training: 0.22

Running classifier: 2 
Model_name: Bagging Classifier 
Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True,
                                   random_state=42, verbose=3))])

key: fit_time 
value: [0.09015608 0.09956288 0.13018298 0.1281836  0.09882951 0.11058927
 0.10722399 0.10750437 0.09557104 0.11693478]

mean value: 0.10847384929656982

key: score_time 
value: [0.04173255 0.06210113 0.05165505 0.06780457 0.05070138 0.05854702
 0.05252862 0.06386089 0.07204771 0.04695678]

mean value: 0.05679357051849365

key: test_mcc 
value: [ 0.16666667 -0.16666667  0.         -0.66666667  1.          0.40824829
  1.          0.57735027  0.57735027  0.57735027]

mean value: 0.3473632431366074

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.4        0.57142857 0.33333333 1.         0.5
 1.         0.66666667 0.8        0.8       ]

mean value: 0.6571428571428571

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.33333333 0.4        0.33333333 1.         1.
 1.         1.         0.66666667 0.66666667]

mean value: 0.6900000000000001

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 1.         0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.4  0.4  0.2  1.   0.6  1.   0.75 0.75 0.75]

mean value: 0.645

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.41666667 0.5        0.16666667 1.         0.66666667
 1.         0.75       0.75       0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.25       0.4        0.2        1.         0.33333333
 1.         0.5        0.66666667 0.66666667]

mean value: 0.535

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.35

Running classifier: 3 
Model_name: Decision Tree 
Model func: DecisionTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', DecisionTreeClassifier(random_state=42))])

key: fit_time 
value: [0.00920463 0.01015854 0.00991392 0.01018214 0.00919986 0.00899363
 0.00970221 0.01006627 0.01015687 0.00998592]

mean value: 0.00975639820098877

key: score_time 
value: [0.00920033 0.009377   0.00934243 0.0094049  0.00862455 0.00882125
 0.00914502 0.00914598 0.00916696 0.00915956]

mean value: 0.009138798713684082

key: test_mcc 
value: [ 0.61237244  0.61237244  0.         -0.66666667 -0.40824829  0.40824829
  0.          0.          0.57735027  0.57735027]

mean value: 0.17127787431041744

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 0.57142857 0.33333333 0.57142857 0.5
 0.5        0.5        0.8        0.8       ]

mean value: 0.590952380952381

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         0.4        0.33333333 0.5        1.
 0.5        0.5        0.66666667 0.66666667]

mean value: 0.6566666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  0.4  0.2  0.4  0.6  0.5  0.5  0.75 0.75]

mean value: 0.5700000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       0.5        0.16666667 0.33333333 0.66666667
 0.5        0.5        0.75       0.75      ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        0.4        0.2        0.4        0.33333333
 0.33333333 0.33333333 0.66666667 0.66666667]

mean value: 0.4333333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.21

MCC on Training: 0.17

Running classifier: 4 
Model_name: Extra Tree 
Model func: ExtraTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreeClassifier(random_state=42))])

key: fit_time 
value: [0.00935197 0.00813079 0.00821328 0.00815511 0.00824046 0.00807285
 0.00819373 0.00816941 0.00828099 0.00876212]

mean value: 0.008357071876525879

key: score_time 
value: [0.00913334 0.00846624 0.00820827 0.00829124 0.00821042 0.00843406
 0.00823331 0.0084455  0.00835991 0.00913835]

mean value: 0.008492064476013184

key: test_mcc 
value: [ 0.61237244 -0.40824829  0.40824829 -0.40824829  0.61237244  0.66666667
  1.          0.57735027  0.          0.        ]

mean value: 0.30605135167840186

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.         0.66666667 0.57142857 0.85714286 0.8
 1.         0.8        0.5        0.5       ]

mean value: 0.6361904761904762

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.         0.5        0.5        0.75       1.
 1.         0.66666667 0.5        0.5       ]

mean value: 0.6416666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.66666667
 1.         1.         0.5        0.5       ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.4  0.6  0.4  0.8  0.8  1.   0.75 0.5  0.5 ]

mean value: 0.655

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.33333333 0.66666667 0.33333333 0.75       0.83333333
 1.         0.75       0.5        0.5       ]

mean value: 0.6416666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.         0.5        0.4        0.75       0.66666667
 1.         0.66666667 0.33333333 0.33333333]

mean value: 0.5149999999999999

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.14

MCC on Training: 0.31

Running classifier: 5 
Model_name: Extra Trees 
Model func: ExtraTreesClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreesClassifier(random_state=42))])

key: fit_time 
value: [0.08343029 0.07999516 0.0794611  0.08021879 0.08074927 0.07914686
 0.08507109 0.08297443 0.08542418 0.08515954]

mean value: 0.08216307163238526

key: score_time 
value: [0.01861143 0.01846194 0.01848698 0.01834464 0.01951504 0.01870656
 0.01854014 0.01856232 0.018502   0.01856565]

mean value: 0.01862967014312744

key: test_mcc 
value: [ 0.16666667 -0.66666667  0.66666667 -0.40824829  0.61237244  0.40824829
  0.57735027  0.57735027  0.57735027 -0.57735027]

mean value: 0.19337396407417132

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.8        0.57142857 0.85714286 0.5
 0.66666667 0.66666667 0.8        0.4       ]

mean value: 0.5761904761904761

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.66666667 0.5        0.75       1.
 1.         1.         0.66666667 0.33333333]

mean value: 0.6416666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        0.5        1.         0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.8  0.4  0.8  0.6  0.75 0.75 0.75 0.25]

mean value: 0.5900000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.16666667 0.83333333 0.33333333 0.75       0.66666667
 0.75       0.75       0.75       0.25      ]

mean value: 0.5833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.66666667 0.4        0.75       0.33333333
 0.5        0.5        0.66666667 0.25      ]

mean value: 0.43999999999999995

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.15

MCC on Training: 0.19

Running classifier: 6 
Model_name: Gradient Boosting 
Model func: GradientBoostingClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GradientBoostingClassifier(random_state=42))])

key: fit_time 
value: [0.12611079 0.1153512  0.10698032 0.1233685  0.11780524 0.10499978
 0.11987543 0.12128925 0.12485504 0.10909796]

mean value: 0.11697335243225097

key: score_time 
value: [0.00882769 0.00873971 0.00909352 0.00874662 0.00869823 0.0087409
 0.00900912 0.0095787  0.0087769  0.00871825]

mean value: 0.008892965316772462

key: test_mcc 
value: [0.61237244 0.61237244 0.         0.16666667 0.16666667 0.40824829
 0.57735027 0.         0.57735027 0.57735027]

mean value: 0.36983773027576633

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 0.57142857 0.66666667 0.66666667 0.5
 0.66666667 0.5        0.8        0.8       ]

mean value: 0.6504761904761904

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         0.4        0.66666667 0.66666667 1.
 1.         0.5        0.66666667 0.66666667]

mean value: 0.7566666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  0.4  0.6  0.6  0.6  0.75 0.5  0.75 0.75]

mean value: 0.655

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       0.5        0.58333333 0.58333333 0.66666667
 0.75       0.5        0.75       0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        0.4        0.5        0.5        0.33333333
 0.5        0.33333333 0.66666667 0.66666667]

mean value: 0.49000000000000005

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.48

MCC on Training: 0.37

Running classifier: 7 
Model_name: Gaussian NB 
Model func: GaussianNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianNB())])

key: fit_time 
value: [0.00808597 0.00802708 0.00837111 0.00846457 0.01209831 0.00804043
 0.00856924 0.00883937 0.00938725 0.00927067]

mean value: 0.008915400505065918

key: score_time 
value: [0.00836539 0.00826764 0.00840521 0.00876927 0.00855994 0.00856376
 0.00871682 0.00931144 0.00927901 0.00861907]

mean value: 0.008685755729675292

key: test_mcc 
value: [-0.16666667  0.          0.          0.61237244  0.          0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.34313043286826167

key: train_mcc 
value: [0.57570364 0.698212   0.63496528 0.49692935 0.59982886 0.63994524
 0.56652882 0.52704628 0.54659439 0.60609153]

mean value: 0.5891845382894122

key: test_fscore 
value: [0.4        0.57142857 0.57142857 0.85714286 0.75       0.5
 1.         1.         0.66666667 0.8       ]

mean value: 0.7116666666666667

key: train_fscore 
value: [0.80851064 0.85714286 0.83333333 0.76595745 0.80851064 0.82608696
 0.8        0.78431373 0.79166667 0.81632653]

mean value: 0.8091848793171292

key: test_precision 
value: [0.33333333 0.4        0.4        0.75       0.6        1.
 1.         1.         0.5        0.66666667]

mean value: 0.665

key: train_precision 
value: [0.73076923 0.75       0.74074074 0.66666667 0.7037037  0.73076923
 0.68965517 0.66666667 0.7037037  0.71428571]

mean value: 0.7096960829719451

key: test_recall 
value: [0.5        1.         1.         1.         1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.8833333333333332

key: train_recall 
value: [0.9047619  1.         0.95238095 0.9        0.95       0.95
 0.95238095 0.95238095 0.9047619  0.95238095]

mean value: 0.9419047619047619

key: test_accuracy 
value: [0.4  0.4  0.4  0.8  0.6  0.6  1.   1.   0.5  0.75]

mean value: 0.645

key: train_accuracy 
value: [0.7804878  0.82926829 0.80487805 0.73170732 0.7804878  0.80487805
 0.76190476 0.73809524 0.76190476 0.78571429]

mean value: 0.7779326364692218

key: test_roc_auc 
value: [0.41666667 0.5        0.5        0.75       0.5        0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [0.77738095 0.825      0.80119048 0.73571429 0.78452381 0.80833333
 0.76190476 0.73809524 0.76190476 0.78571429]

mean value: 0.7779761904761904

key: test_jcc 
value: [0.25       0.4        0.4        0.75       0.6        0.33333333
 1.         1.         0.5        0.66666667]

mean value: 0.5900000000000001

key: train_jcc 
value: [0.67857143 0.75       0.71428571 0.62068966 0.67857143 0.7037037
 0.66666667 0.64516129 0.65517241 0.68965517]

mean value: 0.6802477473500833

MCC on Blind test: -0.03

MCC on Training: 0.34

Running classifier: 8 
Model_name: Gaussian Process 
Model func: GaussianProcessClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianProcessClassifier(random_state=42))])

key: fit_time 
value: [0.0098021  0.00998521 0.00969505 0.00958514 0.00961423 0.00966907
 0.00969791 0.00961161 0.00959039 0.00962734]

mean value: 0.00968780517578125

key: score_time 
value: [0.00874043 0.00836539 0.00836205 0.00834465 0.00827217 0.00839257
 0.00840402 0.00839138 0.00837803 0.00836492]

mean value: 0.00840156078338623

key: test_mcc 
value: [ 0.16666667 -0.66666667  0.66666667 -0.40824829  1.          0.40824829
  0.57735027  1.         -0.57735027 -0.57735027]

mean value: 0.15893163974770408

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.8        0.57142857 1.         0.5
 0.66666667 1.         0.4        0.4       ]

mean value: 0.5838095238095239

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.66666667 0.5        1.         1.
 1.         1.         0.33333333 0.33333333]

mean value: 0.6333333333333332

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.8  0.4  1.   0.6  0.75 1.   0.25 0.25]

mean value: 0.585

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.16666667 0.83333333 0.33333333 1.         0.66666667
 0.75       1.         0.25       0.25      ]

mean value: 0.5833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.66666667 0.4        1.         0.33333333
 0.5        1.         0.25       0.25      ]

mean value: 0.4733333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.16

Running classifier: 9 
Model_name: K-Nearest Neighbors 
Model func: KNeighborsClassifier()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', KNeighborsClassifier())])

key: fit_time 
value: [0.00794935 0.00888395 0.00920367 0.00797439 0.00891042 0.00893974
 0.00868678 0.00903749 0.00901794 0.00891948]

mean value: 0.00875232219696045

key: score_time 
value: [0.01479959 0.00988865 0.00997066 0.00931573 0.00966287 0.00976324
 0.00944853 0.01455832 0.00960279 0.00982571]

mean value: 0.010683608055114747

key: test_mcc 
value: [ 0.16666667 -0.16666667  0.40824829 -0.40824829  0.16666667 -0.61237244
  0.          0.57735027  0.          0.57735027]

mean value: 0.07089947693501238

key: train_mcc 
value: [0.36718832 0.56527676 0.56527676 0.56086079 0.56086079 0.52420964
 0.43656413 0.43052839 0.4472136  0.47673129]

mean value: 0.49347104597079217

key: test_fscore 
value: [0.5        0.4        0.66666667 0.57142857 0.66666667 0.
 0.5        0.8        0.66666667 0.8       ]

mean value: 0.5571428571428572

key: train_fscore 
value: [0.71111111 0.8        0.8        0.76923077 0.76923077 0.77272727
 0.73913043 0.72727273 0.75       0.74418605]

mean value: 0.7582889130866886

key: test_precision 
value: [0.5        0.33333333 0.5        0.5        0.66666667 0.
 0.5        0.66666667 0.5        0.66666667]

mean value: 0.4833333333333333

key: train_precision 
value: [0.66666667 0.75       0.75       0.78947368 0.78947368 0.70833333
 0.68       0.69565217 0.66666667 0.72727273]

mean value: 0.722353893627349

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [0.76190476 0.85714286 0.85714286 0.75       0.75       0.85
 0.80952381 0.76190476 0.85714286 0.76190476]

mean value: 0.8016666666666665

key: test_accuracy 
value: [0.6  0.4  0.6  0.4  0.6  0.2  0.5  0.75 0.5  0.75]

mean value: 0.53

key: train_accuracy 
value: [0.68292683 0.7804878  0.7804878  0.7804878  0.7804878  0.75609756
 0.71428571 0.71428571 0.71428571 0.73809524]

mean value: 0.7441927990708479

key: test_roc_auc 
value: [0.58333333 0.41666667 0.66666667 0.33333333 0.58333333 0.25
 0.5        0.75       0.5        0.75      ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [0.68095238 0.77857143 0.77857143 0.7797619  0.7797619  0.75833333
 0.71428571 0.71428571 0.71428571 0.73809524]

mean value: 0.7436904761904762

key: test_jcc 
value: [0.33333333 0.25       0.5        0.4        0.5        0.
 0.33333333 0.66666667 0.5        0.66666667]

mean value: 0.41500000000000004

key: train_jcc 
value: [0.55172414 0.66666667 0.66666667 0.625      0.625      0.62962963
 0.5862069  0.57142857 0.6        0.59259259]

mean value: 0.6114915161466885

MCC on Blind test: 0.21

MCC on Training: 0.07

Running classifier: 10 
Model_name: LDA 
Model func: LinearDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LinearDiscriminantAnalysis())])

key: fit_time 
value: [0.01098967 0.01364136 0.01366186 0.01374197 0.01377368 0.01355457
 0.01382995 0.01405096 0.01379728 0.01390123]

mean value: 0.013494253158569336

key: score_time 
value: [0.01137424 0.01152468 0.0114975  0.01156473 0.01150799 0.01160431
 0.0115006  0.01160264 0.01156759 0.01153708]

mean value: 0.0115281343460083

key: test_mcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
[ 0.16666667 -0.16666667 -0.61237244 -0.40824829  0.40824829 -0.16666667
 -0.57735027  1.          1.          0.57735027]

mean value: 0.12209608976375388

key: train_mcc 
value: [0.8547619  0.75714286 0.95227002 0.90238095 0.90238095 0.85441771
 0.9047619  0.9047619  0.76277007 0.9047619 ]

mean value: 0.8700410175391831

key: test_fscore 
value: [0.5        0.4        0.33333333 0.57142857 0.5        0.4
 0.4        1.         1.         0.66666667]

mean value: 0.5771428571428572

key: train_fscore 
value: [0.92682927 0.87804878 0.97674419 0.95       0.95       0.92307692
 0.95238095 0.95238095 0.87804878 0.95238095]

mean value: 0.9339890795534584

key: test_precision 
value: [0.5        0.33333333 0.25       0.5        1.         0.5
 0.33333333 1.         1.         1.        ]

mean value: 0.6416666666666666

key: train_precision 
value: [0.95       0.9        0.95454545 0.95       0.95       0.94736842
 0.95238095 0.95238095 0.9        0.95238095]

mean value: 0.9409056732740944

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.33333333 0.33333333
 0.5        1.         1.         0.5       ]

mean value: 0.5833333333333333

key: train_recall 
value: [0.9047619  0.85714286 1.         0.95       0.95       0.9
 0.95238095 0.95238095 0.85714286 0.95238095]

mean value: 0.9276190476190477

key: test_accuracy 
value: [0.6  0.4  0.2  0.4  0.6  0.4  0.25 1.   1.   0.75]

mean value: 0.5599999999999999

key: train_accuracy 
value: [0.92682927 0.87804878 0.97560976 0.95121951 0.95121951 0.92682927
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9347851335656214

key: test_roc_auc 
value: [0.58333333 0.41666667 0.25       0.33333333 0.66666667 0.41666667
 0.25       1.         1.         0.75      ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [0.92738095 0.87857143 0.975      0.95119048 0.95119048 0.92619048
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9347619047619047

key: test_jcc 
value: [0.33333333 0.25       0.2        0.4        0.33333333 0.25
 0.25       1.         1.         0.5       ]

mean value: 0.45166666666666666

key: train_jcc 
value: [0.86363636 0.7826087  0.95454545 0.9047619  0.9047619  0.85714286
 0.90909091 0.90909091 0.7826087  0.90909091]

mean value: 0.8777338603425558

MCC on Blind test: -0.3

MCC on Training: 0.12

Running classifier: 11 
Model_name: Logistic Regression 
Model func: LogisticRegression(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegression(random_state=42))])

key: fit_time 
value: [0.02226996 0.01795316 0.01448131 0.01715255 0.01667118 0.01589561
 0.01461196 0.01626348 0.01555753 0.01657391]

mean value: 0.016743063926696777

key: score_time 
value: [0.01129746 0.00871801 0.00937819 0.00914931 0.00875235 0.00827265
 0.00844073 0.00896955 0.00891781 0.00913954]

mean value: 0.00910356044769287

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829  0.61237244 -0.61237244
  1.          1.          0.          1.        ]

mean value: 0.09587585476806848

key: train_mcc 
value: [0.85441771 0.95238095 1.         0.90238095 0.90238095 0.90649828
 0.85811633 0.85811633 0.85811633 0.9047619 ]

mean value: 0.8997169740060625

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.57142857 0.85714286 0.
 1.         1.         0.5        1.        ]

mean value: 0.5595238095238095

key: train_fscore 
value: [0.93023256 0.97560976 1.         0.95       0.95       0.94736842
 0.92682927 0.92682927 0.92682927 0.95238095]

mean value: 0.9486079492548729

key: test_precision 
value: [0.   0.25 0.25 0.5  0.75 0.   1.   1.   0.5  1.  ]

mean value: 0.525

key: train_precision 
value: [0.90909091 1.         1.         0.95       0.95       1.
 0.95       0.95       0.95       0.95238095]

mean value: 0.9611471861471861

key: test_recall 
value: [0.         0.5        0.5        0.66666667 1.         0.
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_recall 
value: [0.95238095 0.95238095 1.         0.95       0.95       0.9
 0.9047619  0.9047619  0.9047619  0.95238095]

mean value: 0.9371428571428572

key: test_accuracy 
value: [0.4 0.2 0.2 0.4 0.8 0.2 1.  1.  0.5 1. ]

mean value: 0.5700000000000001

key: train_accuracy 
value: [0.92682927 0.97560976 1.         0.95121951 0.95121951 0.95121951
 0.92857143 0.92857143 0.92857143 0.95238095]

mean value: 0.9494192799070849

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.33333333 0.75       0.25
 1.         1.         0.5        1.        ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [0.92619048 0.97619048 1.         0.95119048 0.95119048 0.95
 0.92857143 0.92857143 0.92857143 0.95238095]

mean value: 0.9492857142857142

key: test_jcc 
value: [0.         0.2        0.2        0.4        0.75       0.
 1.         1.         0.33333333 1.        ]

mean value: 0.4883333333333333

key: train_jcc 
value: [0.86956522 0.95238095 1.         0.9047619  0.9047619  0.9
 0.86363636 0.86363636 0.86363636 0.90909091]

mean value: 0.9031469979296066

MCC on Blind test: 0.03

MCC on Training: 0.1

Running classifier: 12 
Model_name: Logistic RegressionCV 
Model func: LogisticRegressionCV(cv=3, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegressionCV(cv=3, random_state=42))])

key: fit_time 
value: [0.14993048 0.1616044  0.152004   0.169487   0.18104076 0.15314579
 0.16439247 0.16538048 0.16092014 0.17207479]

mean value: 0.16299803256988527

key: score_time 
value: [0.00850177 0.00943732 0.00930285 0.00938153 0.00937605 0.00874543
 0.00849867 0.00855517 0.00860405 0.0086534 ]

mean value: 0.008905625343322754

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244 -0.40824829  0.16666667  0.
  1.          1.          0.          0.        ]

mean value: 0.012542521434735132

key: train_mcc 
value: [0.41487884 1.         0.7098505  1.         1.         0.95227002
 0.85811633 1.         1.         1.        ]

mean value: 0.8935115692085429

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.57142857 0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4904761904761905

key: train_fscore 
value: [0.72727273 1.         0.86363636 1.         1.         0.97435897
 0.92682927 1.         1.         1.        ]

mean value: 0.9492097333560748

key: test_precision 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.         0.25       0.25       0.5        0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4666666666666666

key: train_precision 
value: [0.69565217 1.         0.82608696 1.         1.         1.
 0.95       1.         1.         1.        ]

mean value: 0.9471739130434782

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.5333333333333333

key: train_recall 
value: [0.76190476 1.         0.9047619  1.         1.         0.95
 0.9047619  1.         1.         1.        ]

mean value: 0.9521428571428571

key: test_accuracy 
value: [0.4 0.2 0.2 0.4 0.6 0.4 1.  1.  0.5 0.5]

mean value: 0.52

key: train_accuracy 
value: [0.70731707 1.         0.85365854 1.         1.         0.97560976
 0.92857143 1.         1.         1.        ]

mean value: 0.9465156794425088

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.33333333 0.58333333 0.5
 1.         1.         0.5        0.5       ]

mean value: 0.525

key: train_roc_auc 
value: [0.70595238 1.         0.85238095 1.         1.         0.975
 0.92857143 1.         1.         1.        ]

mean value: 0.9461904761904762

key: test_jcc 
value: [0.         0.2        0.2        0.4        0.5        0.
 1.         1.         0.33333333 0.33333333]

mean value: 0.39666666666666667

key: train_jcc 
value: [0.57142857 1.         0.76       1.         1.         0.95
 0.86363636 1.         1.         1.        ]

mean value: 0.9145064935064935

MCC on Blind test: -0.05

MCC on Training: 0.01

Running classifier: 13 
Model_name: MLP 
Model func: MLPClassifier(max_iter=500, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MLPClassifier(max_iter=500, random_state=42))])

key: fit_time 
value: [0.24151373 0.31732178 0.20899272 0.2480824  0.23589945 0.24067688
 0.24676585 0.24599648 0.23300409 0.33954525]

mean value: 0.25577986240386963

key: score_time 
value: [0.01282716 0.01175332 0.01173353 0.01182961 0.01173878 0.01169705
 0.01172638 0.01176333 0.01172805 0.01187062]

mean value: 0.01186678409576416

key: test_mcc 
value: [ 0.         -0.61237244 -0.16666667 -0.40824829  0.16666667  0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.19649778334938311

