From b37a950fec43ebf1c7448f9107b1d24e5a3de202 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Fri, 24 Jun 2022 15:40:18 +0100 Subject: [PATCH] optimised run_7030.py to generate ouput from dict now that the processfunction and parameter dicts have been added --- scripts/ml/MultClfs.py | 68 +- scripts/ml/log_alr_7030.txt | 72 - scripts/ml/log_embb_7030.txt | 26177 ---------------------------- scripts/ml/log_gid_7030.txt | 24347 -------------------------- scripts/ml/log_katg_7030.txt | 25943 --------------------------- scripts/ml/log_pnca_7030.txt | 25034 -------------------------- scripts/ml/log_rpob_7030.txt | 26172 --------------------------- scripts/ml/ml_data_7030.py | 2 +- scripts/ml/run_7030.py | 635 +- scripts/ml/run_7030_LOOP.py | 112 +- scripts/ml/running_ml_scripts.txt | 5 +- scripts/ml/test_MultClfs.py | 21 +- 12 files changed, 180 insertions(+), 128408 deletions(-) delete mode 100644 scripts/ml/log_alr_7030.txt delete mode 100644 scripts/ml/log_embb_7030.txt delete mode 100644 scripts/ml/log_gid_7030.txt delete mode 100644 scripts/ml/log_katg_7030.txt delete mode 100644 scripts/ml/log_pnca_7030.txt delete mode 100644 scripts/ml/log_rpob_7030.txt mode change 100755 => 100644 scripts/ml/run_7030.py diff --git a/scripts/ml/MultClfs.py b/scripts/ml/MultClfs.py index 7e04e71..39377fa 100644 --- a/scripts/ml/MultClfs.py +++ b/scripts/ml/MultClfs.py @@ -197,35 +197,35 @@ def MultModelsCl(input_df, target, skf_cv # Specify multiple Classification Models #====================================================== models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) ) - # , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) ) - # , ('Decision Tree' , DecisionTreeClassifier(**rs) ) - # , ('Extra Tree' , ExtraTreeClassifier(**rs) ) - # , ('Extra Trees' , ExtraTreesClassifier(**rs) ) - # , ('Gradient Boosting' , GradientBoostingClassifier(**rs) ) - # , ('Gaussian NB' , GaussianNB() ) - # , ('Gaussian Process' , GaussianProcessClassifier(**rs) ) - # , ('K-Nearest Neighbors' , KNeighborsClassifier() ) - # , ('LDA' , LinearDiscriminantAnalysis() ) + , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) ) + , ('Decision Tree' , DecisionTreeClassifier(**rs) ) + , ('Extra Tree' , ExtraTreeClassifier(**rs) ) + , ('Extra Trees' , ExtraTreesClassifier(**rs) ) + , ('Gradient Boosting' , GradientBoostingClassifier(**rs) ) + , ('Gaussian NB' , GaussianNB() ) + , ('Gaussian Process' , GaussianProcessClassifier(**rs) ) + , ('K-Nearest Neighbors' , KNeighborsClassifier() ) + , ('LDA' , LinearDiscriminantAnalysis() ) , ('Logistic Regression' , LogisticRegression(**rs) ) - # , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs)) - # , ('MLP' , MLPClassifier(max_iter = 500, **rs) ) - # , ('Multinomial' , MultinomialNB() ) - # , ('Naive Bayes' , BernoulliNB() ) - # , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) ) - # , ('QDA' , QuadraticDiscriminantAnalysis() ) - # , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) ) - # , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5 - # , n_estimators = 1000 - # , bootstrap = True - # , oob_score = True - # , **njobs - # , **rs - # , max_features = 'auto') ) - # , ('Ridge Classifier' , RidgeClassifier(**rs) ) - # , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) ) - # , ('SVC' , SVC(**rs) ) - # , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) ) - # , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) ) + , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs)) + , ('MLP' , MLPClassifier(max_iter = 500, **rs) ) + , ('Multinomial' , MultinomialNB() ) + , ('Naive Bayes' , BernoulliNB() ) + , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) ) + , ('QDA' , QuadraticDiscriminantAnalysis() ) + , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) ) + , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5 + , n_estimators = 1000 + , bootstrap = True + , oob_score = True + , **njobs + , **rs + , max_features = 'auto') ) + , ('Ridge Classifier' , RidgeClassifier(**rs) ) + , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) ) + , ('SVC' , SVC(**rs) ) + , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) ) + , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) ) ] mm_skf_scoresD = {} @@ -440,10 +440,11 @@ def ProcessMultModelsCl(inputD = {}): , '\nCV df:', len(scoresDF_CV.columns) , '\nBT_df:', len(scoresDF_BT.columns) , '\nmetaDF:', len(metaDF.columns)) + if