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.33333333 0.4        0.57142857 0.66666667 0.5
 1.         1.         0.5        0.8       ]

mean value: 0.5771428571428572

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.25       0.33333333 0.5        0.66666667 1.
 1.         1.         0.5        0.66666667]

mean value: 0.5916666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.33333333
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.4  0.4  0.6  0.6  1.   1.   0.5  0.75]

mean value: 0.6050000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.5        0.25       0.41666667 0.33333333 0.58333333 0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.2        0.25       0.4        0.5        0.33333333
 1.         1.         0.33333333 0.66666667]

mean value: 0.4683333333333334

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.2

Running classifier: 14 
Model_name: Multinomial 
Model func: MultinomialNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MultinomialNB())])

key: fit_time 
value: [0.01154876 0.01127577 0.00858712 0.00814319 0.00837946 0.00889277
 0.00902009 0.00868893 0.00828362 0.00838399]

mean value: 0.009120368957519531

key: score_time 
value: [0.01118398 0.01124835 0.00861812 0.00842786 0.00885057 0.00857306
 0.00891972 0.00833225 0.00849366 0.00825143]

mean value: 0.009089899063110352

key: test_mcc 
value: [-0.40824829  0.         -0.61237244 -0.40824829  0.16666667  0.
  1.          0.57735027  0.57735027  0.        ]

mean value: 0.08924981884223976

key: train_mcc 
value: [0.36515617 0.31666667 0.46623254 0.46300848 0.46300848 0.41428571
 0.43052839 0.38138504 0.42857143 0.43052839]

mean value: 0.415937128657245

key: test_fscore 
value: [0.         0.57142857 0.33333333 0.57142857 0.66666667 0.
 1.         0.8        0.8        0.5       ]

mean value: 0.5242857142857142

key: train_fscore 
value: [0.69767442 0.66666667 0.75555556 0.71794872 0.71794872 0.7
 0.72727273 0.69767442 0.71428571 0.72727273]

mean value: 0.712229966416013

key: test_precision 
value: [0.         0.4        0.25       0.5        0.66666667 0.
 1.         0.66666667 0.66666667 0.5       ]

mean value: 0.46499999999999997

key: train_precision 
value: [0.68181818 0.66666667 0.70833333 0.73684211 0.73684211 0.7
 0.69565217 0.68181818 0.71428571 0.69565217]

mean value: 0.701791063627448

key: test_recall 
value: [0.         1.         0.5        0.66666667 0.66666667 0.
 1.         1.         1.         0.5       ]

mean value: 0.6333333333333333

key: train_recall 
value: [0.71428571 0.66666667 0.80952381 0.7        0.7        0.7
 0.76190476 0.71428571 0.71428571 0.76190476]

mean value: 0.7242857142857143

key: test_accuracy 
value: [0.4  0.4  0.2  0.4  0.6  0.4  1.   0.75 0.75 0.5 ]

mean value: 0.54

key: train_accuracy 
value: [0.68292683 0.65853659 0.73170732 0.73170732 0.73170732 0.70731707
 0.71428571 0.69047619 0.71428571 0.71428571]

mean value: 0.7077235772357724

key: test_roc_auc 
value: [0.33333333 0.5        0.25       0.33333333 0.58333333 0.5
 1.         0.75       0.75       0.5       ]

mean value: 0.55

key: train_roc_auc 
value: [0.68214286 0.65833333 0.7297619  0.73095238 0.73095238 0.70714286
 0.71428571 0.69047619 0.71428571 0.71428571]

mean value: 0.7072619047619048

key: test_jcc 
value: [0.         0.4        0.2        0.4        0.5        0.
 1.         0.66666667 0.66666667 0.33333333]

mean value: 0.41666666666666663

key: train_jcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.53571429 0.5        0.60714286 0.56       0.56       0.53846154
 0.57142857 0.53571429 0.55555556 0.57142857]

mean value: 0.5535445665445665

MCC on Blind test: 0.23

MCC on Training: 0.09

Running classifier: 15 
Model_name: Naive Bayes 
Model func: BernoulliNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', BernoulliNB())])

key: fit_time 
value: [0.00845838 0.00860071 0.00848722 0.00885081 0.00808978 0.00885248
 0.00819016 0.00901937 0.00845933 0.00805902]

mean value: 0.00850672721862793

key: score_time 
value: [0.00894594 0.00872087 0.00868893 0.00826478 0.00870943 0.00836611
 0.00838709 0.00906491 0.00829411 0.00821567]

mean value: 0.008565783500671387

key: test_mcc 
value: [ 0.61237244 -0.16666667 -0.61237244  1.         -0.61237244  0.40824829
  0.57735027  0.          0.57735027  0.57735027]

mean value: 0.2361259995670279

key: train_mcc 
value: [0.65952381 0.78072006 0.78072006 0.698212   0.65915306 0.81975606
 0.67357531 0.78446454 0.81322028 0.78446454]

mean value: 0.7453809730969173

key: test_fscore 
value: [0.66666667 0.4        0.33333333 1.         0.         0.5
 0.66666667 0.         0.8        0.66666667]

mean value: 0.5033333333333333

key: train_fscore 
value: [0.82926829 0.86486486 0.86486486 0.78787879 0.75       0.88888889
 0.82051282 0.86486486 0.9        0.86486486]

mean value: 0.8436008249422884

key: test_precision 
value: [1.         0.33333333 0.25       1.         0.         1.
 1.         0.         0.66666667 1.        ]

mean value: 0.625

key: train_precision 
value: [0.85       1.         1.         1.         1.         1.
 0.88888889 1.         0.94736842 1.        ]

mean value: 0.9686257309941521

key: test_recall 
value: [0.5        0.5        0.5        1.         0.         0.33333333
 0.5        0.         1.         0.5       ]

mean value: 0.4833333333333333

key: train_recall 
value: [0.80952381 0.76190476 0.76190476 0.65       0.6        0.8
 0.76190476 0.76190476 0.85714286 0.76190476]

mean value: 0.7526190476190475

key: test_accuracy 
value: [0.8  0.4  0.2  1.   0.2  0.6  0.75 0.5  0.75 0.75]

mean value: 0.595

key: train_accuracy 
value: [0.82926829 0.87804878 0.87804878 0.82926829 0.80487805 0.90243902
 0.83333333 0.88095238 0.9047619  0.88095238]

mean value: 0.8621951219512196

key: test_roc_auc 
value: [0.75       0.41666667 0.25       1.         0.25       0.66666667
 0.75       0.5        0.75       0.75      ]

mean value: 0.6083333333333333

key: train_roc_auc 
value: [0.8297619  0.88095238 0.88095238 0.825      0.8        0.9
 0.83333333 0.88095238 0.9047619  0.88095238]

mean value: 0.8616666666666667

key: test_jcc 
value: [0.5        0.25       0.2        1.         0.         0.33333333
 0.5        0.         0.66666667 0.5       ]

mean value: 0.39499999999999996

key: train_jcc 
value: [0.70833333 0.76190476 0.76190476 0.65       0.6        0.8
 0.69565217 0.76190476 0.81818182 0.76190476]

mean value: 0.7319786373047242

MCC on Blind test: -0.07

MCC on Training: 0.24

Running classifier: 16 
Model_name: Passive Aggresive 
Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 PassiveAggressiveClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00944281 0.00986218 0.00964999 0.00876284 0.00858927 0.00873113
 0.00857592 0.00833726 0.00859237 0.0086391 ]

mean value: 0.008918285369873047

key: score_time 
value: [0.00912499 0.00907922 0.00838161 0.00837588 0.00822067 0.00842881
 0.00826788 0.00829506 0.0084579  0.00854564]

mean value: 0.008517765998840332

key: test_mcc 
value: [-0.66666667 -0.16666667 -0.61237244 -0.40824829  0.16666667  0.
  1.          1.          0.          1.        ]

mean value: 0.13127126071736755

key: train_mcc 
value: [0.77831178 0.90692382 1.         0.74124932 0.95238095 0.95227002
 0.80952381 0.81322028 0.8660254  0.78446454]

mean value: 0.860436992906499

key: test_fscore 
value: [0.         0.4        0.33333333 0.57142857 0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.5471428571428572

key: train_fscore 
value: [0.89361702 0.95       1.         0.86956522 0.97560976 0.97435897
 0.9047619  0.90909091 0.92307692 0.86486486]

mean value: 0.9264945570919038

key: test_precision 
value: [0.         0.33333333 0.25       0.5        0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.525

key: train_precision 
value: [0.80769231 1.         1.         0.76923077 0.95238095 1.
 0.9047619  0.86956522 1.         1.        ]

mean value: 0.9303631151457239

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         1.         0.5        1.        ]

mean value: 0.5833333333333333

key: train_recall 
value: [1.         0.9047619  1.         1.         1.         0.95
 0.9047619  0.95238095 0.85714286 0.76190476]

mean value: 0.9330952380952382

key: test_accuracy 
value: [0.2 0.4 0.2 0.4 0.6 0.4 1.  1.  0.5 1. ]

mean value: 0.5700000000000001

key: train_accuracy 
value: [0.87804878 0.95121951 1.         0.85365854 0.97560976 0.97560976
 0.9047619  0.9047619  0.92857143 0.88095238]

mean value: 0.9253193960511034

key: test_roc_auc 
value: [0.16666667 0.41666667 0.25       0.33333333 0.58333333 0.5
 1.         1.         0.5        1.        ]

mean value: 0.575

key: train_roc_auc 
value: [0.875      0.95238095 1.         0.85714286 0.97619048 0.975
 0.9047619  0.9047619  0.92857143 0.88095238]

mean value: 0.9254761904761905

key: test_jcc 
value: [0.         0.25       0.2        0.4        0.5        0.
 1.         1.         0.33333333 1.        ]

mean value: 0.4683333333333334

key: train_jcc 
value: [0.80769231 0.9047619  1.         0.76923077 0.95238095 0.95
 0.82608696 0.83333333 0.85714286 0.76190476]

mean value: 0.8662533842968625

MCC on Blind test: 0.05

MCC on Training: 0.13

Running classifier: 17 
Model_name: QDA 
Model func: QuadraticDiscriminantAnalysis()

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', QuadraticDiscriminantAnalysis())])

key: fit_time 
value: [0.0084331  0.00824332 0.00820327 0.00829053 0.00904059 0.00834751
 0.00831366 0.00825167 0.00869656 0.00836062]

mean value: 0.008418083190917969

key: score_time 
value: [0.00837088 0.00824666 0.00837278 0.00829577 0.00903153 0.00833392
 0.0082221  0.00839305 0.00824666 0.00831914]

mean value: 0.00838325023651123

key: test_mcc 
value: [-0.40824829 -0.40824829  0.          0.16666667  0.66666667  0.40824829
 -0.57735027  1.         -0.57735027  0.        ]

mean value: 0.027038450449021856

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.         0.         0.66666667 0.8        0.5
 0.4        1.         0.4        0.5       ]

mean value: 0.42666666666666664

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.         0.         0.66666667 1.         1.
 0.33333333 1.         0.33333333 0.5       ]

mean value: 0.4833333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.         0.         0.66666667 0.66666667 0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.41666666666666663

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.6  0.6  0.8  0.6  0.25 1.   0.25 0.5 ]

mean value: 0.54

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.33333333 0.5        0.58333333 0.83333333 0.66666667
 0.25       1.         0.25       0.5       ]

mean value: 0.525

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.         0.         0.5        0.66666667 0.33333333
 0.25       1.         0.25       0.33333333]

mean value: 0.33333333333333337

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.07

MCC on Training: 0.03

Running classifier: 18 
Model_name: Random Forest 
Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(n_estimators=1000, n_jobs=12,
                                        random_state=42))])

key: fit_time 
value: [0.60021329 0.57720327 0.54249668 0.54380131 0.54201055 0.56307817
 0.5312767  0.56808448 0.59204793 0.53911257]

mean value: 0.5599324941635132

key: score_time 
value: [0.18375874 0.14311218 0.16572428 0.13906121 0.1496129  0.14000821
 0.14519739 0.13516092 0.16832924 0.17730117]

mean value: 0.15472662448883057

key: test_mcc 
value: [ 0.16666667 -1.          0.40824829 -0.40824829  1.          0.40824829
  0.57735027  1.          0.57735027 -0.57735027]

mean value: 0.21522652263201553

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.66666667 0.57142857 1.         0.5
 0.66666667 1.         0.8        0.4       ]

mean value: 0.6104761904761905

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.5        0.5        1.         1.
 1.         1.         0.66666667 0.33333333]

mean value: 0.65

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         1.         0.5       ]

mean value: 0.65

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.   0.6  0.4  1.   0.6  0.75 1.   0.75 0.25]

mean value: 0.595

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.         0.66666667 0.33333333 1.         0.66666667
 0.75       1.         0.75       0.25      ]

mean value: 0.6

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.5        0.4        1.         0.33333333
 0.5        1.         0.66666667 0.25      ]

mean value: 0.49833333333333335

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.22

Running classifier: 19 
Model_name: Random Forest2 
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...age_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                                        n_estimators=1000, n_jobs=12,
                                        oob_score=True, random_state=42))])

key: fit_time 
value: [0.88413286 0.83431458 0.85372138 0.92493558 0.80129886 0.96354318
 0.86790776 0.80547285 0.90535116 0.86681151]

mean value: 0.8707489728927612

key: score_time 
value: [0.18714929 0.14363599 0.18987608 0.16804981 0.18646693 0.21752691
 0.18257451 0.16987777 0.17513752 0.19997525]

mean value: 0.182027006149292

key: test_mcc 
value: [ 0.16666667 -1.          0.40824829 -0.40824829  0.61237244  0.40824829
  1.          1.          0.57735027  0.        ]

mean value: 0.276463766201595

key: train_mcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.7565654  0.90692382 0.8047619  0.90238095 0.8547619  0.85441771
 0.9047619  0.80952381 0.81322028 0.80952381]

mean value: 0.841684150259194

key: test_fscore 
value: [0.5        0.         0.66666667 0.57142857 0.85714286 0.5
 1.         1.         0.8        0.5       ]

mean value: 0.6395238095238095

key: train_fscore 
value: [0.88372093 0.95       0.9047619  0.95       0.92682927 0.92307692
 0.95238095 0.9047619  0.9        0.9047619 ]

mean value: 0.9200293788268832

key: test_precision 
value: [0.5        0.         0.5        0.5        0.75       1.
 1.         1.         0.66666667 0.5       ]

mean value: 0.6416666666666667

key: train_precision 
value: [0.86363636 1.         0.9047619  0.95       0.9047619  0.94736842
 0.95238095 0.9047619  0.94736842 0.9047619 ]

mean value: 0.9279801777170199

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.7

key: train_recall 
value: [0.9047619  0.9047619  0.9047619  0.95       0.95       0.9
 0.95238095 0.9047619  0.85714286 0.9047619 ]

mean value: 0.9133333333333334

key: test_accuracy 
value: [0.6  0.   0.6  0.4  0.8  0.6  1.   1.   0.75 0.5 ]

mean value: 0.625

key: train_accuracy 
value: [0.87804878 0.95121951 0.90243902 0.95121951 0.92682927 0.92682927
 0.95238095 0.9047619  0.9047619  0.9047619 ]

mean value: 0.9203252032520325

key: test_roc_auc 
value: [0.58333333 0.         0.66666667 0.33333333 0.75       0.66666667
 1.         1.         0.75       0.5       ]

mean value: 0.625

key: train_roc_auc 
value: [0.87738095 0.95238095 0.90238095 0.95119048 0.92738095 0.92619048
 0.95238095 0.9047619  0.9047619  0.9047619 ]

mean value: 0.9203571428571428

key: test_jcc 
value: [0.33333333 0.         0.5        0.4        0.75       0.33333333
 1.         1.         0.66666667 0.33333333]

mean value: 0.5316666666666666

key: train_jcc 
value: [0.79166667 0.9047619  0.82608696 0.9047619  0.86363636 0.85714286
 0.90909091 0.82608696 0.81818182 0.82608696]

mean value: 0.8527503293807641

MCC on Blind test: -0.13

MCC on Training: 0.28

Running classifier: 20 
Model_name: Ridge Classifier 
Model func: RidgeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifier(random_state=42))])

key: fit_time 
value: [0.02394223 0.00923705 0.0101018  0.00940156 0.00993848 0.00877523
 0.00975657 0.01009393 0.00900698 0.00886273]

mean value: 0.010911655426025391

key: score_time 
value: [0.01595521 0.00951576 0.00970626 0.00916386 0.00844979 0.00879884
 0.00896311 0.00875783 0.00836921 0.00870633]

mean value: 0.009638619422912598

key: test_mcc 
value: [-0.40824829 -0.16666667 -0.61237244 -0.66666667  0.61237244  0.
  1.          1.          0.          1.        ]

mean value: 0.17584183762028038

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.4        0.33333333 0.33333333 0.85714286 0.
 1.         1.         0.5        1.        ]

mean value: 0.5423809523809524

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.33333333 0.25       0.33333333 0.75       0.
 1.         1.         0.5        1.        ]

mean value: 0.5166666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.5        0.33333333 1.         0.
 1.         1.         0.5        1.        ]

mean value: 0.5833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4 0.4 0.2 0.2 0.8 0.4 1.  1.  0.5 1. ]

mean value: 0.5900000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.41666667 0.25       0.16666667 0.75       0.5
 1.         1.         0.5        1.        ]

mean value: 0.5916666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.25       0.2        0.2        0.75       0.
 1.         1.         0.33333333 1.        ]

mean value: 0.47333333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.31

MCC on Training: 0.18

Running classifier: 21 
Model_name: Ridge ClassifierCV 
Model func: RidgeClassifierCV(cv=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifierCV(cv=3))])

key: fit_time 
value: [0.02831769 0.02491283 0.02651954 0.02506971 0.02484965 0.02458382
 0.02456856 0.02427793 0.02577114 0.02595329]

mean value: 0.0254824161529541

key: score_time 
value: [0.00875139 0.00857258 0.00846076 0.0084796  0.00839949 0.00886321
 0.00845504 0.00846457 0.00845838 0.008497  ]

mean value: 0.008540201187133788

key: test_mcc 
value: [ 0.         -0.16666667 -0.61237244 -0.66666667  0.61237244  0.40824829
  1.          1.          0.          0.57735027]

mean value: 0.21522652263201558

key: train_mcc 
value: [1.         1.         0.8547619  1.         1.         1.
 0.85811633 1.         1.         1.        ]

mean value: 0.9712878235082938

key: test_fscore 
value: [0.         0.4        0.33333333 0.33333333 0.85714286 0.5
 1.         1.         0.5        0.66666667]

mean value: 0.5590476190476191

key: train_fscore 
value: [1.         1.         0.92682927 1.         1.         1.
 0.92682927 1.         1.         1.        ]

mean value: 0.9853658536585366

key: test_precision 
value: [0.         0.33333333 0.25       0.33333333 0.75       1.
 1.         1.         0.5        1.        ]

mean value: 0.6166666666666666

key: train_precision 
value: [1.   1.   0.95 1.   1.   1.   0.95 1.   1.   1.  ]

mean value: 0.99

key: test_recall 
value: [0.         0.5        0.5        0.33333333 1.         0.33333333
 1.         1.         0.5        0.5       ]

mean value: 0.5666666666666667

key: train_recall 
value: [1.        1.        0.9047619 1.        1.        1.        0.9047619
 1.        1.        1.       ]

mean value: 0.980952380952381

key: test_accuracy 
value: [0.6  0.4  0.2  0.2  0.8  0.6  1.   1.   0.5  0.75]

mean value: 0.605

key: train_accuracy 
value: [1.         1.         0.92682927 1.         1.         1.
 0.92857143 1.         1.         1.        ]

mean value: 0.9855400696864113

key: test_roc_auc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.5        0.41666667 0.25       0.16666667 0.75       0.66666667
 1.         1.         0.5        0.75      ]

mean value: 0.6

key: train_roc_auc 
value: [1.         1.         0.92738095 1.         1.         1.
 0.92857143 1.         1.         1.        ]

mean value: 0.9855952380952381

key: test_jcc 
value: [0.         0.25       0.2        0.2        0.75       0.33333333
 1.         1.         0.33333333 0.5       ]

mean value: 0.45666666666666667

key: train_jcc 
value: [1.         1.         0.86363636 1.         1.         1.
 0.86363636 1.         1.         1.        ]

mean value: 0.9727272727272727

MCC on Blind test: -0.31

MCC on Training: 0.22

Running classifier: 22 
Model_name: SVC 
Model func: SVC(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SVC(random_state=42))])

key: fit_time 
value: [0.00965905 0.00967765 0.00953698 0.00963664 0.00914717 0.00982451
 0.00912523 0.00886345 0.0094161  0.00933146]

mean value: 0.009421825408935547

key: score_time 
value: [0.00935841 0.00985456 0.00974321 0.00947905 0.00937629 0.00971651
 0.0096314  0.00936913 0.00922918 0.00883269]

mean value: 0.0094590425491333

key: test_mcc 
value: [-0.40824829 -0.16666667 -0.16666667 -0.40824829  0.16666667  0.
  1.          0.57735027  0.          0.        ]

mean value: 0.059418702159523294

key: train_mcc 
value: [0.65952381 0.8547619  0.75714286 0.7633652  0.65871309 0.85441771
 0.76980036 0.62187434 0.71428571 0.71754731]

mean value: 0.7371432287703027

key: test_fscore 
value: [0.         0.4        0.4        0.57142857 0.66666667 0.
 1.         0.66666667 0.5        0.5       ]

mean value: 0.4704761904761904

key: train_fscore 
value: [0.82926829 0.92682927 0.87804878 0.86486486 0.82051282 0.92307692
 0.87179487 0.8        0.85714286 0.86363636]

mean value: 0.8635175042492115

key: test_precision 
value: [0.         0.33333333 0.33333333 0.5        0.66666667 0.
 1.         1.         0.5        0.5       ]

mean value: 0.4833333333333333

key: train_precision 
value: [0.85       0.95       0.9        0.94117647 0.84210526 0.94736842
 0.94444444 0.84210526 0.85714286 0.82608696]

mean value: 0.8900429676065696

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         0.5        0.5        0.5       ]

mean value: 0.4833333333333333

key: train_recall 
value: [0.80952381 0.9047619  0.85714286 0.8        0.8        0.9
 0.80952381 0.76190476 0.85714286 0.9047619 ]

mean value: 0.8404761904761904

key: test_accuracy 
value: [0.4  0.4  0.4  0.4  0.6  0.4  1.   0.75 0.5  0.5 ]

mean value: 0.5349999999999999

key: train_accuracy 
value: [0.82926829 0.92682927 0.87804878 0.87804878 0.82926829 0.92682927
 0.88095238 0.80952381 0.85714286 0.85714286]

mean value: 0.8673054587688733

key: test_roc_auc 
value: [0.33333333 0.41666667 0.41666667 0.33333333 0.58333333 0.5
 1.         0.75       0.5        0.5       ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [0.8297619  0.92738095 0.87857143 0.87619048 0.82857143 0.92619048
 0.88095238 0.80952381 0.85714286 0.85714286]

mean value: 0.8671428571428572

key: test_jcc 
value: [0.         0.25       0.25       0.4        0.5        0.
 1.         0.5        0.33333333 0.33333333]

mean value: 0.3566666666666667

key: train_jcc 
value: [0.70833333 0.86363636 0.7826087  0.76190476 0.69565217 0.85714286
 0.77272727 0.66666667 0.75       0.76      ]

mean value: 0.7618672124976473

MCC on Blind test: 0.12

MCC on Training: 0.06

Running classifier: 23 
Model_name: Stochastic GDescent 
Model func: SGDClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SGDClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00848198 0.00838614 0.00836706 0.00840068 0.00844431 0.00905252
 0.00888348 0.00917697 0.00973868 0.00835919]

mean value: 0.008729100227355957

key: score_time 
value: [0.00826812 0.00857329 0.00815392 0.00844526 0.00853515 0.00836849
 0.00935626 0.00903535 0.00908804 0.00849342]

mean value: 0.008631730079650879

key: test_mcc 
value: [-0.40824829 -0.61237244 -0.61237244  0.16666667  0.          0.
  1.          0.57735027  0.          0.57735027]

mean value: 0.0688374043190466

key: train_mcc 
value: [0.90649828 1.         1.         0.95227002 0.70272837 1.
 0.55901699 0.40824829 0.36760731 0.8660254 ]

mean value: 0.7762394663113877

key: test_fscore 
value: [0.         0.33333333 0.33333333 0.66666667 0.75       0.
 1.         0.66666667 0.66666667 0.66666667]

mean value: 0.5083333333333334

key: train_fscore 
value: [0.95454545 1.         1.         0.97435897 0.85106383 1.
 0.64516129 0.44444444 0.72413793 0.92307692]

mean value: 0.8516788847570094

key: test_precision 
value: [0.         0.25       0.25       0.66666667 0.6        0.
 1.         1.         0.5        1.        ]

mean value: 0.5266666666666666

key: train_precision 
value: [0.91304348 1.         1.         1.         0.74074074 1.
 1.         1.         0.56756757 1.        ]

mean value: 0.9221351786569176

key: test_recall 
value: [0.         0.5        0.5        0.66666667 1.         0.
 1.         0.5        1.         0.5       ]

mean value: 0.5666666666666667

key: train_recall 
value: [1.         1.         1.         0.95       1.         1.
 0.47619048 0.28571429 1.         0.85714286]

mean value: 0.856904761904762

key: test_accuracy 
value: [0.4  0.2  0.2  0.6  0.6  0.4  1.   0.75 0.5  0.75]

mean value: 0.54

key: train_accuracy 
value: [0.95121951 1.         1.         0.97560976 0.82926829 1.
 0.73809524 0.64285714 0.61904762 0.92857143]

mean value: 0.8684668989547039

key: test_roc_auc 
value: [0.33333333 0.25       0.25       0.58333333 0.5        0.5
 1.         0.75       0.5        0.75      ]

mean value: 0.5416666666666666

key: train_roc_auc 
value: [0.95       1.         1.         0.975      0.83333333 1.
 0.73809524 0.64285714 0.61904762 0.92857143]

mean value: 0.8686904761904761

key: test_jcc 
value: [0.  0.2 0.2 0.5 0.6 0.  1.  0.5 0.5 0.5]

mean value: 0.4

key: train_jcc 
value: [0.91304348 1.         1.         0.95       0.74074074 1.
 0.47619048 0.28571429 0.56756757 0.85714286]

mean value: 0.7790399405616796

MCC on Blind test: 0.0

MCC on Training: 0.07

Running classifier: 24 
Model_name: XGBoost 
Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:427: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_CV['source_data'] = 'CV'
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:454: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_BT['source_data'] = 'BT'
XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...
                               interaction_constraints=None, learning_rate=None,
                               max_delta_step=None, max_depth=None,
                               min_child_weight=None, missing=nan,
                               monotone_constraints=None, n_estimators=100,
                               n_jobs=12, num_parallel_tree=None,
                               predictor=None, random_state=42, reg_alpha=None,
                               reg_lambda=None, scale_pos_weight=None,
                               subsample=None, tree_method=None,
                               use_label_encoder=False,
                               validate_parameters=None, verbosity=0))])

key: fit_time 
value: [0.03942108 0.0357883  0.03759861 0.03542089 0.0371716  0.03603816
 0.03947306 0.20275068 0.03404307 0.03263688]

mean value: 0.05303423404693604

key: score_time 
value: [0.01133871 0.01178408 0.01097012 0.01137185 0.01078272 0.01107693
 0.01159072 0.01123071 0.01056361 0.01047015]

mean value: 0.011117959022521972

key: test_mcc 
value: [ 0.61237244 -0.16666667  0.         -0.66666667  0.66666667  0.40824829
  0.57735027  0.57735027  0.57735027  0.57735027]

mean value: 0.31633551362514944

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.4        0.57142857 0.33333333 0.8        0.5
 0.66666667 0.66666667 0.8        0.8       ]

mean value: 0.6204761904761904

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.33333333 0.4        0.33333333 1.         1.
 1.         1.         0.66666667 0.66666667]

mean value: 0.74

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 0.66666667 0.33333333
 0.5        0.5        1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.4  0.4  0.2  0.8  0.6  0.75 0.75 0.75 0.75]

mean value: 0.62

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.41666667 0.5        0.16666667 0.83333333 0.66666667
 0.75       0.75       0.75       0.75      ]

mean value: 0.6333333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.25       0.4        0.2        0.66666667 0.33333333
 0.5        0.5        0.66666667 0.66666667]

mean value: 0.4683333333333334

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.22

MCC on Training: 0.32

Extracting tts_split_name: 70_30

Total cols in each df: 
CV df: 8 
metaDF: 15

Adding column: Model_name

Total cols in bts df: 
BT_df: 8

First proceeding to rowbind CV and BT dfs:

Final output should have: 23 columns

Combinig 2 using pd.concat by row ~ rowbind 
Checking Dims of df to combine: 
Dim of CV: (24, 8) 
Dim of BT: (24, 8)
8

Number of Common columns: 8 
These are: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall']

Concatenating dfs with different resampling methods [WF]: 
Split type: 70_30 
No. of dfs combining: 2

PASS: 2 dfs successfully combined 
nrows in combined_df_wf: 48 
ncols in combined_df_wf: 8

PASS: proceeding to merge metadata with CV and BT dfs

Adding column: Model_name

========================================================= 
SUCCESS: Ran multiple classifiers 
=======================================================

==============================================================
 
Running several classification models (n): 24 
List of models:
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3))
('Decision Tree', DecisionTreeClassifier(random_state=42))
('Extra Tree', ExtraTreeClassifier(random_state=42))
('Extra Trees', ExtraTreesClassifier(random_state=42))
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
('Gaussian NB', GaussianNB())
('Gaussian Process', GaussianProcessClassifier(random_state=42))
('K-Nearest Neighbors', KNeighborsClassifier())
('LDA', LinearDiscriminantAnalysis())
('Logistic Regression', LogisticRegression(random_state=42))
('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
('MLP', MLPClassifier(max_iter=500, random_state=42))
('Multinomial', MultinomialNB())
('Naive Bayes', BernoulliNB())
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
('QDA', QuadraticDiscriminantAnalysis())
('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42))
('Ridge Classifier', RidgeClassifier(random_state=42))
('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
('SVC', SVC(random_state=42))
('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0))

================================================================


Running classifier: 1 
Model_name: AdaBoost Classifier 
Model func: AdaBoostClassifier(random_state=42)

Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend 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 2 of 8 for this parallel run (total 100)...
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 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 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 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
q<00>:l3V@/<2F><>(Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
<00>loky_p<5F><00><><E0>8VP|=׍[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
<00><><EF><FF><FF><FF><FF><FF>q@<40><>.<2E>@<40><>.<2E>[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.4s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.4s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
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 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', AdaBoostClassifier(random_state=42))])

key: fit_time 
value: [0.07088137 0.06979895 0.07155275 0.07130504 0.07216024 0.07068849
 0.07368135 0.07089376 0.07142544 0.07148743]

mean value: 0.07138748168945312

key: score_time 
value: [0.01498604 0.01561141 0.01537704 0.01454854 0.01553416 0.01516986
 0.0147202  0.01489377 0.01559353 0.01535773]

mean value: 0.01517922878265381

key: test_mcc 
value: [0.16666667 0.16666667 0.66666667 0.16666667 0.61237244 0.40824829
 0.57735027 0.         1.         0.57735027]

mean value: 0.43419879312055765

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.5        0.8        0.66666667 0.85714286 0.5
 0.8        0.5        1.         0.8       ]

mean value: 0.6923809523809524

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.5        0.66666667 0.66666667 0.75       1.
 0.66666667 0.5        1.         0.66666667]

mean value: 0.6916666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.75

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.6  0.8  0.6  0.8  0.6  0.75 0.5  1.   0.75]

mean value: 0.7

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.58333333 0.83333333 0.58333333 0.75       0.66666667
 0.75       0.5        1.         0.75      ]

mean value: 0.7

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.33333333 0.66666667 0.5        0.75       0.33333333
 0.66666667 0.33333333 1.         0.66666667]

mean value: 0.5583333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.03

MCC on Training: 0.43

Running classifier: 2 
Model_name: Bagging Classifier 
Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True,
                                   random_state=42, verbose=3))])

key: fit_time 
value: [0.0968864  0.11801028 0.09970188 0.12701225 0.14569211 0.10844421
 0.14443231 0.11996961 0.10739756 0.11337447]

mean value: 0.11809210777282715

key: score_time 
value: [0.06114459 0.03731775 0.06222534 0.07297015 0.05134463 0.05550885
 0.03839612 0.06989408 0.0383203  0.05145597]

mean value: 0.05385777950286865

key: test_mcc 
value: [0.16666667 0.61237244 1.         0.16666667 0.16666667 0.40824829
 0.57735027 0.         1.         1.        ]

mean value: 0.5097970995349284

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.66666667 1.         0.66666667 0.66666667 0.5
 0.8        0.66666667 1.         1.        ]

mean value: 0.7466666666666667

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        1.         1.         0.66666667 0.66666667 1.
 0.66666667 0.5        1.         1.        ]

mean value: 0.8

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.7666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.8  1.   0.6  0.6  0.6  0.75 0.5  1.   1.  ]

mean value: 0.745

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.75       1.         0.58333333 0.58333333 0.66666667
 0.75       0.5        1.         1.        ]

mean value: 0.7416666666666667

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.5        1.         0.5        0.5        0.33333333
 0.66666667 0.5        1.         1.        ]

mean value: 0.6333333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.31

MCC on Training: 0.51

Running classifier: 3 
Model_name: Decision Tree 
Model func: DecisionTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', DecisionTreeClassifier(random_state=42))])

key: fit_time 
value: [0.0103035  0.00883818 0.0100503  0.00899768 0.0089047  0.01392531
 0.01449227 0.01354194 0.00921726 0.00934529]

mean value: 0.010761642456054687

key: score_time 
value: [0.00900936 0.00859308 0.00837898 0.00829482 0.00833654 0.01341581
 0.0134666  0.0091486  0.00860786 0.00855803]

mean value: 0.00958096981048584

key: test_mcc 
value: [ 0.61237244  0.61237244  0.66666667  0.16666667  0.61237244  0.40824829
  0.57735027 -0.57735027  1.          0.57735027]

mean value: 0.46560492000742065

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 0.8        0.66666667 0.85714286 0.5
 0.8        0.4        1.         0.8       ]

mean value: 0.7157142857142857

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         0.66666667 0.66666667 0.75       1.
 0.66666667 0.33333333 1.         0.66666667]

mean value: 0.775

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.75

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  0.8  0.6  0.8  0.6  0.75 0.25 1.   0.75]

mean value: 0.7150000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       0.83333333 0.58333333 0.75       0.66666667
 0.75       0.25       1.         0.75      ]

mean value: 0.7083333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        0.66666667 0.5        0.75       0.33333333
 0.66666667 0.25       1.         0.66666667]

mean value: 0.5833333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.21

MCC on Training: 0.47

Running classifier: 4 
Model_name: Extra Tree 
Model func: ExtraTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreeClassifier(random_state=42))])

key: fit_time 
value: [0.00927901 0.0085938  0.0082202  0.00806785 0.00863695 0.00819731
 0.0090692  0.0092175  0.00897098 0.00883889]

mean value: 0.008709168434143067

key: score_time 
value: [0.00919247 0.00865507 0.00819445 0.00891638 0.00822783 0.00916386
 0.00907803 0.00921464 0.00912642 0.0083282 ]

mean value: 0.008809733390808105

key: test_mcc 
value: [ 0.61237244 -0.66666667  1.          0.16666667  0.61237244 -0.40824829
  0.57735027  0.          0.         -1.        ]

mean value: 0.0893846850117352

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.         1.         0.66666667 0.85714286 0.57142857
 0.8        0.66666667 0.5        0.        ]

mean value: 0.5728571428571428

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.         1.         0.66666667 0.75       0.5
 0.66666667 0.5        0.5        0.        ]

mean value: 0.5583333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.66666667
 1.         1.         0.5        0.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.2  1.   0.6  0.8  0.4  0.75 0.5  0.5  0.  ]

mean value: 0.555

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.16666667 1.         0.58333333 0.75       0.33333333
 0.75       0.5        0.5        0.        ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.         1.         0.5        0.75       0.4
 0.66666667 0.5        0.33333333 0.        ]

mean value: 0.46499999999999997

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.14

MCC on Training: 0.09

Running classifier: 5 
Model_name: Extra Trees 
Model func: ExtraTreesClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreesClassifier(random_state=42))])

key: fit_time 
value: [0.08186746 0.07829213 0.07604194 0.07571936 0.07979321 0.07597041
 0.0771172  0.07677698 0.07582831 0.07556725]

mean value: 0.07729742527008057

key: score_time 
value: [0.01793885 0.01814055 0.01664257 0.01657104 0.01739144 0.01732755
 0.01675653 0.01675177 0.01702929 0.01664209]

mean value: 0.017119169235229492

key: test_mcc 
value: [ 0.16666667 -0.66666667  0.16666667  0.16666667  1.         -0.16666667
 -0.57735027  0.          1.          0.57735027]

mean value: 0.16666666666666666

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.5        0.66666667 1.         0.4
 0.4        0.66666667 1.         0.66666667]

mean value: 0.58

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.5        0.66666667 1.         0.5
 0.33333333 0.5        1.         1.        ]

mean value: 0.6

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         0.5        0.66666667 1.         0.33333333
 0.5        1.         1.         0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.2  0.6  0.6  1.   0.4  0.25 0.5  1.   0.75]

mean value: 0.5900000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.16666667 0.58333333 0.58333333 1.         0.41666667
 0.25       0.5        1.         0.75      ]

mean value: 0.5833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.33333333 0.5        1.         0.25
 0.25       0.5        1.         0.5       ]

mean value: 0.4666666666666666

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.15

MCC on Training: 0.17

Running classifier: 6 
Model_name: Gradient Boosting 
Model func: GradientBoostingClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GradientBoostingClassifier(random_state=42))])

key: fit_time 
value: [0.12004709 0.11619353 0.11725736 0.11747646 0.11872649 0.10573745
 0.10928178 0.1207552  0.11994958 0.11960483]

mean value: 0.11650297641754151

key: score_time 
value: [0.00889969 0.00885892 0.00928378 0.00892401 0.00893497 0.00892329
 0.00901747 0.00891471 0.00900674 0.00896883]

mean value: 0.008973240852355957

key: test_mcc 
value: [ 0.61237244  0.61237244  1.          0.16666667  1.          0.40824829
  0.57735027 -0.57735027  1.          0.        ]

mean value: 0.47996598285221187

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.66666667 1.         0.66666667 1.         0.5
 0.8        0.4        1.         0.5       ]

mean value: 0.72

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         1.         1.         0.66666667 1.         1.
 0.66666667 0.33333333 1.         0.5       ]

mean value: 0.8166666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.33333333
 1.         0.5        1.         0.5       ]

mean value: 0.7

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.8  1.   0.6  1.   0.6  0.75 0.25 1.   0.5 ]

mean value: 0.7300000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.75       1.         0.58333333 1.         0.66666667
 0.75       0.25       1.         0.5       ]

mean value: 0.725

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.5        1.         0.5        1.         0.33333333
 0.66666667 0.25       1.         0.33333333]

mean value: 0.6083333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.48

MCC on Training: 0.48

Running classifier: 7 
Model_name: Gaussian NB 
Model func: GaussianNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianNB())])

key: fit_time 
value: [0.00813937 0.00863266 0.00883198 0.00876927 0.00880313 0.00876546
 0.00889754 0.0079639  0.008147   0.00815392]

mean value: 0.008510422706604005

key: score_time 
value: [0.00908256 0.00896645 0.0085392  0.00891399 0.00834346 0.00852489
 0.00860429 0.00880432 0.00860095 0.00899816]

mean value: 0.008737826347351074

key: test_mcc 
value: [0.16666667 0.         0.40824829 0.61237244 0.61237244 0.40824829
 0.         0.57735027 0.57735027 0.        ]

mean value: 0.33626086573652336

key: train_mcc 
value: [0.61969655 0.62048368 0.53206577 0.56010413 0.59982886 0.63994524
 0.58834841 0.54659439 0.56652882 0.56652882]

mean value: 0.5840124674124577

key: test_fscore 
value: [0.5        0.57142857 0.66666667 0.85714286 0.85714286 0.5
 0.66666667 0.8        0.8        0.66666667]

mean value: 0.6885714285714286

key: train_fscore 
value: [0.82608696 0.82352941 0.79166667 0.79166667 0.80851064 0.82608696
 0.80851064 0.79166667 0.8        0.8       ]

mean value: 0.8067724601403929

key: test_precision 
value: [0.5        0.4        0.5        0.75       0.75       1.
 0.5        0.66666667 0.66666667 0.5       ]

mean value: 0.6233333333333333

key: train_precision 
value: [0.76       0.7        0.7037037  0.67857143 0.7037037  0.73076923
 0.73076923 0.7037037  0.68965517 0.68965517]

mean value: 0.7090531346048587

key: test_recall 
value: [0.5        1.         1.         1.         1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.8833333333333332

key: train_recall 
value: [0.9047619  1.         0.9047619  0.95       0.95       0.95
 0.9047619  0.9047619  0.95238095 0.95238095]

mean value: 0.9373809523809523

key: test_accuracy 
value: [0.6  0.4  0.6  0.8  0.8  0.6  0.5  0.75 0.75 0.5 ]

mean value: 0.63

key: train_accuracy 
value: [0.80487805 0.7804878  0.75609756 0.75609756 0.7804878  0.80487805
 0.78571429 0.76190476 0.76190476 0.76190476]

mean value: 0.7754355400696864

key: test_roc_auc 
value: [0.58333333 0.5        0.66666667 0.75       0.75       0.66666667
 0.5        0.75       0.75       0.5       ]

mean value: 0.6416666666666666

key: train_roc_auc 
value: [0.80238095 0.775      0.75238095 0.76071429 0.78452381 0.80833333
 0.78571429 0.76190476 0.76190476 0.76190476]

mean value: 0.7754761904761904

key: test_jcc 
value: [0.33333333 0.4        0.5        0.75       0.75       0.33333333
 0.5        0.66666667 0.66666667 0.5       ]

mean value: 0.54

key: train_jcc 
value: [0.7037037  0.7        0.65517241 0.65517241 0.67857143 0.7037037
 0.67857143 0.65517241 0.66666667 0.66666667]

mean value: 0.6763400839262909

MCC on Blind test: -0.03

MCC on Training: 0.34

Running classifier: 8 
Model_name: Gaussian Process 
Model func: GaussianProcessClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianProcessClassifier(random_state=42))])

key: fit_time 
value: [0.01024461 0.01082993 0.00972795 0.01096725 0.00998783 0.00987554
 0.01061535 0.01060319 0.01106381 0.01122451]

mean value: 0.010513997077941895

key: score_time 
value: [0.00941777 0.00849223 0.00860977 0.0084219  0.00841165 0.00892353
 0.009655   0.00945258 0.0088129  0.00955749]

mean value: 0.008975481986999512

key: test_mcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[ 0.16666667 -1.          1.          0.16666667  1.         -0.16666667
 -0.57735027  0.          0.          0.57735027]

mean value: 0.11666666666666667

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         1.         0.66666667 1.         0.4
 0.4        0.66666667 0.5        0.66666667]

mean value: 0.58

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         1.         0.66666667 1.         0.5
 0.33333333 0.5        0.5        1.        ]

mean value: 0.6

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.   1.   0.6  1.   0.4  0.25 0.5  0.5  0.75]

mean value: 0.5599999999999999

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.         1.         0.58333333 1.         0.41666667
 0.25       0.5        0.5        0.75      ]

mean value: 0.5583333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         1.         0.5        1.         0.25
 0.25       0.5        0.33333333 0.5       ]

mean value: 0.4666666666666666

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.12

Running classifier: 9 
Model_name: K-Nearest Neighbors 
Model func: KNeighborsClassifier()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', KNeighborsClassifier())])

key: fit_time 
value: [0.00882387 0.00779724 0.00790095 0.00829983 0.00905085 0.0081563
 0.0088253  0.00841641 0.0089922  0.00852966]

mean value: 0.00847926139831543

key: score_time 
value: [0.0096581  0.0095911  0.00939512 0.00957799 0.00974464 0.0098207
 0.00983071 0.01009345 0.00980425 0.00920081]

mean value: 0.009671688079833984

key: test_mcc 
value: [ 0.16666667 -0.61237244  0.40824829  0.16666667 -0.40824829  0.
 -0.57735027  0.          0.57735027  0.        ]

mean value: -0.02790391023624612

key: train_mcc 
value: [0.31655495 0.47439956 0.51320273 0.51551459 0.46428571 0.56836003
 0.57735027 0.43656413 0.47673129 0.52380952]

mean value: 0.4866772800860473

key: test_fscore 
value: [0.5        0.33333333 0.66666667 0.66666667 0.57142857 0.
 0.4        0.66666667 0.8        0.5       ]

mean value: 0.5104761904761904

key: train_fscore 
value: [0.68181818 0.76595745 0.77272727 0.76190476 0.73170732 0.79069767
 0.8        0.73913043 0.74418605 0.76190476]

mean value: 0.7550033897949502

key: test_precision 
value: [0.5        0.25       0.5        0.66666667 0.5        0.
 0.33333333 0.5        0.66666667 0.5       ]

mean value: 0.4416666666666666

key: train_precision 
value: [0.65217391 0.69230769 0.73913043 0.72727273 0.71428571 0.73913043
 0.75       0.68       0.72727273 0.76190476]

mean value: 0.7183478405652319

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         0.5       ]

mean value: 0.6333333333333333

key: train_recall 
value: [0.71428571 0.85714286 0.80952381 0.8        0.75       0.85
 0.85714286 0.80952381 0.76190476 0.76190476]

mean value: 0.7971428571428572

key: test_accuracy 
value: [0.6  0.2  0.6  0.6  0.4  0.4  0.25 0.5  0.75 0.5 ]

mean value: 0.48

key: train_accuracy 
value: [0.65853659 0.73170732 0.75609756 0.75609756 0.73170732 0.7804878
 0.78571429 0.71428571 0.73809524 0.76190476]

mean value: 0.7414634146341463

key: test_roc_auc 
value: [0.58333333 0.25       0.66666667 0.58333333 0.33333333 0.5
 0.25       0.5        0.75       0.5       ]

mean value: 0.4916666666666667

key: train_roc_auc 
value: [0.65714286 0.72857143 0.7547619  0.75714286 0.73214286 0.78214286
 0.78571429 0.71428571 0.73809524 0.76190476]

mean value: 0.7411904761904762

key: test_jcc 
value: [0.33333333 0.2        0.5        0.5        0.4        0.
 0.25       0.5        0.66666667 0.33333333]

mean value: 0.3683333333333333

key: train_jcc 
value: [0.51724138 0.62068966 0.62962963 0.61538462 0.57692308 0.65384615
 0.66666667 0.5862069  0.59259259 0.61538462]

mean value: 0.6074565281461833

MCC on Blind test: 0.21

MCC on Training: -0.03

Running classifier: 10 
Model_name: LDA 
Model func: LinearDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LinearDiscriminantAnalysis())])

key: fit_time 
value: [0.01167679 0.01826382 0.01448941 0.01492167 0.01469254 0.01517916
 0.01869845 0.01910591 0.01449609 0.0146513 ]

mean value: 0.01561751365661621

key: score_time 
value: [0.01538086 0.01228762 0.01230597 0.01232481 0.01150227 0.01221585
 0.01167464 0.01225209 0.01155663 0.01207185]

mean value: 0.012357258796691894

key: test_mcc 
value: [-0.61237244  0.16666667  1.         -0.61237244 -0.66666667  0.66666667
  0.57735027  0.57735027  1.         -0.57735027]

mean value: 0.15192720644647034

key: train_mcc 
value: [0.8547619  0.75714286 0.90238095 0.90238095 0.90238095 0.8047619
 0.9047619  0.85811633 0.9047619  0.9047619 ]

mean value: 0.8696211568416272

key: test_fscore 
value: [0.33333333 0.5        1.         0.         0.33333333 0.8
 0.8        0.66666667 1.         0.4       ]

mean value: 0.5833333333333334

key: train_fscore 
value: [0.92682927 0.87804878 0.95238095 0.95       0.95       0.9
 0.95238095 0.92682927 0.95238095 0.95238095]

mean value: 0.934123112659698

key: test_precision 
value: [0.25       0.5        1.         0.         0.33333333 1.
 0.66666667 1.         1.         0.33333333]

mean value: 0.6083333333333333

key: train_precision 
value: [0.95       0.9        0.95238095 0.95       0.95       0.9
 0.95238095 0.95       0.95238095 0.95238095]

mean value: 0.940952380952381

key: test_recall 
value: [0.5        0.5        1.         0.         0.33333333 0.66666667
 1.         0.5        1.         0.5       ]

mean value: 0.6

key: train_recall 
value: [0.9047619  0.85714286 0.95238095 0.95       0.95       0.9
 0.95238095 0.9047619  0.95238095 0.95238095]

mean value: 0.9276190476190477

key: test_accuracy 
value: [0.2  0.6  1.   0.2  0.2  0.8  0.75 0.75 1.   0.25]

mean value: 0.575

key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
train_accuracy 
value: [0.92682927 0.87804878 0.95121951 0.95121951 0.95121951 0.90243902
 0.95238095 0.92857143 0.95238095 0.95238095]

mean value: 0.9346689895470384

key: test_roc_auc 
value: [0.25       0.58333333 1.         0.25       0.16666667 0.83333333
 0.75       0.75       1.         0.25      ]

mean value: 0.5833333333333334

key: train_roc_auc 
value: [0.92738095 0.87857143 0.95119048 0.95119048 0.95119048 0.90238095
 0.95238095 0.92857143 0.95238095 0.95238095]

mean value: 0.9347619047619047

key: test_jcc 
value: [0.2        0.33333333 1.         0.         0.2        0.66666667
 0.66666667 0.5        1.         0.25      ]

mean value: 0.48166666666666663

key: train_jcc 
value: [0.86363636 0.7826087  0.90909091 0.9047619  0.9047619  0.81818182
 0.90909091 0.86363636 0.90909091 0.90909091]

mean value: 0.8773950686994165

MCC on Blind test: -0.3

MCC on Training: 0.15

Running classifier: 11 
Model_name: Logistic Regression 
Model func: LogisticRegression(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegression(random_state=42))])

key: fit_time 
value: [0.02232027 0.01768303 0.01362634 0.01618624 0.01690435 0.01509452
 0.01629186 0.0160079  0.01721382 0.01615214]

mean value: 0.016748046875

key: score_time 
value: [0.01149535 0.00892115 0.0090301  0.00848508 0.00858998 0.00863743
 0.00846815 0.00854588 0.00891304 0.00888133]

mean value: 0.00899674892425537

key: test_mcc 
value: [-0.66666667  0.16666667  0.61237244  0.16666667  0.61237244  0.
  0.57735027  0.          1.          0.        ]

mean value: 0.24687618072478817

key: train_mcc 
value: [0.90238095 0.95238095 0.8547619  1.         0.90238095 0.90649828
 0.80952381 0.9047619  0.85811633 0.95346259]

mean value: 0.9044267675090705

key: test_fscore 
value: [0.         0.5        0.66666667 0.66666667 0.85714286 0.
 0.8        0.66666667 1.         0.66666667]

mean value: 0.5823809523809524

key: train_fscore 
value: [0.95238095 0.97560976 0.92682927 1.         0.95       0.94736842
 0.9047619  0.95238095 0.92682927 0.97674419]

mean value: 0.951290470930588

key: test_precision 
value: [0.         0.5        1.         0.66666667 0.75       0.
 0.66666667 0.5        1.         0.5       ]

mean value: 0.5583333333333333

key: train_precision 
value: [0.95238095 1.         0.95       1.         0.95       1.
 0.9047619  0.95238095 0.95       0.95454545]

mean value: 0.9614069264069263

key: test_recall 
value: [0.         0.5        0.5        0.66666667 1.         0.
 1.         1.         1.         1.        ]

mean value: 0.6666666666666666

key: train_recall 
value: [0.95238095 0.95238095 0.9047619  1.         0.95       0.9
 0.9047619  0.95238095 0.9047619  1.        ]

mean value: 0.9421428571428571

key: test_accuracy 
value: [0.2  0.6  0.8  0.6  0.8  0.4  0.75 0.5  1.   0.5 ]

mean value: 0.615

key: train_accuracy 
value: [0.95121951 0.97560976 0.92682927 1.         0.95121951 0.95121951
 0.9047619  0.95238095 0.92857143 0.97619048]

mean value: 0.9518002322880372

key: test_roc_auc 
value: [0.16666667 0.58333333 0.75       0.58333333 0.75       0.5
 0.75       0.5        1.         0.5       ]

mean value: 0.6083333333333333

key: train_roc_auc 
value: [0.95119048 0.97619048 0.92738095 1.         0.95119048 0.95
 0.9047619  0.95238095 0.92857143 0.97619048]

mean value: 0.9517857142857142

key: test_jcc 
value: [0.         0.33333333 0.5        0.5        0.75       0.
 0.66666667 0.5        1.         0.5       ]

mean value: 0.475

key: train_jcc 
value: [0.90909091 0.95238095 0.86363636 1.         0.9047619  0.9
 0.82608696 0.90909091 0.86363636 0.95454545]

mean value: 0.9083229813664596

MCC on Blind test: 0.03

MCC on Training: 0.25

Running classifier: 12 
Model_name: Logistic RegressionCV 
Model func: LogisticRegressionCV(cv=3, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegressionCV(cv=3, random_state=42))])

key: fit_time 
value: [0.15982699 0.18013906 0.17114401 0.16270256 0.18830061 0.18011808
 0.17512369 0.18504333 0.1956327  0.18354774]

mean value: 0.17815787792205812

key: score_time 
value: [0.00964713 0.00978851 0.00865817 0.00940061 0.00867319 0.00906944
 0.0096128  0.00865793 0.00980377 0.0095191 ]

mean value: 0.009283065795898438

key: test_mcc 
value: [-0.66666667  0.16666667  0.61237244  0.16666667  0.          0.40824829
  0.57735027  0.          1.          0.        ]

mean value: 0.226463766201595

key: train_mcc 
value: [0.8047619  1.         0.70714286 1.         0.         1.
 0.76277007 1.         1.         1.        ]

mean value: 0.8274674833301235

key: test_fscore 
value: [0.         0.5        0.66666667 0.66666667 0.         0.5
 0.8        0.66666667 1.         0.66666667]

mean value: 0.5466666666666666

key: train_fscore 
value: [0.9047619  1.         0.85714286 1.         0.         1.
 0.87804878 1.         1.         1.        ]

mean value: 0.8639953542392567

key: test_precision 
value: [0.         0.5        1.         0.66666667 0.         1.
 0.66666667 0.5        1.         0.5       ]

mean value: 0.5833333333333333

key: train_precision 
value: [0.9047619  1.         0.85714286 1.         0.         1.
 0.9        1.         1.         1.        ]

mean value: 0.8661904761904762

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6

key: train_recall 
value: [0.9047619  1.         0.85714286 1.         0.         1.
 0.85714286 1.         1.         1.        ]

mean value: 0.8619047619047618

key: test_accuracy 
value: [0.2  0.6  0.8  0.6  0.4  0.6  0.75 0.5  1.   0.5 ]

mean value: 0.595

key: train_accuracy 
value: [0.90243902 1.         0.85365854 1.         0.51219512 1.
 0.88095238 1.         1.         1.        ]

mean value: 0.914924506387921

key: test_roc_auc 
value: [0.16666667 0.58333333 0.75       0.58333333 0.5        0.66666667
 0.75       0.5        1.         0.5       ]

mean value: 0.6

key: train_roc_auc 
value: [0.90238095 1.         0.85357143 1.         0.5        1.
 0.88095238 1.         1.         1.        ]

mean value: 0.9136904761904763

key: test_jcc 
value: [0.         0.33333333 0.5        0.5        0.         0.33333333
 0.66666667 0.5        1.         0.5       ]

mean value: 0.4333333333333333

key: train_jcc 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
[0.82608696 1.         0.75       1.         0.         1.
 0.7826087  1.         1.         1.        ]

mean value: 0.8358695652173914

MCC on Blind test: -0.23

MCC on Training: 0.23

Running classifier: 13 
Model_name: MLP 
Model func: MLPClassifier(max_iter=500, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MLPClassifier(max_iter=500, random_state=42))])

key: fit_time 
value: [0.22335672 0.23056054 0.23545814 0.23782778 0.33668995 0.23442483
 0.24139619 0.23602247 0.24432111 0.23437071]

mean value: 0.24544284343719483

key: score_time 
value: [0.01173234 0.01174593 0.0117445  0.02407169 0.01178074 0.011729
 0.01169252 0.01177955 0.01169324 0.01172042]

mean value: 0.012968993186950684

key: test_mcc 
value: [-0.40824829  0.16666667 -0.40824829  0.16666667  0.40824829  0.40824829
  1.          0.          0.57735027  0.        ]

mean value: 0.19106836025229593

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.5        0.         0.66666667 0.5        0.5
 1.         0.66666667 0.66666667 0.66666667]

mean value: 0.5166666666666667

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.5        0.         0.66666667 1.         1.
 1.         0.5        1.         0.5       ]

mean value: 0.6166666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.         0.66666667 0.33333333 0.33333333
 1.         1.         0.5        1.        ]

mean value: 0.5333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.6  0.4  0.6  0.6  0.6  1.   0.5  0.75 0.5 ]

mean value: 0.595

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.58333333 0.33333333 0.58333333 0.66666667 0.66666667
 1.         0.5        0.75       0.5       ]

mean value: 0.5916666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.33333333 0.         0.5        0.33333333 0.33333333
 1.         0.5        0.5        0.5       ]

mean value: 0.4

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.19

Running classifier: 14 
Model_name: Multinomial 
Model func: MultinomialNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MultinomialNB())])

key: fit_time 
value: [0.01112938 0.01100969 0.00846815 0.00803924 0.00810933 0.00823998
 0.00898314 0.00798368 0.00822973 0.00795078]

mean value: 0.008814311027526856

key: score_time 
value: [0.01108384 0.01106858 0.00879216 0.00833678 0.00837255 0.00889945
 0.00877881 0.00815177 0.00817919 0.0082829 ]

mean value: 0.008994603157043457

key: test_mcc 
value: [-0.40824829  0.40824829 -0.16666667  0.16666667  0.16666667  0.
  0.          0.          0.57735027  0.        ]

mean value: 0.07440169358562924

key: train_mcc 
value: [0.2681441  0.65871309 0.41487884 0.41487884 0.51190476 0.41428571
 0.42857143 0.42857143 0.38138504 0.52620136]

mean value: 0.44475346110468605

key: test_fscore 
value: [0.         0.66666667 0.4        0.66666667 0.66666667 0.
 0.66666667 0.66666667 0.8        0.5       ]

mean value: 0.5033333333333333

key: train_fscore 
value: [0.66666667 0.8372093  0.72727273 0.68421053 0.75       0.7
 0.71428571 0.71428571 0.69767442 0.77272727]

mean value: 0.7264332342484117

key: test_precision 
value: [0.         0.5        0.33333333 0.66666667 0.66666667 0.
 0.5        0.5        0.66666667 0.5       ]

mean value: 0.4333333333333333

key: train_precision 
value: [0.625      0.81818182 0.69565217 0.72222222 0.75       0.7
 0.71428571 0.71428571 0.68181818 0.73913043]

mean value: 0.7160576259489303

key: test_recall 
value: [0.         1.         0.5        0.66666667 0.66666667 0.
 1.         1.         1.         0.5       ]

mean value: 0.6333333333333333

key: train_recall 
value: [0.71428571 0.85714286 0.76190476 0.65       0.75       0.7
 0.71428571 0.71428571 0.71428571 0.80952381]

mean value: 0.7385714285714287

key: test_accuracy 
value: [0.4  0.6  0.4  0.6  0.6  0.4  0.5  0.5  0.75 0.5 ]

mean value: 0.525

key: train_accuracy 
value: [0.63414634 0.82926829 0.70731707 0.70731707 0.75609756 0.70731707
 0.71428571 0.71428571 0.69047619 0.76190476]

mean value: 0.7222415795586528

key: test_roc_auc 
value: [0.33333333 0.66666667 0.41666667 0.58333333 0.58333333 0.5
 0.5        0.5        0.75       0.5       ]

mean value: 0.5333333333333333

key: train_roc_auc 
value: [0.63214286 0.82857143 0.70595238 0.70595238 0.75595238 0.70714286
 0.71428571 0.71428571 0.69047619 0.76190476]

mean value: 0.7216666666666667

key: test_jcc 
value: [0.         0.5        0.25       0.5        0.5        0.
 0.5        0.5        0.66666667 0.33333333]

mean value: 0.375

key: train_jcc 
value: [0.5        0.72       0.57142857 0.52       0.6        0.53846154
 0.55555556 0.55555556 0.53571429 0.62962963]

mean value: 0.5726345136345137

MCC on Blind test: 0.23

MCC on Training: 0.07

Running classifier: 15 
Model_name: Naive Bayes 
Model func: BernoulliNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', BernoulliNB())])

key: fit_time 
value: [0.00891638 0.00885677 0.00823188 0.00822496 0.00908804 0.00876379
 0.00821567 0.00836968 0.00843811 0.00831771]

mean value: 0.008542299270629883

key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")

value: [0.00907683 0.00833702 0.00835824 0.00836325 0.0092988  0.00829434
 0.00833154 0.00847101 0.00832009 0.00835919]

mean value: 0.008521032333374024

key: test_mcc 
value: [ 0.61237244 -0.16666667 -0.16666667  0.16666667 -0.61237244  0.40824829
  0.57735027 -0.57735027  0.57735027 -0.57735027]

mean value: 0.02415816237971965

key: train_mcc 
value: [0.78072006 0.7633652  0.62325386 0.73786479 0.73786479 0.73786479
 0.82462113 0.78446454 0.72760688 0.78446454]

mean value: 0.7502090562083469

key: test_fscore 
value: [0.66666667 0.4        0.4        0.66666667 0.         0.5
 0.66666667 0.         0.8        0.4       ]

mean value: 0.45

key: train_fscore 
value: [0.86486486 0.88888889 0.78947368 0.82352941 0.82352941 0.82352941
 0.89473684 0.86486486 0.84210526 0.86486486]

mean value: 0.8480387508251285

key: test_precision 
value: [1.         0.33333333 0.33333333 0.66666667 0.         1.
 1.         0.         0.66666667 0.33333333]

mean value: 0.5333333333333333

key: train_precision 
value: [1.         0.83333333 0.88235294 1.         1.         1.
 1.         1.         0.94117647 1.        ]

mean value: 0.9656862745098038

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.         0.33333333
 0.5        0.         1.         0.5       ]

mean value: 0.45

key: train_recall 
value: [0.76190476 0.95238095 0.71428571 0.7        0.7        0.7
 0.80952381 0.76190476 0.76190476 0.76190476]

mean value: 0.7623809523809524

key: test_accuracy 
value: [0.8  0.4  0.4  0.6  0.2  0.6  0.75 0.25 0.75 0.25]

mean value: 0.5

key: train_accuracy 
value: [0.87804878 0.87804878 0.80487805 0.85365854 0.85365854 0.85365854
 0.9047619  0.88095238 0.85714286 0.88095238]

mean value: 0.8645760743321718

key: test_roc_auc 
value: [0.75       0.41666667 0.41666667 0.58333333 0.25       0.66666667
 0.75       0.25       0.75       0.25      ]

mean value: 0.5083333333333333

key: train_roc_auc 
value: [0.88095238 0.87619048 0.80714286 0.85       0.85       0.85
 0.9047619  0.88095238 0.85714286 0.88095238]

mean value: 0.8638095238095238

key: test_jcc 
value: [0.5        0.25       0.25       0.5        0.         0.33333333
 0.5        0.         0.66666667 0.25      ]

mean value: 0.32499999999999996

key: train_jcc 
value: [0.76190476 0.8        0.65217391 0.7        0.7        0.7
 0.80952381 0.76190476 0.72727273 0.76190476]

mean value: 0.7374684735554301

MCC on Blind test: -0.07

MCC on Training: 0.02

Running classifier: 16 
Model_name: Passive Aggresive 
Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 PassiveAggressiveClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.0114274  0.00852418 0.00853562 0.00821495 0.00862932 0.00857162
 0.00912762 0.00930691 0.00964165 0.00927424]

mean value: 0.009125351905822754

key: score_time 
value: [0.0082233  0.00810313 0.00813222 0.00810933 0.00816798 0.00892162
 0.00892925 0.00901985 0.00906062 0.00914264]

mean value: 0.008580994606018067

key: test_mcc 
value: [-0.61237244 -0.16666667  0.40824829  0.16666667  0.          0.
  1.          0.57735027  1.          0.        ]

mean value: 0.2373226123957694

key: train_mcc 
value: [0.77831178 0.90649828 0.698212   0.86333169 0.8213423  0.90649828
 0.82462113 0.78446454 0.82462113 0.95346259]

mean value: 0.8361363718506268

key: test_fscore 
value: [0.33333333 0.4        0.66666667 0.66666667 0.75       0.
 1.         0.8        1.         0.66666667]

mean value: 0.6283333333333333

key: train_fscore 
value: [0.89361702 0.95454545 0.85714286 0.93023256 0.90909091 0.94736842
 0.89473684 0.86486486 0.91304348 0.97674419]

mean value: 0.9141386592525492

key: test_precision 
value: [0.25       0.33333333 0.5        0.66666667 0.6        0.
 1.         0.66666667 1.         0.5       ]

mean value: 0.5516666666666666

key: train_precision 
value: [0.80769231 0.91304348 0.75       0.86956522 0.83333333 1.
 1.         1.         0.84       0.95454545]

mean value: 0.896817979122327

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.
 1.         1.         1.         1.        ]

mean value: 0.7666666666666666

key: train_recall 
value: [1.         1.         1.         1.         1.         0.9
 0.80952381 0.76190476 1.         1.        ]

mean value: 0.9471428571428572

key: test_accuracy 
value: [0.2  0.4  0.6  0.6  0.6  0.4  1.   0.75 1.   0.5 ]

mean value: 0.605

key: train_accuracy 
value: [0.87804878 0.95121951 0.82926829 0.92682927 0.90243902 0.95121951
 0.9047619  0.88095238 0.9047619  0.97619048]

mean value: 0.9105691056910569

key: test_roc_auc 
value: [0.25       0.41666667 0.66666667 0.58333333 0.5        0.5
 1.         0.75       1.         0.5       ]

mean value: 0.6166666666666666

key: train_roc_auc 
value: [0.875      0.95       0.825      0.92857143 0.9047619  0.95
 0.9047619  0.88095238 0.9047619  0.97619048]

mean value: 0.9099999999999999

key: test_jcc 
value: [0.2        0.25       0.5        0.5        0.6        0.
 1.         0.66666667 1.         0.5       ]

mean value: 0.5216666666666667

key: train_jcc 
value: [0.80769231 0.91304348 0.75       0.86956522 0.83333333 0.9
 0.80952381 0.76190476 0.84       0.95454545]

mean value: 0.843960836265184

MCC on Blind test: 0.01

MCC on Training: 0.24

Running classifier: 17 
Model_name: QDA 
Model func: QuadraticDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', QuadraticDiscriminantAnalysis())])

key: fit_time 
value: [0.00907946 0.00956416 0.00959086 0.00946903 0.00860596 0.00879407
 0.00890636 0.00897217 0.00866079 0.00853682]

mean value: 0.00901796817779541

key: score_time 
value: [0.00881743 0.00922155 0.00925684 0.00932598 0.00868034 0.00835371
 0.00860643 0.00837111 0.00851178 0.00883555]

mean value: 0.00879807472229004

key: test_mcc 
value: [-0.16666667 -0.61237244  0.61237244 -0.40824829 -0.16666667 -0.66666667
  0.57735027  0.         -1.          0.        ]

mean value: -0.1830898021274237

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.33333333 0.66666667 0.57142857 0.4        0.33333333
 0.8        0.66666667 0.         0.5       ]

mean value: 0.4671428571428572

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
[0.33333333 0.25       1.         0.5        0.5        0.33333333
 0.66666667 0.5        0.         0.5       ]

mean value: 0.4583333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.33333333 0.33333333
 1.         1.         0.         0.5       ]

mean value: 0.5333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.2  0.8  0.4  0.4  0.2  0.75 0.5  0.   0.5 ]

mean value: 0.41500000000000004

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.25       0.75       0.33333333 0.41666667 0.16666667
 0.75       0.5        0.         0.5       ]

mean value: 0.4083333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.2        0.5        0.4        0.25       0.2
 0.66666667 0.5        0.         0.33333333]

mean value: 0.33

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.39

MCC on Training: -0.18

Running classifier: 18 
Model_name: Random Forest 
Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(n_estimators=1000, n_jobs=12,
                                        random_state=42))])

key: fit_time 
value: [0.6046989  0.5468781  0.5721488  0.52542901 0.56998491 0.55421329
 0.53914094 0.54499841 0.56526446 0.5692544 ]

mean value: 0.5592011213302612

key: score_time 
value: [0.11945629 0.18391323 0.1769104  0.14987516 0.16597605 0.19020319
 0.15206194 0.1613009  0.19221568 0.15517879]

mean value: 0.16470916271209718

key: test_mcc 
value: [0.16666667 0.         1.         0.16666667 0.61237244 0.40824829
 0.         0.         1.         0.57735027]

mean value: 0.39313043286826166

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         1.         0.66666667 0.85714286 0.5
 0.66666667 0.66666667 1.         0.66666667]

mean value: 0.6523809523809525

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         1.         0.66666667 0.75       1.
 0.5        0.5        1.         1.        ]

mean value: 0.6916666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.7

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.6  1.   0.6  0.8  0.6  0.5  0.5  1.   0.75]

mean value: 0.695

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.5        1.         0.58333333 0.75       0.66666667
 0.5        0.5        1.         0.75      ]

mean value: 0.6833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         1.         0.5        0.75       0.33333333
 0.5        0.5        1.         0.5       ]

mean value: 0.5416666666666666

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.3

MCC on Training: 0.39

Running classifier: 19 
Model_name: Random Forest2 
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...age_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                                        n_estimators=1000, n_jobs=12,
                                        oob_score=True, random_state=42))])

key: fit_time 
value: [0.83173156 0.87211442 0.86492038 0.81923556 0.89267898 0.93334937
 0.88286328 0.85631347 0.8562398  0.84969449]

mean value: 0.8659141302108765

key: score_time 
value: [0.19917798 0.18953228 0.17793465 0.16570473 0.18589878 0.21631145
 0.18925476 0.2202661  0.18394113 0.17896175]

mean value: 0.19069836139678956

key: test_mcc 
value: [0.16666667 0.61237244 0.66666667 0.16666667 1.         0.40824829
 0.         0.         0.57735027 0.57735027]

mean value: 0.41753212645389093

key: train_mcc 
value: [0.8047619  0.90692382 0.8047619  0.90238095 0.8047619  0.8047619
 0.80952381 0.85811633 0.80952381 0.80952381]

mean value: 0.8315040154305618

key: test_fscore 
value: [0.5        0.66666667 0.8        0.66666667 1.         0.5
 0.66666667 0.66666667 0.8        0.66666667]

mean value: 0.6933333333333334

key: train_fscore 
value: [0.9047619  0.95       0.9047619  0.95       0.9        0.9
 0.9047619  0.92682927 0.9047619  0.9047619 ]

mean value: 0.9150638792102207

key: test_precision 
value: [0.5        1.         0.66666667 0.66666667 1.         1.
 0.5        0.5        0.66666667 1.        ]

mean value: 0.75

key: train_precision 
value: [0.9047619 1.        0.9047619 0.95      0.9       0.9       0.9047619
 0.95      0.9047619 0.9047619]

mean value: 0.9223809523809525

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.75

key: train_recall 
value: [0.9047619 0.9047619 0.9047619 0.95      0.9       0.9       0.9047619
 0.9047619 0.9047619 0.9047619]

mean value: 0.9083333333333334

key: test_accuracy 
value: [0.6  0.8  0.8  0.6  1.   0.6  0.5  0.5  0.75 0.75]

mean value: 0.6900000000000001

key: train_accuracy 
value: [0.90243902 0.95121951 0.90243902 0.95121951 0.90243902 0.90243902
 0.9047619  0.92857143 0.9047619  0.9047619 ]

mean value: 0.9155052264808363

key: test_roc_auc 
value: [0.58333333 0.75       0.83333333 0.58333333 1.         0.66666667
 0.5        0.5        0.75       0.75      ]

mean value: 0.6916666666666667

key: train_roc_auc 
value: [0.90238095 0.95238095 0.90238095 0.95119048 0.90238095 0.90238095
 0.9047619  0.92857143 0.9047619  0.9047619 ]

mean value: 0.915595238095238

key: test_jcc 
value: [0.33333333 0.5        0.66666667 0.5        1.         0.33333333
 0.5        0.5        0.66666667 0.5       ]
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

mean value: 0.55

key: train_jcc 
value: [0.82608696 0.9047619  0.82608696 0.9047619  0.81818182 0.81818182
 0.82608696 0.86363636 0.82608696 0.82608696]

mean value: 0.8439958592132506

MCC on Blind test: -0.05

MCC on Training: 0.42

Running classifier: 20 
Model_name: Ridge Classifier 
Model func: RidgeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifier(random_state=42))])

key: fit_time 
value: [0.00993299 0.01000404 0.00990272 0.00939608 0.00977564 0.00943708
 0.00933385 0.00914884 0.00901127 0.00894332]

mean value: 0.009488582611083984

key: score_time 
value: [0.00900126 0.00870061 0.00940204 0.00922155 0.00895357 0.00903201
 0.00875378 0.00931644 0.00902128 0.00850487]

mean value: 0.008990740776062012

key: test_mcc 
value: [-0.40824829 -0.16666667  0.16666667 -0.16666667  0.66666667  0.40824829
  1.          0.          1.          0.        ]

mean value: 0.25

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.         0.4        0.5        0.4        0.8        0.5
 1.         0.66666667 1.         0.66666667]

mean value: 0.5933333333333334

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.         0.33333333 0.5        0.5        1.         1.
 1.         0.5        1.         0.5       ]

mean value: 0.6333333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.         0.5        0.5        0.33333333 0.66666667 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4 0.4 0.6 0.4 0.8 0.6 1.  0.5 1.  0.5]

mean value: 0.62

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.33333333 0.41666667 0.58333333 0.41666667 0.83333333 0.66666667
 1.         0.5        1.         0.5       ]

mean value: 0.625

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.         0.25       0.33333333 0.25       0.66666667 0.33333333
 1.         0.5        1.         0.5       ]

mean value: 0.4833333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.31

MCC on Training: 0.25

Running classifier: 21 
Model_name: Ridge ClassifierCV 
Model func: RidgeClassifierCV(cv=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifierCV(cv=3))])

key: fit_time 
value: [0.02450752 0.0244503  0.02426744 0.02479362 0.02454066 0.02436495
 0.02435231 0.02432179 0.02435064 0.02467346]

mean value: 0.024462270736694335

key: score_time 
value: [0.00837088 0.00856066 0.00850224 0.00854135 0.00844646 0.00846434
 0.00847292 0.00845075 0.0084188  0.00848889]

mean value: 0.00847172737121582

key: test_mcc 
value: [-0.66666667 -0.16666667  0.16666667 -0.16666667  0.61237244  0.40824829
  1.          0.          1.          0.        ]

mean value: 0.21872873928263248

key: train_mcc 
value: [0.85441771 1.         1.         1.         0.85441771 1.
 1.         1.         1.         1.        ]

mean value: 0.9708835415718322

key: test_fscore 
value: [0.         0.4        0.5        0.4        0.85714286 0.5
 1.         0.66666667 1.         0.66666667]

mean value: 0.599047619047619

key: train_fscore 
value: [0.93023256 1.         1.         1.         0.92307692 1.
 1.         1.         1.         1.        ]

mean value: 0.9853309481216458

key: test_precision 
value: [0.         0.33333333 0.5        0.5        0.75       1.
 1.         0.5        1.         0.5       ]

mean value: 0.6083333333333333

key: train_precision 
value: [0.90909091 1.         1.         1.         0.94736842 1.
 1.         1.         1.         1.        ]

mean value: 0.985645933014354

key: test_recall 
value: [0.         0.5        0.5        0.33333333 1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6666666666666666

key: train_recall 
value: [0.95238095 1.         1.         1.         0.9        1.
 1.         1.         1.         1.        ]

mean value: 0.9852380952380952

key: test_accuracy 
value: [0.2 0.4 0.6 0.4 0.8 0.6 1.  0.5 1.  0.5]

mean value: 0.6

key: train_accuracy 
value: [0.92682927 1.         1.         1.         0.92682927 1.
 1.         1.         1.         1.        ]

mean value: 0.9853658536585366

key: test_roc_auc 
value: [0.16666667 0.41666667 0.58333333 0.41666667 0.75       0.66666667
 1.         0.5        1.         0.5       ]

mean value: 0.6

key: train_roc_auc 
value: [0.92619048 1.         1.         1.         0.92619048 1.
 1.         1.         1.         1.        ]

mean value: 0.9852380952380952

key: test_jcc 
value: [0.         0.25       0.33333333 0.25       0.75       0.33333333
 1.         0.5        1.         0.5       ]

mean value: 0.4916666666666666

key: train_jcc 
value: [0.86956522 1.         1.         1.         0.85714286 1.
 1.         1.         1.         1.        ]

mean value: 0.9726708074534163

MCC on Blind test: -0.31

MCC on Training: 0.22

Running classifier: 22 
Model_name: SVC 
Model func: SVC(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SVC(random_state=42))])

key: fit_time 
value: [0.00820303 0.00800443 0.00808978 0.00802493 0.00798249 0.00805926
 0.00806236 0.00807571 0.00816321 0.00828719]

mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
0.008095240592956543

key: score_time 
value: [0.00824666 0.00841951 0.00829244 0.0082736  0.00825191 0.00831676
 0.00824118 0.008219   0.00826025 0.00825834]

mean value: 0.008277964591979981

key: test_mcc 
value: [-0.40824829 -0.61237244  0.16666667 -0.16666667  0.66666667  0.
  0.         -0.57735027  0.          0.57735027]

mean value: -0.035395405949299096

key: train_mcc 
value: [0.7565654  0.8047619  0.65952381 0.7633652  0.7565654  0.7565654
 0.71428571 0.67357531 0.66742381 0.76277007]

mean value: 0.73154020250403

key: test_fscore 
value: [0.         0.33333333 0.5        0.4        0.8        0.
 0.66666667 0.4        0.5        0.66666667]

mean value: 0.42666666666666664

key: train_fscore 
value: [0.88372093 0.9047619  0.82926829 0.86486486 0.87179487 0.87179487
 0.85714286 0.82051282 0.8372093  0.88372093]

mean value: 0.8624791646345814

key: test_precision 
value: [0.         0.25       0.5        0.5        1.         0.
 0.5        0.33333333 0.5        1.        ]

mean value: 0.4583333333333333

key: train_precision 
value: [0.86363636 0.9047619  0.85       0.94117647 0.89473684 0.89473684
 0.85714286 0.88888889 0.81818182 0.86363636]

mean value: 0.8776898351046958

key: test_recall 
value: [0.         0.5        0.5        0.33333333 0.66666667 0.
 1.         0.5        0.5        0.5       ]

mean value: 0.45

key: train_recall 
value: [0.9047619  0.9047619  0.80952381 0.8        0.85       0.85
 0.85714286 0.76190476 0.85714286 0.9047619 ]

mean value: 0.85

key: test_accuracy 
value: [0.4  0.2  0.6  0.4  0.8  0.4  0.5  0.25 0.5  0.75]

mean value: 0.4800000000000001

key: train_accuracy 
value: [0.87804878 0.90243902 0.82926829 0.87804878 0.87804878 0.87804878
 0.85714286 0.83333333 0.83333333 0.88095238]

mean value: 0.8648664343786294

key: test_roc_auc 
value: [0.33333333 0.25       0.58333333 0.41666667 0.83333333 0.5
 0.5        0.25       0.5        0.75      ]

mean value: 0.4916666666666666

key: train_roc_auc 
value: [0.87738095 0.90238095 0.8297619  0.87619048 0.87738095 0.87738095
 0.85714286 0.83333333 0.83333333 0.88095238]

mean value: 0.8645238095238096

key: test_jcc 
value: [0.         0.2        0.33333333 0.25       0.66666667 0.
 0.5        0.25       0.33333333 0.5       ]

mean value: 0.30333333333333334

key: train_jcc 
value: [0.79166667 0.82608696 0.70833333 0.76190476 0.77272727 0.77272727
 0.75       0.69565217 0.72       0.79166667]

mean value: 0.7590765104460757

MCC on Blind test: 0.12

MCC on Training: -0.04

Running classifier: 23 
Model_name: Stochastic GDescent 
Model func: SGDClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SGDClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00881124 0.00913858 0.00944018 0.0088892  0.00848913 0.00884056
 0.00910306 0.00832677 0.00881267 0.00922275]

mean value: 0.008907413482666016

key: score_time 
value: [0.00928545 0.00922966 0.00934339 0.0086813  0.00818038 0.00930667
 0.00825071 0.00884247 0.00897217 0.00909185]

mean value: 0.008918404579162598

key: test_mcc 
value: [-0.66666667  0.16666667  0.          0.16666667 -0.16666667  0.
  1.          0.          0.57735027  0.        ]

mean value: 0.10773502691896257

key: train_mcc 
value: [1.         0.95238095 0.59093684 0.90649828 0.90238095 0.73786479
 0.81322028 1.         0.90889326 0.90889326]

mean value: 0.8721068614545577

key: test_fscore 
value: [0.         0.5        0.         0.66666667 0.4        0.
 1.         0.66666667 0.66666667 0.66666667]

mean value: 0.45666666666666667

key: train_fscore 
value: [1.         0.97560976 0.6875     0.94736842 0.95       0.82352941
 0.9        1.         0.95       0.95      ]

mean value: 0.9184007588914896

key: test_precision 
value: [0.         0.5        0.         0.66666667 0.5        0.
 1.         0.5        1.         0.5       ]

mean value: 0.4666666666666666

key: train_precision 
value: [1.         1.         1.         1.         0.95       1.
 0.94736842 1.         1.         1.        ]

mean value: 0.9897368421052631

key: test_recall 
value: [0.         0.5        0.         0.66666667 0.33333333 0.
 1.         1.         0.5        1.        ]

mean value: 0.5

key: train_recall 
value: [1.         0.95238095 0.52380952 0.9        0.95       0.7
 0.85714286 1.         0.9047619  0.9047619 ]

mean value: 0.8692857142857143

key: test_accuracy 
value: [0.2  0.6  0.6  0.6  0.4  0.4  1.   0.5  0.75 0.5 ]

mean value: 0.5549999999999999

key: train_accuracy 
value: [1.         0.97560976 0.75609756 0.95121951 0.95121951 0.85365854
 0.9047619  1.         0.95238095 0.95238095]

mean value: 0.9297328687572589

key: test_roc_auc 
value: [0.16666667 0.58333333 0.5        0.58333333 0.41666667 0.5
 1.         0.5        0.75       0.5       ]

mean value: 0.55

key: train_roc_auc 
value: [1.         0.97619048 0.76190476 0.95       0.95119048 0.85
 0.9047619  1.         0.95238095 0.95238095]

mean value: 0.9298809523809524

key: test_jcc 
value: [0.         0.33333333 0.         0.5        0.25       0.
 1.         0.5        0.5        0.5       ]

mean value: 0.3583333333333333

key: train_jcc 
value: [1.         0.95238095 0.52380952 0.9        0.9047619  0.7
 0.81818182 1.         0.9047619  0.9047619 ]

mean value: 0.860865800865801

MCC on Blind test: 0.15

MCC on Training: 0.11

Running classifier: 24 
Model_name: XGBoost 
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0)

Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:427: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_CV['source_data'] = 'CV'
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:454: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_BT['source_data'] = 'BT'
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...
                               interaction_constraints=None, learning_rate=None,
                               max_delta_step=None, max_depth=None,
                               min_child_weight=None, missing=nan,
                               monotone_constraints=None, n_estimators=100,
                               n_jobs=12, num_parallel_tree=None,
                               predictor=None, random_state=42, reg_alpha=None,
                               reg_lambda=None, scale_pos_weight=None,
                               subsample=None, tree_method=None,
                               use_label_encoder=False,
                               validate_parameters=None, verbosity=0))])

key: fit_time 
value: [0.03750682 0.03452182 0.03570294 0.03578782 0.03548884 0.03390956
 0.03578544 0.03628993 0.03644991 0.0362761 ]

mean value: 0.03577191829681396

key: score_time 
value: [0.01046443 0.01038051 0.01065159 0.01118374 0.01060486 0.01112819
 0.01139307 0.01042986 0.01071906 0.01041365]

mean value: 0.010736894607543946

key: test_mcc 
value: [ 0.61237244  0.16666667  1.         -0.16666667  0.16666667  0.40824829
  0.57735027 -0.57735027  1.          1.        ]

mean value: 0.4187287392826324

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.66666667 0.5        1.         0.4        0.66666667 0.5
 0.8        0.4        1.         1.        ]

mean value: 0.6933333333333334

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [1.         0.5        1.         0.5        0.66666667 1.
 0.66666667 0.33333333 1.         1.        ]

mean value: 0.7666666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 0.66666667 0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.8  0.6  1.   0.4  0.6  0.6  0.75 0.25 1.   1.  ]

mean value: 0.7

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.75       0.58333333 1.         0.41666667 0.58333333 0.66666667
 0.75       0.25       1.         1.        ]

mean value: 0.7

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.5        0.33333333 1.         0.25       0.5        0.33333333
 0.66666667 0.25       1.         1.        ]

mean value: 0.5833333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.22

MCC on Training: 0.42

Extracting tts_split_name: 70_30

Total cols in each df: 
CV df: 8 
metaDF: 15

Adding column: Model_name

Total cols in bts df: 
BT_df: 8

First proceeding to rowbind CV and BT dfs:

Final output should have: 23 columns

Combinig 2 using pd.concat by row ~ rowbind 
Checking Dims of df to combine: 
Dim of CV: (24, 8) 
Dim of BT: (24, 8)
8

Number of Common columns: 8 
These are: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall']

Concatenating dfs with different resampling methods [WF]: 
Split type: 70_30 
No. of dfs combining: 2

PASS: 2 dfs successfully combined 
nrows in combined_df_wf: 48 
ncols in combined_df_wf: 8

PASS: proceeding to merge metadata with CV and BT dfs

Adding column: Model_name

========================================================= 
SUCCESS: Ran multiple classifiers 
=======================================================

==============================================================
 
Running several classification models (n): 24 
List of models:
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3))
('Decision Tree', DecisionTreeClassifier(random_state=42))
('Extra Tree', ExtraTreeClassifier(random_state=42))
('Extra Trees', ExtraTreesClassifier(random_state=42))
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
('Gaussian NB', GaussianNB())
('Gaussian Process', GaussianProcessClassifier(random_state=42))
('K-Nearest Neighbors', KNeighborsClassifier())
('LDA', LinearDiscriminantAnalysis())
('Logistic Regression', LogisticRegression(random_state=42))
('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42))
('MLP', MLPClassifier(max_iter=500, random_state=42))
('Multinomial', MultinomialNB())
('Naive Bayes', BernoulliNB())
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42))
('QDA', QuadraticDiscriminantAnalysis())
('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42))
('Ridge Classifier', RidgeClassifier(random_state=42))
('Ridge ClassifierCV', RidgeClassifierCV(cv=3))
('SVC', SVC(random_state=42))
('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42))
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0))

================================================================


Running classifier: 1 
Model_name: AdaBoost Classifier 
Model func: AdaBoostClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', AdaBoostClassifier(random_state=42))])

key: fit_time 
value: [0.07621503 0.07360363 0.07417893 0.07430983 0.07171845 0.07407093
 0.07249784 0.06705332 0.068156   0.0687573 ]

mean value: 0.07205612659454345

key: score_time 
value: [0.01585674 0.0160594  0.01581192 0.0159893  0.01554942 0.01597142
 0.01444983 0.01414967 0.01507425 0.01533413]

mean value: 0.015424609184265137

key: test_mcc 
value: [-0.61237244  0.16666667  0.66666667  0.40824829  1.         -0.16666667
  0.57735027  0.57735027  0.57735027  0.57735027]

mean value: 0.3771943598193238

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.1s remaining:    0.2s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.1s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.4s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.2s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.3s remaining:    0.5s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.3s remaining:    0.1s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.3s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.1s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=12)]: Done   4 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done   9 out of  12 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=12)]: Done  12 out of  12 | elapsed:    0.0s finished
test_fscore 
value: [0.33333333 0.5        0.8        0.5        1.         0.4
 0.8        0.8        0.8        0.8       ]

mean value: 0.6733333333333332

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.25       0.5        0.66666667 1.         1.         0.5
 0.66666667 0.66666667 0.66666667 0.66666667]

mean value: 0.6583333333333334

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.33333333 1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.7666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.2  0.6  0.8  0.6  1.   0.4  0.75 0.75 0.75 0.75]

mean value: 0.6599999999999999

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.25       0.58333333 0.83333333 0.66666667 1.         0.41666667
 0.75       0.75       0.75       0.75      ]

mean value: 0.675

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.2        0.33333333 0.66666667 0.33333333 1.         0.25
 0.66666667 0.66666667 0.66666667 0.66666667]

mean value: 0.545

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.03

MCC on Training: 0.38

Running classifier: 2 
Model_name: Bagging Classifier 
Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42,
                  verbose=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True,
                                   random_state=42, verbose=3))])

key: fit_time 
value: [0.10585761 0.10880518 0.11610079 0.11265016 0.11422777 0.109828
 0.12987709 0.12740016 0.10178447 0.17598081]

mean value: 0.12025120258331298

key: score_time 
value: [0.04848433 0.05669498 0.06805182 0.0736239  0.04690218 0.07367539
 0.07672715 0.03672194 0.07234097 0.07809186]

mean value: 0.06313145160675049

key: test_mcc 
value: [-0.61237244  0.16666667  0.66666667  0.16666667  0.16666667 -0.16666667
  0.57735027  0.57735027  0.57735027  1.        ]

mean value: 0.3119678371873083

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.33333333 0.5        0.8        0.66666667 0.66666667 0.4
 0.8        0.66666667 0.8        1.        ]

mean value: 0.6633333333333333

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.25       0.5        0.66666667 0.66666667 0.66666667 0.5
 0.66666667 1.         0.66666667 1.        ]

mean value: 0.6583333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.2  0.6  0.8  0.6  0.6  0.4  0.75 0.75 0.75 1.  ]

mean value: 0.645

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.25       0.58333333 0.83333333 0.58333333 0.58333333 0.41666667
 0.75       0.75       0.75       1.        ]

mean value: 0.65

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.2        0.33333333 0.66666667 0.5        0.5        0.25
 0.66666667 0.5        0.66666667 1.        ]

mean value: 0.5283333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.23

MCC on Training: 0.31

Running classifier: 3 
Model_name: Decision Tree 
Model func: DecisionTreeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', DecisionTreeClassifier(random_state=42))])

key: fit_time 
value: [0.02258229 0.00880694 0.00900722 0.00884438 0.00875759 0.00896645
 0.00875711 0.00936079 0.0086937  0.00957966]

mean value: 0.010335612297058105

key: score_time 
value: [0.00884223 0.00870609 0.00825596 0.00897503 0.00818658 0.00845528
 0.00824118 0.00823307 0.00876999 0.00825143]

mean value: 0.00849168300628662

key: test_mcc 
value: [-0.61237244  0.61237244  0.66666667  0.66666667  0.66666667 -0.16666667
  0.57735027  0.57735027  0.          0.57735027]

mean value: 0.3565384140902211

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.33333333 0.66666667 0.8        0.8        0.8        0.4
 0.8        0.66666667 0.5        0.8       ]

mean value: 0.6566666666666667

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.25       1.         0.66666667 1.         1.         0.5
 0.66666667 1.         0.5        0.66666667]

mean value: 0.725

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 1.         0.5        0.5        1.        ]

mean value: 0.6666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.2  0.8  0.8  0.8  0.8  0.4  0.75 0.75 0.5  0.75]

mean value: 0.655

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.25       0.75       0.83333333 0.83333333 0.83333333 0.41666667
 0.75       0.75       0.5        0.75      ]

mean value: 0.6666666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.2        0.5        0.66666667 0.66666667 0.66666667 0.25
 0.66666667 0.5        0.33333333 0.66666667]

mean value: 0.5116666666666666

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.21

MCC on Training: 0.36

Running classifier: 4 
Model_name: Extra Tree 
Model func: ExtraTreeClassifier(random_state=42)

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreeClassifier(random_state=42))])

key: fit_time 
value: [0.010041   0.00920272 0.00931287 0.00932002 0.00854254 0.00927401
 0.00925541 0.0091722  0.00911927 0.0091598 ]

mean value: 0.009239983558654786

key: score_time 
value: [0.00951338 0.00924969 0.0091536  0.00929809 0.00920486 0.00916767
 0.00916123 0.00914097 0.00921082 0.00898218]

mean value: 0.009208250045776366

key: test_mcc 
value: [-0.16666667 -0.40824829  1.         -0.40824829  0.16666667 -0.40824829
  1.          0.         -0.57735027  0.        ]

mean value: 0.01979048594187849

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.         1.         0.57142857 0.66666667 0.57142857
 1.         0.         0.4        0.5       ]

mean value: 0.510952380952381

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.         1.         0.5        0.66666667 0.5
 1.         0.         0.33333333 0.5       ]

mean value: 0.4833333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 0.66666667 0.66666667
 1.         0.         0.5        0.5       ]

mean value: 0.55

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  1.   0.4  0.6  0.4  1.   0.5  0.25 0.5 ]

mean value: 0.545

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.33333333 1.         0.33333333 0.58333333 0.33333333
 1.         0.5        0.25       0.5       ]

mean value: 0.525

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.         1.         0.4        0.5        0.4
 1.         0.         0.25       0.33333333]

mean value: 0.4133333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.14

MCC on Training: 0.02

Running classifier: 5 
Model_name: Extra Trees 
Model func: ExtraTreesClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', ExtraTreesClassifier(random_state=42))])

key: fit_time 
value: [0.0820632  0.07688308 0.07963014 0.07842422 0.07723594 0.07713103
 0.08013487 0.07813239 0.07798409 0.0784924 ]

mean value: 0.07861113548278809

key: score_time 
value: [0.01680231 0.01802921 0.01681995 0.01691961 0.01716518 0.01819587
 0.01705599 0.01786733 0.0177505  0.0167017 ]

mean value: 0.01733076572418213

key: test_mcc 
value: [ 0.16666667  0.          0.66666667  0.16666667  1.         -0.66666667
  0.          0.57735027  0.57735027  0.        ]

mean value: 0.2488033871712585

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         0.8        0.66666667 1.         0.33333333
 0.5        0.66666667 0.8        0.5       ]

mean value: 0.5766666666666667

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         0.66666667 0.66666667 1.         0.33333333
 0.5        1.         0.66666667 0.5       ]

mean value: 0.5833333333333333

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        0.5        1.         0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.6  0.8  0.6  1.   0.2  0.5  0.75 0.75 0.5 ]

mean value: 0.63

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.5        0.83333333 0.58333333 1.         0.16666667
 0.5        0.75       0.75       0.5       ]

mean value: 0.6166666666666667

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         0.66666667 0.5        1.         0.2
 0.33333333 0.5        0.66666667 0.33333333]

mean value: 0.4533333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.15

MCC on Training: 0.25

Running classifier: 6 
Model_name: Gradient Boosting 
Model func: GradientBoostingClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GradientBoostingClassifier(random_state=42))])

key: fit_time 
value: [0.12404656 0.11600733 0.11816883 0.12366486 0.11906648 0.10522175
 0.10942721 0.12072515 0.11076951 0.12015915]

mean value: 0.11672568321228027

key: score_time 
value: [0.00864172 0.00862288 0.0088222  0.00874257 0.00877643 0.00863934
 0.0090692  0.00877285 0.00887442 0.008744  ]

mean value: 0.008770561218261719

key: test_mcc 
value: [-0.16666667  0.61237244  1.          0.66666667  0.66666667  0.40824829
  0.57735027  0.57735027  0.57735027  0.        ]

mean value: 0.49193382003952024

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.66666667 1.         0.8        0.8        0.5
 0.8        0.66666667 0.8        0.5       ]

mean value: 0.6933333333333332

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 1.         1.         1.         1.         1.
 0.66666667 1.         0.66666667 0.5       ]

mean value: 0.8166666666666668

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 1.         0.5        1.         0.5       ]

mean value: 0.6666666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.8  1.   0.8  0.8  0.6  0.75 0.75 0.75 0.5 ]

mean value: 0.7150000000000001

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.75       1.         0.83333333 0.83333333 0.66666667
 0.75       0.75       0.75       0.5       ]

mean value: 0.725

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.5        1.         0.66666667 0.66666667 0.33333333
 0.66666667 0.5        0.66666667 0.33333333]

mean value: 0.5583333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.48

MCC on Training: 0.49

Running classifier: 7 
Model_name: Gaussian NB 
Model func: GaussianNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianNB())])

key: fit_time 
value: [0.00915074 0.00822377 0.00828218 0.0084331  0.0093925  0.0093267
 0.00845575 0.00869799 0.00905776 0.00892186]

mean value: 0.008794236183166503

key: score_time 
value: [0.00872087 0.00841856 0.00854039 0.00893021 0.0093658  0.00918722
 0.00909758 0.00853658 0.00864816 0.00879574]

mean value: 0.00882411003112793

key: test_mcc 
value: [-0.61237244  0.66666667  0.66666667  0.61237244  0.         -0.16666667
  0.57735027  0.57735027  0.          0.57735027]

mean value: 0.2898717474235544

key: train_mcc 
value: [0.61969655 0.5519099  0.48849265 0.59093684 0.58066054 0.63994524
 0.60609153 0.52704628 0.58834841 0.52704628]

mean value: 0.5720174209906098

key: test_fscore 
value: [0.33333333 0.8        0.8        0.85714286 0.75       0.4
 0.8        0.8        0.66666667 0.8       ]

mean value: 0.7007142857142857

key: train_fscore 
value: [0.82608696 0.8        0.7755102  0.8        0.8        0.82608696
 0.81632653 0.78431373 0.80851064 0.78431373]

mean value: 0.802114873701562

key: test_precision 
value: [0.25       0.66666667 0.66666667 0.75       0.6        0.5
 0.66666667 0.66666667 0.5        0.66666667]

mean value: 0.5933333333333334

key: train_precision 
value: [0.76       0.68965517 0.67857143 0.66666667 0.72       0.73076923
 0.71428571 0.66666667 0.73076923 0.66666667]

mean value: 0.7024050776809398

key: test_recall 
value: [0.5        1.         1.         1.         1.         0.33333333
 1.         1.         1.         1.        ]

mean value: 0.8833333333333332

key: train_recall 
value: [0.9047619  0.95238095 0.9047619  1.         0.9        0.95
 0.95238095 0.95238095 0.9047619  0.95238095]

mean value: 0.9373809523809523

key: test_accuracy 
value: [0.2  0.8  0.8  0.8  0.6  0.4  0.75 0.75 0.5  0.75]

mean value: 0.635

key: train_accuracy 
value: [0.80487805 0.75609756 0.73170732 0.75609756 0.7804878  0.80487805
 0.78571429 0.73809524 0.78571429 0.73809524]

mean value: 0.7681765389082462

key: test_roc_auc 
value: [0.25       0.83333333 0.83333333 0.75       0.5        0.41666667
 0.75       0.75       0.5        0.75      ]

mean value: 0.6333333333333334

key: train_roc_auc 
value: [0.80238095 0.75119048 0.72738095 0.76190476 0.78333333 0.80833333
 0.78571429 0.73809524 0.78571429 0.73809524]

mean value: 0.7682142857142857

key: test_jcc 
value: [0.2        0.66666667 0.66666667 0.75       0.6        0.25
 0.66666667 0.66666667 0.5        0.66666667]

mean value: 0.5633333333333334

key: train_jcc 
value: [0.7037037  0.66666667 0.63333333 0.66666667 0.66666667 0.7037037
 0.68965517 0.64516129 0.67857143 0.64516129]

mean value: 0.6699289922371123

MCC on Blind test: -0.03

MCC on Training: 0.29

Running classifier: 8 
Model_name: Gaussian Process 
Model func: GaussianProcessClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', GaussianProcessClassifier(random_state=42))])

key: fit_time 
value: [0.0100069  0.00988388 0.00976467 0.01003194 0.0100348  0.01075625
 0.01098824 0.01043177 0.0102098  0.00995898]

mean value: 0.0102067232131958

key: score_time 
value: [0.00851536 0.00850487 0.00874519 0.00857115 0.00878572 0.00922728
 0.00910878 0.00863576 0.00853658 0.00946045]

mean value: 0.008809113502502441

key: test_mcc 
value: [ 0.16666667 -0.40824829  1.         -0.40824829  0.         -0.66666667
  0.          1.          0.          0.57735027]

mean value: 0.12608536882618998

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         1.         0.57142857 0.75       0.33333333
 0.5        1.         0.5        0.66666667]

mean value: 0.5821428571428572

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         1.         0.5        0.6        0.33333333
 0.5        1.         0.5        1.        ]

mean value: 0.5933333333333334

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 0.5        1.         0.5        0.5       ]

mean value: 0.6

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.4  1.   0.4  0.6  0.2  0.5  1.   0.5  0.75]

mean value: 0.595

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.33333333 1.         0.33333333 0.5        0.16666667
 0.5        1.         0.5        0.75      ]

mean value: 0.5666666666666667

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         1.         0.4        0.6        0.2
 0.33333333 1.         0.33333333 0.5       ]

mean value: 0.4699999999999999

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.04

MCC on Training: 0.13

Running classifier: 9 
Model_name: K-Nearest Neighbors 
Model func: KNeighborsClassifier()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', KNeighborsClassifier())])

key: fit_time 
value: [0.00818205 0.00851893 0.00917649 0.00848556 0.00831485 0.00897002
 0.00830007 0.00902057 0.00891423 0.00867987]

mean value: 0.00865626335144043

key: score_time 
value: [0.01083255 0.00954747 0.01015925 0.00933123 0.00915051 0.01006079
 0.0101018  0.00983715 0.00978494 0.00884175]

mean value: 0.009764742851257325

key: test_mcc 
value: [-0.16666667  0.16666667  0.66666667 -0.40824829 -0.40824829 -1.
  0.          1.          0.57735027 -0.57735027]

mean value: -0.014982991426105957

key: train_mcc 
value: [0.37309549 0.46623254 0.46623254 0.56190476 0.56836003 0.6133669
 0.53357838 0.43656413 0.52620136 0.43052839]

mean value: 0.4976064514538302

key: test_fscore 
value: [0.4        0.5        0.8        0.57142857 0.57142857 0.
 0.5        1.         0.8        0.4       ]

mean value: 0.5542857142857144

key: train_fscore 
value: [0.72340426 0.75555556 0.75555556 0.7804878  0.79069767 0.80952381
 0.7826087  0.73913043 0.77272727 0.72727273]

mean value: 0.7636963785685505

key: test_precision 
value: [0.33333333 0.5        0.66666667 0.5        0.5        0.
 0.5        1.         0.66666667 0.33333333]

mean value: 0.5

key: train_precision 
value: [0.65384615 0.70833333 0.70833333 0.76190476 0.73913043 0.77272727
 0.72       0.68       0.73913043 0.69565217]

mean value: 0.7179057898623116

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 0.5        1.         1.         0.5       ]

mean value: 0.6333333333333333

key: train_recall 
value: [0.80952381 0.80952381 0.80952381 0.8        0.85       0.85
 0.85714286 0.80952381 0.80952381 0.76190476]

mean value: 0.8166666666666667

key: test_accuracy 
value: [0.4  0.6  0.8  0.4  0.4  0.   0.5  1.   0.75 0.25]

mean value: 0.51

key: train_accuracy 
value: [0.68292683 0.73170732 0.73170732 0.7804878  0.7804878  0.80487805
 0.76190476 0.71428571 0.76190476 0.71428571]

mean value: 0.7464576074332172

key: test_roc_auc 
value: [0.41666667 0.58333333 0.83333333 0.33333333 0.33333333 0.
 0.5        1.         0.75       0.25      ]

mean value: 0.5

key: train_roc_auc 
value: [0.6797619  0.7297619  0.7297619  0.78095238 0.78214286 0.80595238
 0.76190476 0.71428571 0.76190476 0.71428571]

mean value: 0.7460714285714285

key: test_jcc 
value: [0.25       0.33333333 0.66666667 0.4        0.4        0.
 0.33333333 1.         0.66666667 0.25      ]

mean value: 0.43

key: train_jcc 
value: [0.56666667 0.60714286 0.60714286 0.64       0.65384615 0.68
 0.64285714 0.5862069  0.62962963 0.57142857]

mean value: 0.6184920775265603

MCC on Blind test: 0.21

MCC on Training: -0.01

Running classifier: 10 
Model_name: LDA 
Model func: LinearDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LinearDiscriminantAnalysis())])

key: fit_time 
value: [0.00984979 0.01346278 0.01353693 0.01350045 0.01351118 0.01342678
 0.01358747 0.01378918 0.01363397 0.0136826 ]

mean value: 0.013198113441467286

key: score_time 
value: [0.01135111 0.01145077 0.01126647 0.0113349  0.01132107 0.01151824
 0.01132131 0.01134777 0.01139069 0.01145697]

mean value: 0.011375927925109863

key: test_mcc 
value: [-1.          0.66666667  0.66666667  0.66666667 -0.16666667 -0.40824829
  0.         -0.57735027  0.57735027 -0.57735027]

mean value: -0.015226522632015571

key: train_mcc 
value: [1.         0.71121921 0.8547619  0.90238095 0.90238095 0.85441771
 0.9047619  0.9047619  0.76277007 0.9047619 ]

mean value: 0.8702216512400438

key: test_fscore 
value: [0.         0.8        0.8        0.8        0.4        0.57142857
 0.66666667 0.         0.8        0.4       ]

mean value: 0.5238095238095238

key: train_fscore 
value: [1.         0.85       0.92682927 0.95       0.95       0.92307692
 0.95238095 0.95238095 0.88372093 0.95238095]

mean value: 0.934076997874502

key: test_precision 
value: [0.         0.66666667 0.66666667 1.         0.5        0.5
 0.5        0.         0.66666667 0.33333333]

mean value: 0.4833333333333333

key: train_precision 
value: [1.         0.89473684 0.95       0.95       0.95       0.94736842
 0.95238095 0.95238095 0.86363636 0.95238095]

mean value: 0.9412884483937116

key: test_recall 
value: [0.         1.         1.         0.66666667 0.33333333 0.66666667
 1.         0.         1.         0.5       ]

mean value: 0.6166666666666666

key: train_recall 
value: [1.         0.80952381 0.9047619  0.95       0.95       0.9
 0.95238095 0.95238095 0.9047619  0.95238095]

mean value: 0.9276190476190477

key: test_accuracy 
value: [0.   0.8  0.8  0.8  0.4  0.4  0.5  0.25 0.75 0.25]

mean value: 0.495

key: train_accuracy 
value: [1.         0.85365854 0.92682927 0.95121951 0.95121951 0.92682927
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9347851335656214

key: test_roc_auc 
value: [0.         0.83333333 0.83333333 0.83333333 0.41666667 0.33333333
 0.5        0.25       0.75       0.25      ]

mean value: 0.5

key: train_roc_auc 
value: [1.         0.8547619  0.92738095 0.95119048 0.95119048 0.92619048
 0.95238095 0.95238095 0.88095238 0.95238095]

mean value: 0.9348809523809523

key: test_jcc 
value: [0.         0.66666667 0.66666667 0.66666667 0.25       0.4
 0.5        0.         0.66666667 0.25      ]

mean value: 0.4066666666666666

key: train_jcc 
value: [1.         0.73913043 0.86363636 0.9047619  0.9047619  0.85714286
 0.90909091 0.90909091 0.79166667 0.90909091]

mean value: 0.8788372859025033

MCC on Blind test: -0.3

MCC on Training: -0.02

Running classifier: 11 
Model_name: Logistic Regression 
Model func: LogisticRegression(random_state=42)

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegression(random_state=42))])

key: fit_time 
value: [0.02167916 0.01727057 0.01534629 0.01548314 0.01699781 0.01598907
 0.01678061 0.01377487 0.01523089 0.01571107]

mean value: 0.016426348686218263

key: score_time 
value: [0.01209736 0.00889635 0.00891614 0.00930095 0.00926661 0.00888228
 0.00913954 0.00879836 0.00855303 0.00841308]

mean value: 0.009226369857788085

key: test_mcc 
value: [-0.16666667  0.16666667  0.61237244  0.66666667  1.         -0.61237244
  1.          1.          0.          0.57735027]

mean value: 0.42440169358562924

key: train_mcc 
value: [1.         0.95238095 0.8547619  0.95238095 0.90238095 0.90238095
 0.85811633 0.85811633 0.95346259 0.85811633]

mean value: 0.9092097294494407

key: test_fscore 
value: [0.4        0.5        0.66666667 0.8        1.         0.
 1.         1.         0.66666667 0.8       ]

mean value: 0.6833333333333333

key: train_fscore 
value: [1.         0.97560976 0.92682927 0.97560976 0.95       0.95
 0.92682927 0.92682927 0.97560976 0.92682927]

mean value: 0.9534146341463415

key: test_precision 
value: [0.33333333 0.5        1.         1.         1.         0.
 1.         1.         0.5        0.66666667]

mean value: 0.7

key: train_precision 
value: [1.         1.         0.95       0.95238095 0.95       0.95
 0.95       0.95       1.         0.95      ]

mean value: 0.9652380952380952

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 1.         0.
 1.         1.         1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1.         0.95238095 0.9047619  1.         0.95       0.95
 0.9047619  0.9047619  0.95238095 0.9047619 ]

mean value: 0.9423809523809524

key: test_accuracy 
value: [0.4  0.6  0.8  0.8  1.   0.2  1.   1.   0.5  0.75]

mean value: 0.7050000000000001

key: train_accuracy 
value: [1.         0.97560976 0.92682927 0.97560976 0.95121951 0.95121951
 0.92857143 0.92857143 0.97619048 0.92857143]

mean value: 0.954239256678281

key: test_roc_auc 
value: [0.41666667 0.58333333 0.75       0.83333333 1.         0.25
 1.         1.         0.5        0.75      ]

mean value: 0.7083333333333333

key: train_roc_auc 
value: [1.         0.97619048 0.92738095 0.97619048 0.95119048 0.95119048
 0.92857143 0.92857143 0.97619048 0.92857143]

mean value: 0.954404761904762

key: test_jcc 
value: [0.25       0.33333333 0.5        0.66666667 1.         0.
 1.         1.         0.5        0.66666667]

mean value: 0.5916666666666667

key: train_jcc 
value: [1.         0.95238095 0.86363636 0.95238095 0.9047619  0.9047619
 0.86363636 0.86363636 0.95238095 0.86363636]

mean value: 0.9121212121212121

MCC on Blind test: 0.03

MCC on Training: 0.42

Running classifier: 12 
Model_name: Logistic RegressionCV 
Model func: LogisticRegressionCV(cv=3, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', LogisticRegressionCV(cv=3, random_state=42))])

key: fit_time 
value: [0.15119743 0.17550659 0.1849103  0.17043042 0.17547202 0.18751788
 0.18874145 0.18453479 0.16989779 0.18126726]

mean value: 0.17694759368896484

key: score_time 
value: [0.00947404 0.00928736 0.00936866 0.00957561 0.00939107 0.0097065
 0.00903535 0.00937462 0.00960827 0.0092833 ]

mean value: 0.009410476684570313

key: test_mcc 
value: [-0.16666667 -0.16666667  0.61237244  0.16666667  0.66666667 -0.16666667
  1.          1.          0.          0.57735027]

mean value: 0.35230560382187537

key: train_mcc 
value: [1.         1.         0.75714286 1.         0.95238095 1.
 1.         1.         0.76277007 0.80952381]

mean value: 0.9281817690444093

key: test_fscore 
value: [0.4        0.4        0.66666667 0.66666667 0.8        0.4
 1.         1.         0.66666667 0.8       ]

mean value: 0.68

key: train_fscore 
value: [1.         1.         0.87804878 1.         0.97560976 1.
 1.         1.         0.87804878 0.9047619 ]

mean value: 0.9636469221835074

key: test_precision 
value: [0.33333333 0.33333333 1.         0.66666667 1.         0.5
 1.         1.         0.5        0.66666667]

mean value: 0.7

key: train_precision 
value: [1.         1.         0.9        1.         0.95238095 1.
 1.         1.         0.9        0.9047619 ]

mean value: 0.9657142857142859

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.66666667 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1.         1.         0.85714286 1.         1.         1.
 1.         1.         0.85714286 0.9047619 ]

mean value: 0.961904761904762

key: test_accuracy 
value: [0.4  0.4  0.8  0.6  0.8  0.4  1.   1.   0.5  0.75]

mean value: 0.665

key: train_accuracy 
value: [1.         1.         0.87804878 1.         0.97560976 1.
 1.         1.         0.88095238 0.9047619 ]

mean value: 0.9639372822299652

key: test_roc_auc 
value: [0.41666667 0.41666667 0.75       0.58333333 0.83333333 0.41666667
 1.         1.         0.5        0.75      ]

mean value: 0.6666666666666666

key: train_roc_auc 
value: [1.         1.         0.87857143 1.         0.97619048 1.
 1.         1.         0.88095238 0.9047619 ]

mean value: 0.964047619047619

key: test_jcc 
value: [0.25       0.25       0.5        0.5        0.66666667 0.25
 1.         1.         0.5        0.66666667]

mean value: 0.5583333333333333

key: train_jcc 
value: [1.         1.         0.7826087  1.         0.95238095 1.
 1.         1.         0.7826087  0.82608696]

mean value: 0.9343685300207039

MCC on Blind test: -0.07

MCC on Training: 0.35

Running classifier: 13 
Model_name: MLP 
Model func: MLPClassifier(max_iter=500, random_state=42)

Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MLPClassifier(max_iter=500, random_state=42))])

key: fit_time 
value: [0.21241379 0.21670055 0.24280739 0.23050094 0.24434733 0.36418724
 0.24961042 0.24872684 0.23138309 0.25354838]

mean value: 0.24942259788513182

key: score_time 
value: [0.0118506  0.01177287 0.01247406 0.0120101  0.01199889 0.01177573
 0.01179838 0.01185966 0.01180196 0.01221299]

mean value: 0.011955523490905761

key: test_mcc 
value: [-0.16666667 -0.16666667  0.          0.16666667  0.40824829 -0.16666667
  1.          1.          0.          0.57735027]

mean value: 0.26522652263201557

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.4        0.         0.66666667 0.5        0.4
 1.         1.         0.66666667 0.8       ]

mean value: 0.5833333333333334

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.33333333 0.         0.66666667 1.         0.5
 1.         1.         0.5        0.66666667]

mean value: 0.6

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        0.         0.66666667 0.33333333 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6333333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.6  0.6  0.6  0.4  1.   1.   0.5  0.75]

mean value: 0.625

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.41666667 0.5        0.58333333 0.66666667 0.41666667
 1.         1.         0.5        0.75      ]

mean value: 0.625

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.25       0.         0.5        0.33333333 0.25
 1.         1.         0.5        0.66666667]

mean value: 0.475

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.05

MCC on Training: 0.27

Running classifier: 14 
Model_name: Multinomial 
Model func: MultinomialNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', MultinomialNB())])

key: fit_time 
value: [0.01140952 0.01130176 0.00922251 0.00951958 0.00954652 0.00928497
 0.00912905 0.00929046 0.00946569 0.00906324]

mean value: 0.009723329544067382

key: score_time 
value: [0.01129246 0.01127911 0.00967002 0.00933909 0.00947571 0.0094676
 0.00899863 0.00900888 0.00985169 0.0090816 ]

mean value: 0.009746479988098144

key: test_mcc 
value: [-0.40824829  0.66666667  0.16666667  0.66666667  0.16666667 -1.
  0.57735027  0.57735027  0.          0.57735027]

mean value: 0.1990469183771681

key: train_mcc 
value: [0.41487884 0.56086079 0.41487884 0.61152662 0.36718832 0.41428571
 0.43052839 0.43052839 0.42857143 0.43052839]

mean value: 0.4503775710325074

key: test_fscore 
value: [0.         0.8        0.5        0.8        0.66666667 0.
 0.8        0.66666667 0.66666667 0.66666667]

mean value: 0.5566666666666668

key: train_fscore 
value: [0.72727273 0.79069767 0.72727273 0.78947368 0.64864865 0.7
 0.72727273 0.72727273 0.71428571 0.72727273]

mean value: 0.727946935792713

key: test_precision 
value: [0.         0.66666667 0.5        1.         0.66666667 0.
 0.66666667 1.         0.5        1.        ]

mean value: 0.6

key: train_precision 
value: [0.69565217 0.77272727 0.69565217 0.83333333 0.70588235 0.7
 0.69565217 0.69565217 0.71428571 0.69565217]

mean value: 0.7204489542852714

key: test_recall 
value: [0.         1.         0.5        0.66666667 0.66666667 0.
 1.         0.5        1.         0.5       ]

mean value: 0.5833333333333333

key: train_recall 
value: [0.76190476 0.80952381 0.76190476 0.75       0.6        0.7
 0.76190476 0.76190476 0.71428571 0.76190476]

mean value: 0.7383333333333333

key: test_accuracy 
value: [0.4  0.8  0.6  0.8  0.6  0.   0.75 0.75 0.5  0.75]

mean value: 0.595

key: train_accuracy 
value: [0.70731707 0.7804878  0.70731707 0.80487805 0.68292683 0.70731707
 0.71428571 0.71428571 0.71428571 0.71428571]

mean value: 0.7247386759581882

key: test_roc_auc 
value: [0.33333333 0.83333333 0.58333333 0.83333333 0.58333333 0.
 0.75       0.75       0.5        0.75      ]

mean value: 0.5916666666666666

key: train_roc_auc 
value: [0.70595238 0.7797619  0.70595238 0.80357143 0.68095238 0.70714286
 0.71428571 0.71428571 0.71428571 0.71428571]

mean value: 0.724047619047619

key: test_jcc 
value: [0.         0.66666667 0.33333333 0.66666667 0.5        0.
 0.66666667 0.5        0.5        0.5       ]

mean value: 0.4333333333333333

key: train_jcc 
value: [0.57142857 0.65384615 0.57142857 0.65217391 0.48       0.53846154
 0.57142857 0.57142857 0.55555556 0.57142857]

mean value: 0.5737180018049582

MCC on Blind test: 0.23

MCC on Training: 0.2

Running classifier: 15 
Model_name: Naive Bayes 
Model func: BernoulliNB()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', BernoulliNB())])

key: fit_time 
value: [0.00921226 0.00931311 0.00936246 0.00890613 0.00910234 0.0093894
 0.00952625 0.00883627 0.00905919 0.00919628]

mean value: 0.00919036865234375

key: score_time 
value: [0.00950003 0.00875306 0.0090425  0.008811   0.00926733 0.00926375
 0.00929689 0.0096004  0.00891113 0.00872374]

mean value: 0.00911698341369629

key: test_mcc 
value: [-0.61237244  0.16666667  0.61237244  0.66666667  0.         -0.16666667
  0.          0.          0.57735027  0.        ]

mean value: 0.12440169358562925

key: train_mcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")

value: [0.71121921 0.8213423  0.6133669  0.77831178 0.63496528 0.86240942
 0.62187434 0.78446454 0.78446454 0.78446454]

mean value: 0.739688284295276

key: test_fscore 
value: [0.33333333 0.5        0.66666667 0.8        0.         0.4
 0.5        0.         0.8        0.5       ]

mean value: 0.45

key: train_fscore 
value: [0.85       0.89473684 0.8        0.85714286 0.76470588 0.91891892
 0.8        0.86486486 0.86486486 0.86486486]

mean value: 0.8480099095114575

key: test_precision 
value: [0.25       0.5        1.         1.         0.         0.5
 0.5        0.         0.66666667 0.5       ]

mean value: 0.4916666666666667

key: train_precision 
value: [0.89473684 1.         0.84210526 1.         0.92857143 1.
 0.84210526 1.         1.         1.        ]

mean value: 0.9507518796992482

key: test_recall 
value: [0.5        0.5        0.5        0.66666667 0.         0.33333333
 0.5        0.         1.         0.5       ]

mean value: 0.45

key: train_recall 
value: [0.80952381 0.80952381 0.76190476 0.75       0.65       0.85
 0.76190476 0.76190476 0.76190476 0.76190476]

mean value: 0.7678571428571429

key: test_accuracy 
value: [0.2  0.6  0.8  0.8  0.4  0.4  0.5  0.5  0.75 0.5 ]

mean value: 0.545

key: train_accuracy 
value: [0.85365854 0.90243902 0.80487805 0.87804878 0.80487805 0.92682927
 0.80952381 0.88095238 0.88095238 0.88095238]

mean value: 0.8623112659698027

key: test_roc_auc 
value: [0.25       0.58333333 0.75       0.83333333 0.5        0.41666667
 0.5        0.5        0.75       0.5       ]

mean value: 0.5583333333333333

key: train_roc_auc 
value: [0.8547619  0.9047619  0.80595238 0.875      0.80119048 0.925
 0.80952381 0.88095238 0.88095238 0.88095238]

mean value: 0.8619047619047621

key: test_jcc 
value: [0.2        0.33333333 0.5        0.66666667 0.         0.25
 0.33333333 0.         0.66666667 0.33333333]

mean value: 0.3283333333333333

key: train_jcc 
value: [0.73913043 0.80952381 0.66666667 0.75       0.61904762 0.85
 0.66666667 0.76190476 0.76190476 0.76190476]

mean value: 0.7386749482401657

MCC on Blind test: -0.07

MCC on Training: 0.12

Running classifier: 16 
Model_name: Passive Aggresive 
Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 PassiveAggressiveClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00955272 0.00897717 0.0087564  0.00835252 0.00891256 0.0086596
 0.00892782 0.00896478 0.00927091 0.00905704]

mean value: 0.00894315242767334

key: score_time 
value: [0.00858188 0.00854778 0.00825453 0.00844264 0.00822306 0.00890517
 0.0086174  0.00894809 0.00845265 0.00848794]

mean value: 0.008546113967895508

key: test_mcc 
value: [-0.16666667  0.16666667  0.66666667 -0.40824829  0.66666667 -0.61237244
  0.57735027  1.          0.          0.57735027]

mean value: 0.24674131455529272

key: train_mcc 
value: [0.95227002 0.90692382 0.50452498 0.62776482 0.95238095 0.95227002
 0.78446454 0.8660254  1.         0.95346259]

mean value: 0.850008713999979

key: test_fscore 
value: [0.4        0.5        0.8        0.57142857 0.8        0.
 0.8        1.         0.66666667 0.8       ]

mean value: 0.6338095238095238

key: train_fscore 
value: [0.97674419 0.95       0.77777778 0.81632653 0.97560976 0.97435897
 0.89361702 0.93333333 1.         0.97674419]

mean value: 0.927451176554951

key: test_precision 
value: [0.33333333 0.5        0.66666667 0.5        1.         0.
 0.66666667 1.         0.5        0.66666667]

mean value: 0.5833333333333333

key: train_precision 
value: [0.95454545 1.         0.63636364 0.68965517 0.95238095 1.
 0.80769231 0.875      1.         0.95454545]

mean value: 0.88701829779416

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.
 1.         1.         1.         1.        ]

mean value: 0.7333333333333333

key: train_recall 
value: [1.        0.9047619 1.        1.        1.        0.95      1.
 1.        1.        1.       ]

mean value: 0.9854761904761904

key: test_accuracy 
value: [0.4  0.6  0.8  0.4  0.8  0.2  0.75 1.   0.5  0.75]

mean value: 0.62

key: train_accuracy 
value: [0.97560976 0.95121951 0.70731707 0.7804878  0.97560976 0.97560976
 0.88095238 0.92857143 1.         0.97619048]

mean value: 0.9151567944250871

key: test_roc_auc 
value: [0.41666667 0.58333333 0.83333333 0.33333333 0.83333333 0.25
 0.75       1.         0.5        0.75      ]

mean value: 0.625

key: train_roc_auc 
value: [0.975      0.95238095 0.7        0.78571429 0.97619048 0.975
 0.88095238 0.92857143 1.         0.97619048]

mean value: 0.915

key: test_jcc 
value: [0.25       0.33333333 0.66666667 0.4        0.66666667 0.
 0.66666667 1.         0.5        0.66666667]

mean value: 0.5149999999999999

key: train_jcc 
value: [0.95454545 0.9047619  0.63636364 0.68965517 0.95238095 0.95
 0.80769231 0.875      1.         0.95454545]

mean value: 0.8724944882703504

MCC on Blind test: -0.05

MCC on Training: 0.25

Running classifier: 17 
Model_name: QDA 
Model func: QuadraticDiscriminantAnalysis()

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', QuadraticDiscriminantAnalysis())])

key: fit_time 
value: [0.0129261  0.00976014 0.00970173 0.00997019 0.00931287 0.00911713
 0.00895834 0.00990105 0.01032019 0.00951266]

mean value: 0.009948039054870605

key: score_time 
value: [0.00934935 0.00955534 0.00973749 0.00854111 0.00894237 0.00947976
 0.00929594 0.00918293 0.00990033 0.00897884]

mean value: 0.009296345710754394

key: test_mcc 
value: [ 0.          0.16666667  0.16666667  0.16666667  0.16666667 -0.66666667
 -0.57735027  0.57735027  0.          0.        ]

mean value: 0.0

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.57142857 0.5        0.5        0.66666667 0.66666667 0.33333333
 0.4        0.8        0.5        0.66666667]

mean value: 0.5604761904761906

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.4        0.5        0.5        0.66666667 0.66666667 0.33333333
 0.33333333 0.66666667 0.5        0.5       ]

mean value: 0.5066666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [1.         0.5        0.5        0.66666667 0.66666667 0.33333333
 0.5        1.         0.5        1.        ]

mean value: 0.6666666666666666

key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.6  0.6  0.6  0.6  0.2  0.25 0.75 0.5  0.5 ]

mean value: 0.5

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.5        0.58333333 0.58333333 0.58333333 0.58333333 0.16666667
 0.25       0.75       0.5        0.5       ]

mean value: 0.5

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.4        0.33333333 0.33333333 0.5        0.5        0.2
 0.25       0.66666667 0.33333333 0.5       ]

mean value: 0.4016666666666667

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.22

MCC on Training: 0.0

Running classifier: 18 
Model_name: Random Forest 
Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(n_estimators=1000, n_jobs=12,
                                        random_state=42))])

key: fit_time 
value: [0.55287409 0.54379725 0.61343741 0.55234051 0.57824874 0.53843427
 0.58962297 0.59904552 0.57388043 0.50798321]

mean value: 0.5649664402008057

key: score_time 
value: [0.11841536 0.16420984 0.15634918 0.14242959 0.1531136  0.12256479
 0.16733861 0.17010379 0.17674661 0.12807941]

mean value: 0.14993507862091066

key: test_mcc 
value: [ 0.16666667 -0.40824829  1.         -0.40824829  0.61237244 -0.66666667
  0.57735027  1.          0.          0.57735027]

mean value: 0.24505763931473198

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.5        0.         1.         0.57142857 0.85714286 0.33333333
 0.8        1.         0.66666667 0.66666667]

mean value: 0.6395238095238096

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.5        0.         1.         0.5        0.75       0.33333333
 0.66666667 1.         0.5        1.        ]

mean value: 0.625

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.         1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.7

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.6  0.4  1.   0.4  0.8  0.2  0.75 1.   0.5  0.75]

mean value: 0.64

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.58333333 0.33333333 1.         0.33333333 0.75       0.16666667
 0.75       1.         0.5        0.75      ]

mean value: 0.6166666666666666

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.33333333 0.         1.         0.4        0.75       0.2
 0.66666667 1.         0.5        0.5       ]

mean value: 0.5349999999999999

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.22

MCC on Training: 0.25

Running classifier: 19 
Model_name: Random Forest2 
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                       n_estimators=1000, n_jobs=12, oob_score=True,
                       random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...age_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model',
                 RandomForestClassifier(max_features='auto', min_samples_leaf=5,
                                        n_estimators=1000, n_jobs=12,
                                        oob_score=True, random_state=42))])

key: fit_time 
value: [0.84869552 0.9469893  0.85308099 0.84434319 0.85773826 0.85807896
 0.85562038 0.89091301 0.8702352  0.84725904]

mean value: 0.8672953844070435

key: score_time 
value: [0.19435334 0.18948293 0.18052363 0.17982292 0.2137394  0.16985703
 0.17996979 0.17333531 0.25137925 0.13845086]

mean value: 0.18709144592285157

key: test_mcc 
value: [ 0.16666667  0.61237244  1.          0.16666667  0.61237244 -0.66666667
  0.57735027  1.          0.          0.57735027]

mean value: 0.4046112076437508

key: train_mcc 
value: [0.8047619  0.90692382 0.8047619  0.90238095 0.85441771 0.90238095
 0.85811633 0.85811633 0.81322028 0.80952381]

mean value: 0.8514604000794735

key: test_fscore 
value: [0.5        0.66666667 1.         0.66666667 0.85714286 0.33333333
 0.8        1.         0.66666667 0.66666667]

mean value: 0.7157142857142857

key: train_fscore 
value: [0.9047619  0.95       0.9047619  0.95       0.92307692 0.95
 0.93023256 0.92682927 0.9        0.9047619 ]

mean value: 0.9244424463794856

key: test_precision 
value: [0.5        1.         1.         0.66666667 0.75       0.33333333
 0.66666667 1.         0.5        1.        ]

mean value: 0.7416666666666666

key: train_precision 
value: [0.9047619  1.         0.9047619  0.95       0.94736842 0.95
 0.90909091 0.95       0.94736842 0.9047619 ]

mean value: 0.9368113465481887

key: test_recall 
value: [0.5        0.5        1.         0.66666667 1.         0.33333333
 1.         1.         1.         0.5       ]

mean value: 0.75

key: train_recall 
value: [0.9047619  0.9047619  0.9047619  0.95       0.9        0.95
 0.95238095 0.9047619  0.85714286 0.9047619 ]

mean value: 0.9133333333333334

key: test_accuracy 
value: [0.6  0.8  1.   0.6  0.8  0.2  0.75 1.   0.5  0.75]

mean value: 0.7

key: train_accuracy 
value: [0.90243902 0.95121951 0.90243902 0.95121951 0.92682927 0.95121951
 0.92857143 0.92857143 0.9047619  0.9047619 ]

mean value: 0.9252032520325203

key: test_roc_auc 
value: [0.58333333 0.75       1.         0.58333333 0.75       0.16666667
 0.75       1.         0.5        0.75      ]

mean value: 0.6833333333333333

key: train_roc_auc 
value: [0.90238095 0.95238095 0.90238095 0.95119048 0.92619048 0.95119048
 0.92857143 0.92857143 0.9047619  0.9047619 ]

mean value: 0.9252380952380953

key: test_jcc 
value: [0.33333333 0.5        1.         0.5        0.75       0.2
 0.66666667 1.         0.5        0.5       ]

mean value: 0.595

key: train_jcc 
value: [0.82608696 0.9047619  0.82608696 0.9047619  0.85714286 0.9047619
 0.86956522 0.86363636 0.81818182 0.82608696]

mean value: 0.8601072840203274

MCC on Blind test: -0.13

MCC on Training: 0.4

Running classifier: 20 
Model_name: Ridge Classifier 
Model func: RidgeClassifier(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifier(random_state=42))])

key: fit_time 
value: [0.02401781 0.00959349 0.00884938 0.0091269  0.0089922  0.00978351
 0.01024103 0.01020026 0.00907898 0.01002502]

mean value: 0.01099085807800293

key: score_time 
value: [0.01877618 0.00848961 0.00889301 0.00845647 0.00908351 0.00914979
 0.0093224  0.00906873 0.00942087 0.00936842]

mean value: 0.010002899169921874

key: test_mcc 
value: [-0.16666667 -0.16666667  0.61237244 -0.16666667  0.66666667 -0.66666667
  1.          1.          0.          0.57735027]

mean value: 0.26897227048854205

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.4        0.66666667 0.4        0.8        0.33333333
 1.         1.         0.66666667 0.8       ]

mean value: 0.6466666666666667

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.33333333 1.         0.5        1.         0.33333333
 1.         1.         0.5        0.66666667]

mean value: 0.6666666666666667

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        0.5        0.33333333 0.66666667 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.4  0.8  0.4  0.8  0.2  1.   1.   0.5  0.75]

mean value: 0.625

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.41666667 0.75       0.41666667 0.83333333 0.16666667
 1.         1.         0.5        0.75      ]

mean value: 0.625

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.25       0.5        0.25       0.66666667 0.2
 1.         1.         0.5        0.66666667]

mean value: 0.5283333333333334

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: -0.31

MCC on Training: 0.27

Running classifier: 21 
Model_name: Ridge ClassifierCV 
Model func: RidgeClassifierCV(cv=3)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', RidgeClassifierCV(cv=3))])

key: fit_time 
value: [0.02835059 0.02416348 0.02409005 0.02419853 0.02421737 0.02447224
 0.02423191 0.02439237 0.02421069 0.02482486]

mean value: 0.024715209007263185

key: score_time 
value: [0.00838375 0.00844097 0.00840139 0.00842237 0.00849485 0.00844979
 0.00832534 0.00857782 0.00840831 0.00849867]

mean value: 0.008440327644348145

key: test_mcc 
value: [-0.16666667 -0.16666667  0.61237244 -0.16666667  0.66666667 -0.66666667
  1.          1.          0.          0.57735027]

mean value: 0.26897227048854205

key: train_mcc 
value: [1.         1.         0.8047619  1.         1.         1.
 1.         1.         1.         0.80952381]

mean value: 0.9614285714285714

key: test_fscore 
value: [0.4        0.4        0.66666667 0.4        0.8        0.33333333
 1.         1.         0.66666667 0.8       ]

mean value: 0.6466666666666667

key: train_fscore 
value: [1.        1.        0.9047619 1.        1.        1.        1.
 1.        1.        0.9047619]

mean value: 0.980952380952381

key: test_precision 
value: [0.33333333 0.33333333 1.         0.5        1.         0.33333333
 1.         1.         0.5        0.66666667]

mean value: 0.6666666666666667

key: train_precision 
value: [1.        1.        0.9047619 1.        1.        1.        1.
 1.        1.        0.9047619]

mean value: 0.980952380952381

key: test_recall 
value: [0.5        0.5        0.5        0.33333333 0.66666667 0.33333333
 1.         1.         1.         1.        ]

mean value: 0.6833333333333333

key: train_recall 
value: [1.        1.        0.9047619 1.        1.        1.        1.
 1.        1.        0.9047619]

mean value: 0.980952380952381

key: test_accuracy 
value: [0.4  0.4  0.8  0.4  0.8  0.2  1.   1.   0.5  0.75]

mean value: 0.625

key: train_accuracy 
value: [1.         1.         0.90243902 1.         1.         1.
 1.         1.         1.         0.9047619 ]

mean value: 0.9807200929152149

key: test_roc_auc 
value: [0.41666667 0.41666667 0.75       0.41666667 0.83333333 0.16666667
 1.         1.         0.5        0.75      ]

mean value: 0.625

key: train_roc_auc 
value: [1.         1.         0.90238095 1.         1.         1.
 1.         1.         1.         0.9047619 ]

mean value: 0.9807142857142856

key: test_jcc 
value: [0.25       0.25       0.5        0.25       0.66666667 0.2
 1.         1.         0.5        0.66666667]

mean value: 0.5283333333333334

key: train_jcc 
value: [1.         1.         0.82608696 1.         1.         1.
 1.         1.         1.         0.82608696]

mean value: 0.9652173913043477

MCC on Blind test: -0.31

MCC on Training: 0.27

Running classifier: 22 
Model_name: SVC 
Model func: SVC(random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SVC(random_state=42))])

key: fit_time 
value: [0.008358   0.00831342 0.0082972  0.00830984 0.00820947 0.00865245
 0.00870299 0.00864148 0.00915098 0.00915289]

mean value: 0.008578872680664063

key: score_time 
value: [0.0083375  0.00837874 0.00833702 0.00835991 0.00863719 0.00874734
 0.00867629 0.00871253 0.00896692 0.00917864]

mean value: 0.00863320827484131

key: test_mcc 
value: [-0.40824829  0.16666667  0.16666667  0.66666667  0.16666667 -1.
  0.57735027  0.57735027  0.57735027  0.57735027]

mean value: 0.20678194529613067

key: train_mcc 
value: [0.70714286 0.90692382 0.65952381 0.80817439 0.7098505  0.75714286
 0.71428571 0.72760688 0.71428571 0.76980036]

mean value: 0.7474736906738404

key: test_fscore 
value: [0.         0.5        0.5        0.8        0.66666667 0.
 0.8        0.66666667 0.8        0.66666667]

mean value: 0.54

key: train_fscore 
value: [0.85714286 0.95       0.82926829 0.89473684 0.84210526 0.87804878
 0.85714286 0.84210526 0.85714286 0.88888889]

mean value: 0.8696581901909244

key: test_precision 
value: [0.         0.5        0.5        1.         0.66666667 0.
 0.66666667 1.         0.66666667 1.        ]

mean value: 0.6

key: train_precision 
value: [0.85714286 1.         0.85       0.94444444 0.88888889 0.85714286
 0.85714286 0.94117647 0.85714286 0.83333333]

mean value: 0.8886414565826332

key: test_recall 
value: [0.         0.5        0.5        0.66666667 0.66666667 0.
 1.         0.5        1.         0.5       ]

mean value: 0.5333333333333333

key: train_recall 
value: [0.85714286 0.9047619  0.80952381 0.85       0.8        0.9
 0.85714286 0.76190476 0.85714286 0.95238095]

mean value: 0.8549999999999999

key: test_accuracy 
value: [0.4  0.6  0.6  0.8  0.6  0.   0.75 0.75 0.75 0.75]

mean value: 0.6

key: train_accuracy 
value: [0.85365854 0.95121951 0.82926829 0.90243902 0.85365854 0.87804878
 0.85714286 0.85714286 0.85714286 0.88095238]

mean value: 0.8720673635307781

key: test_roc_auc 
value: [0.33333333 0.58333333 0.58333333 0.83333333 0.58333333 0.
 0.75       0.75       0.75       0.75      ]

mean value: 0.5916666666666666

key: train_roc_auc 
value: [0.85357143 0.95238095 0.8297619  0.90119048 0.85238095 0.87857143
 0.85714286 0.85714286 0.85714286 0.88095238]

mean value: 0.8720238095238096

key: test_jcc 
value: [0.         0.33333333 0.33333333 0.66666667 0.5        0.
 0.66666667 0.5        0.66666667 0.5       ]

mean value: 0.41666666666666663

key: train_jcc 
value: [0.75       0.9047619  0.70833333 0.80952381 0.72727273 0.7826087
 0.75       0.72727273 0.75       0.8       ]

mean value: 0.7709773197816676

MCC on Blind test: 0.12

MCC on Training: 0.21

Running classifier: 23 
Model_name: Stochastic GDescent 
Model func: SGDClassifier(n_jobs=12, random_state=42)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
       'lineage_count_unique'],
      dtype='object', length=165)),
                                                 ('cat', OneHotEncoder(),
                                                  Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
       'polarity_change', 'water_change', 'active_site'],
      dtype='object'))])),
                ('model', SGDClassifier(n_jobs=12, random_state=42))])

key: fit_time 
value: [0.00836658 0.00825715 0.00891399 0.00957584 0.00861096 0.00823355
 0.00815821 0.00837421 0.00826669 0.00890207]

mean value: 0.008565926551818847

key: score_time 
value: [0.00836182 0.00851631 0.00847912 0.00961375 0.00854182 0.00819373
 0.00820446 0.00824189 0.00828218 0.0082612 ]

mean value: 0.008469629287719726

key: test_mcc 
value: [-0.16666667  0.          1.          0.66666667  0.66666667 -1.
  0.          1.          0.57735027  0.57735027]

mean value: 0.3321367205045918

key: train_mcc 
value: [1.         0.23204774 0.62048368 0.95227002 0.95238095 0.80907152
 0.2773501  1.         0.90889326 0.90889326]

mean value: 0.7661390523375757

key: test_fscore 
value: [0.4        0.57142857 1.         0.8        0.8        0.
 0.66666667 1.         0.8        0.8       ]

mean value: 0.6838095238095238

key: train_fscore 
value: [1.         0.7        0.82352941 0.97435897 0.97560976 0.9047619
 0.7        1.         0.95       0.95454545]

mean value: 0.8982805501528601

key: test_precision 
value: [0.33333333 0.4        1.         1.         1.         0.
 0.5        1.         0.66666667 0.66666667]

mean value: 0.6566666666666667

key: train_precision 
value: [1.         0.53846154 0.7        1.         0.95238095 0.86363636
 0.53846154 1.         1.         0.91304348]

mean value: 0.8505983871201263

key: test_recall 
value: [0.5        1.         1.         0.66666667 0.66666667 0.
 1.         1.         1.         1.        ]

mean value: 0.7833333333333333

key: train_recall 
value: [1.        1.        1.        0.95      1.        0.95      1.
 1.        0.9047619 1.       ]

mean value: 0.9804761904761905

key: test_accuracy 
value: [0.4  0.4  1.   0.8  0.8  0.   0.5  1.   0.75 0.75]

mean value: 0.64

key: train_accuracy 
value: [1.         0.56097561 0.7804878  0.97560976 0.97560976 0.90243902
 0.57142857 1.         0.95238095 0.95238095]

mean value: 0.8671312427409988

key: test_roc_auc 
value: [0.41666667 0.5        1.         0.83333333 0.83333333 0.
 0.5        1.         0.75       0.75      ]

mean value: 0.6583333333333333

key: train_roc_auc 
value: [1.         0.55       0.775      0.975      0.97619048 0.90357143
 0.57142857 1.         0.95238095 0.95238095]

mean value: 0.865595238095238

key: test_jcc 
value: [0.25       0.4        1.         0.66666667 0.66666667 0.
 0.5        1.         0.66666667 0.66666667]

mean value: 0.5816666666666667

key: train_jcc 
value: [1.         0.53846154 0.7        0.95       0.95238095 0.82608696
 0.53846154 1.         0.9047619  0.91304348]

mean value: 0.8323196368848542

MCC on Blind test: 0.04

MCC on Training: 0.33

Running classifier: 24 
Model_name: XGBoost 
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None,
              enable_categorical=False, gamma=None, gpu_id=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=12, num_parallel_tree=None,
              predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              use_label_encoder=False, validate_parameters=None, verbosity=0)

Running model pipeline: Pipeline(steps=[('prep',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num', MinMaxScaler(),
                                                  Index(['consurf_score', 'snap2_score', 'provean_score',
       'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
       'contacts', 'electro_rr', 'electro_mm',
       ...
       'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf',
       'lineage_proportion', 'dist_linea...
                               interaction_constraints=None, learning_rate=None,
                               max_delta_step=None, max_depth=None,
                               min_child_weight=None, missing=nan,
                               monotone_constraints=None, n_estimators=100,
                               n_jobs=12, num_parallel_tree=None,
                               predictor=None, random_state=42, reg_alpha=None,
                               reg_lambda=None, scale_pos_weight=None,
                               subsample=None, tree_method=None,
                               use_label_encoder=False,
                               validate_parameters=None, verbosity=0))])

key: fit_time 
value: [0.07816386 0.03662848 0.03439021 0.033813   0.03872108 0.05464959
 0.03403544 0.03438354 0.03463316 0.03474498]

mean value: 0.041416335105896

key: score_time 
value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:427: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_CV['source_data'] = 'CV'
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:454: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scoresDF_BT['source_data'] = 'BT'
[0.01122093 0.01033044 0.00993037 0.00991368 0.00994444 0.01005077
 0.01007438 0.01027513 0.01007533 0.01037931]

mean value: 0.010219478607177734

key: test_mcc 
value: [-0.16666667  0.16666667  1.          0.66666667  0.16666667  0.40824829
  0.57735027  0.          0.          1.        ]

mean value: 0.3818931892986822

key: train_mcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_fscore 
value: [0.4        0.5        1.         0.8        0.66666667 0.5
 0.8        0.5        0.66666667 1.        ]

mean value: 0.6833333333333333

key: train_fscore 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_precision 
value: [0.33333333 0.5        1.         1.         0.66666667 1.
 0.66666667 0.5        0.5        1.        ]

mean value: 0.7166666666666666

key: train_precision 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_recall 
value: [0.5        0.5        1.         0.66666667 0.66666667 0.33333333
 1.         0.5        1.         1.        ]

mean value: 0.7166666666666666

key: train_recall 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_accuracy 
value: [0.4  0.6  1.   0.8  0.6  0.6  0.75 0.5  0.5  1.  ]

mean value: 0.675

key: train_accuracy 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_roc_auc 
value: [0.41666667 0.58333333 1.         0.83333333 0.58333333 0.66666667
 0.75       0.5        0.5        1.        ]

mean value: 0.6833333333333333

key: train_roc_auc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

key: test_jcc 
value: [0.25       0.33333333 1.         0.66666667 0.5        0.33333333
 0.66666667 0.33333333 0.5        1.        ]

mean value: 0.5583333333333333

key: train_jcc 
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

mean value: 1.0

MCC on Blind test: 0.22

MCC on Training: 0.38

Extracting tts_split_name: 70_30

Total cols in each df: 
CV df: 8 
metaDF: 15

Adding column: Model_name

Total cols in bts df: 
BT_df: 8

First proceeding to rowbind CV and BT dfs:

Final output should have: 23 columns

Combinig 2 using pd.concat by row ~ rowbind 
Checking Dims of df to combine: 
Dim of CV: (24, 8) 
Dim of BT: (24, 8)
8

Number of Common columns: 8 
These are: ['MCC', 'ROC_AUC', 'Accuracy', 'Precision', 'JCC', 'F1', 'source_data', 'Recall']

Concatenating dfs with different resampling methods [WF]: 
Split type: 70_30 
No. of dfs combining: 2

PASS: 2 dfs successfully combined 
nrows in combined_df_wf: 48 
ncols in combined_df_wf: 8

PASS: proceeding to merge metadata with CV and BT dfs

Adding column: Model_name

========================================================= 
SUCCESS: Ran multiple classifiers 
=======================================================
Traceback (most recent call last):
  File "/home/tanu/git/LSHTM_analysis/scripts/ml/./ml_iterator.py", line 107, in <module>
    out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+ out_filename), index = False)
  File "/home/tanu/.local/lib/python3.9/site-packages/pandas/core/generic.py", line 3563, in to_csv
    return DataFrameRenderer(formatter).to_csv(
  File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/formats/format.py", line 1180, in to_csv
    csv_formatter.save()
  File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/formats/csvs.py", line 241, in save
    with get_handle(
  File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/common.py", line 697, in get_handle
    check_parent_directory(str(handle))
  File "/home/tanu/.local/lib/python3.9/site-packages/pandas/io/common.py", line 571, in check_parent_directory
    raise OSError(fr"Cannot save file into a non-existent directory: '{parent}'")
OSError: Cannot save file into a non-existent directory: '/home/tanu/git/Data/ml_combined/genes/home/tanu/git/LSHTM_ML/output/genes'
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...

<00><><FE><FF><FF><FF><FF><FF><00><><FE><FF><FF><FF><FF><FF><FF><FF><FE><FF><FF><FF><FF><FF><FF><FF><FA><FF><FF><FF><FF><FF><00>Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
V<B2><C7>?0*<2A><13>D<D0>?x<>q<B4>Z|¿<><C2BF>y<D0>]<5D><><A2><B2><BF>#bJ$<24>?]3<>f<F9><1B>?-C<1C><>6z?[<5B><> <20><>|<7C><><BF>#Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 9 for this parallel run (total 100)...
Building estimator 2 of 9 for this parallel run (total 100)...
Building estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
(<18>uIv<00><0E>b<F6><62>d&0.<18>u<00>;]U3<>Z<FC><5A>{f4p+<18>u<00>;]U+Ekݧ<6B>;I0!<18>u<00>;]UB<>ף<93>v2<76><32>+<2B>u<00>;]U8<>q9$Ҝ'p%<18>u<00>;]U)<29>Y<C2>e<>a<FE>.<18>u<00>;]UD"r<>!<21>
<D0>+<2B>u<00>;]UXO<58><4F><96>JB<4A><42><1E>u<00>;]U<00>ymm}<7D><><B3>p<C0><1E>u<00>;]U<00><><B8>ԸĂM0(<18>u<00>;]U<00>n
<1D>R<D9>w<D0>#<18>u<00>;]U<00>\<5C><>y<B5>wx<77><78><1E>u<00>;]U<00>h<B5><68> F<>\<5C>+<18>u<00>;]U@<40><15>s<C1>1<>(<18>u0<1B>u`<60>{<7B><05>K@<40><1E>u<00>;]U6<>W8_<38><5F>԰,<18>uPNv}<7D>W<91>F<FA><46>0<D2>><3E>u<00>Jv<00><><8E>t+J<1F><><A0><1E>u<00>;]U<00><02>cH<>+p<><1E>u<00>;]UЬ<>
<0A><>հ-<18>u<00>;]U<00>>'8<>-<2D>p/<18>u<00>;]U<00><>Fˮ<46>BL<42><4C><1E>u<00>;]U<00><1D><><C1><B4>G<9E><47>$<18>u<00>;]U2<1E><0F>0ɠ<30><1E>u<00>[;]U<02><>U<9F><55>e\<5C>><3E><>up<><17>u|<7C><><E1><A5><DB><F9><81><AB><DA>z<EB>uBuilding estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
<00>Rfj<66>U<00>Rfj<66>U<00>Rfj<66>U0~;i<>U`~;i<>Up~;i<>U<00><>:j<>U<00><>:j<>U<00><>:j<>USfj<66>U0Sfj<66>U@Sfj<66>U`<00>Rfj<66>U<00><00><>L"?<00><><80><FF><FF><FF><FF><FF><FF><00>27=<00><><FF><FF><FF><FF><FF><FF><<3C>=<3D>p`p};i<>UૢP<E0ABA2><00><><D0>h<C1>UBuilding estimator 3 of 9 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 4 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 5 of 9 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 9 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 9 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 8 of 9 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 9 of 9 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...
Building estimator 1 of 8 for this parallel run (total 100)...
Building estimator 2 of 8 for this parallel run (total 100)...
Building estimator 3 of 8 for this parallel run (total 100)...
Building estimator 4 of 8 for this parallel run (total 100)...
Building estimator 5 of 8 for this parallel run (total 100)...
Building estimator 6 of 8 for this parallel run (total 100)...
Building estimator 7 of 8 for this parallel run (total 100)...
Building estimator 8 of 8 for this parallel run (total 100)...