len(scoresDF_CV.columns) == len(scoresDF_BT.columns): print('\nFirst proceeding to rowbind CV and BT dfs:') expected_ncols_out = len(scoresDF_BT.columns) + len(metaDF.columns) - print('\nFinal output should have:',expected_ncols_out, 'columns' ) + print('\nFinal output should have:', expected_ncols_out, 'columns' ) #----------------- # Combine WF @@ -496,8 +497,7 @@ def ProcessMultModelsCl(inputD = {}): sys.exit('\nFIRST IF FAILS') else: print('\nConcatenting dfs not possible [WF],check numbers ') - - + #------------------------------------- # Combine WF+Metadata: Final output #------------------------------------- @@ -515,11 +515,15 @@ def ProcessMultModelsCl(inputD = {}): print('\nPASS: Combined df has expected ncols') else: sys.exit('\nFAIL: Length mismatch for combined_df') + + print('\nAdding column: Model_name') + + combDF['Model_name'] = combDF.index print('\n=========================================================' , '\nSUCCESS: Ran multiple classifiers' , '\n=======================================================') - + #resampling_methods_wf = combined_baseline_wf[['resampling']] #resampling_methods_wf = resampling_methods_wf.drop_duplicates() #, '\n', resampling_methods_wf) diff --git a/scripts/ml/log_alr_7030.txt b/scripts/ml/log_alr_7030.txt deleted file mode 100644 index 725784f..0000000 --- a/scripts/ml/log_alr_7030.txt +++ /dev/null @@ -1,72 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -1.22.4 -1.4.1 - -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: 271 - -PASS: my_features_df and aa_df successfully combined -nrows: 271 -ncols: 269 -count of NULL values before imputation - -or_mychisq 256 -log10_or_mychisq 256 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 174 -Traceback (most recent call last): - File "/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py", line 35, in - setvars(gene,drug) - File "/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py", line 636, in setvars - X, X_bts, y, y_bts = train_test_split(x_features, y_target - File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 2454, in train_test_split - train, test = next(cv.split(X=arrays[0], y=stratify)) - File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 1613, in split - for train, test in self._iter_indices(X, y, groups): - File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 1953, in _iter_indices - raise ValueError( -ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. diff --git a/scripts/ml/log_embb_7030.txt b/scripts/ml/log_embb_7030.txt deleted file mode 100644 index 69fed9b..0000000 --- a/scripts/ml/log_embb_7030.txt +++ /dev/null @@ -1,26177 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -/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/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -1.22.4 -1.4.1 - -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: 858 - -PASS: my_features_df and aa_df successfully combined -nrows: 858 -ncols: 269 -count of NULL values before imputation - -or_mychisq 244 -log10_or_mychisq 244 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 174 - -PASS: ML data with input features, training and test generated... - -Total no. of input features: 174 ---------No. of numerical features: 168 ---------No. of categorical features: 6 - -Total no. of evolutionary features: 3 - -Total no. of stability features: 28 ---------Common stabilty cols: 5 ---------Foldx cols: 23 - -Total no. of affinity features: 5 ---------Common affinity cols: 3 ---------Gene specific affinity cols: 2 - -Total no. of residue level features: 132 ---------AA index cols: 123 ---------Residue Prop cols: 3 ---------AA change Prop cols: 6 - -Total no. of genomic features: 6 ---------MAF+OR cols: 2 ---------Lineage cols: 4 ---------Other cols: 0 - -------------------------------------------------------------- -Successfully split data: ALL features -actual values: training set -imputed values: blind test set - -Total data size: 448 - -Train data size: (300, 174) -y_train numbers: Counter({0: 236, 1: 64}) - -Test data size: (148, 174) -y_test_numbers: Counter({0: 117, 1: 31}) - -y_train ratio: 3.6875 -y_test ratio: 3.774193548387097 -------------------------------------------------------------- - -index: 0 -ind: 1 - -Mask count check: True - -index: 1 -ind: 2 - -Mask count check: False -Original Data - Counter({0: 236, 1: 64}) Data dim: (300, 174) -Simple Random OverSampling - Counter({1: 236, 0: 236}) -(472, 174) -Simple Random UnderSampling - Counter({0: 64, 1: 64}) -(128, 174) -Simple Combined Over and UnderSampling - Counter({0: 236, 1: 236}) -(472, 174) - -SMOTE_NC OverSampling - Counter({1: 236, 0: 236}) -(472, 174) - -##################################################################### - -Running ML analysis: feature groups -Gene name: embB -Drug name: ethambutol - -Output directory: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/ - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -key: fit_time -value: [0.02937937 0.03311324 0.03480864 0.0340519 0.03387308 0.03259921 - 0.03355289 0.03285098 0.03236556 0.05050564] - -mean value: 0.034710049629211426 - -key: score_time -value: [0.01199722 0.01186538 0.01197195 0.01283383 0.01298165 0.01298761 - 0.01297903 0.01430368 0.01283002 0.01292968] - -mean value: 0.012768006324768067 - -key: test_mcc -value: [0.60421798 0.47913962 0.34151451 0.16850509 0.78446454 0.53452248 - 0.53452248 0.67082039 0.2941742 0.38888889] - -mean value: 0.48007701907786793 - -key: train_mcc -value: [0.7168396 0.69115394 0.75506049 0.74208347 0.7211399 0.73357553 - 0.7084467 0.70860165 0.73425497 0.68294833] - -mean value: 0.7194104566119666 - -key: test_fscore -value: [0.6 0.54545455 0.4 0.22222222 0.8 0.5 - 0.5 0.72727273 0.4 0.44444444] - -mean value: 0.513939393939394 - -key: train_fscore -value: [0.75 0.72340426 0.78350515 0.7755102 0.75510204 0.76767677 - 0.74747475 0.74226804 0.7628866 0.72164948] - -mean value: 0.7529477293719139 - -key: test_precision -value: [1. 0.75 0.66666667 0.5 1. 1. - 1. 0.8 0.5 0.66666667] - -mean value: 0.7883333333333333 - -key: train_precision -value: [0.92307692 0.91891892 0.95 0.92682927 0.925 0.92682927 - 0.90243902 0.92307692 0.94871795 0.8974359 ] - -mean value: 0.9242324172202222 - -key: test_recall -value: [0.42857143 0.42857143 0.28571429 0.14285714 0.66666667 0.33333333 - 0.33333333 0.66666667 0.33333333 0.33333333] - -mean value: 0.3952380952380953 - -key: train_recall -value: [0.63157895 0.59649123 0.66666667 0.66666667 0.63793103 0.65517241 - 0.63793103 0.62068966 0.63793103 0.60344828] - -mean value: 0.6354506957047793 - -key: test_accuracy -value: [0.86666667 0.83333333 0.8 0.76666667 0.93333333 0.86666667 - 0.86666667 0.9 0.8 0.83333333] - -mean value: 0.8466666666666667 - -key: train_accuracy -value: [0.91111111 0.9037037 0.92222222 0.91851852 0.91111111 0.91481481 - 0.90740741 0.90740741 0.91481481 0.9 ] - -mean value: 0.9111111111111112 - -key: test_roc_auc -value: [0.71428571 0.69254658 0.62111801 0.54968944 0.83333333 0.66666667 - 0.66666667 0.8125 0.625 0.64583333] - -mean value: 0.6827639751552794 - -key: train_roc_auc -value: [0.80874722 0.79120336 0.8286385 0.82629108 0.81189005 0.82051074 - 0.80953155 0.80326936 0.81424854 0.79229018] - -mean value: 0.8106620561525046 - -key: test_jcc -value: [0.42857143 0.375 0.25 0.125 0.66666667 0.33333333 - 0.33333333 0.57142857 0.25 0.28571429] - -mean value: 0.36190476190476184 - -key: train_jcc -value: [0.6 0.56666667 0.6440678 0.63333333 0.60655738 0.62295082 - 0.59677419 0.59016393 0.61666667 0.56451613] - -mean value: 0.6041696917005023 - -key: TN -value: 229 - -mean value: 229.0 - -key: FP -value: 39 - -mean value: 39.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 25 - -mean value: 25.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.87 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.70284939 0.83141327 0.7548821 0.96309876 0.73414922 0.70942235 - 0.77504945 0.77884579 0.69634461 0.74346662] - -mean value: 0.7689521551132202 - -key: score_time -value: [0.01313448 0.01442432 0.01456642 0.01466513 0.01308274 0.01448059 - 0.01453996 0.01436615 0.01447749 0.01468396] - -mean value: 0.014242124557495118 - -key: test_mcc -value: [0.90632697 0.62732919 0.34151451 0.81064348 0.79166667 0.58333333 - 0.66666667 0.79166667 0.79166667 0.67082039] - -mean value: 0.6981634545719541 - -key: train_mcc -value: [1. 1. 1. 0.989012 1. 1. - 0.97854339 1. 1. 1. ] - -mean value: 0.996755538901208 - -key: test_fscore -value: [0.92307692 0.71428571 0.4 0.83333333 0.83333333 0.66666667 - 0.66666667 0.83333333 0.83333333 0.72727273] - -mean value: 0.7431302031302032 - -key: train_fscore -value: [1. 1. 1. 0.99130435 1. 1. - 0.98305085 1. 1. 1. ] - -mean value: 0.9974355195283714 - -key: test_precision -value: [1. 0.71428571 0.66666667 1. 0.83333333 0.66666667 - 1. 0.83333333 0.83333333 0.8 ] - -mean value: 0.8347619047619048 - -key: train_precision -value: [1. 1. 1. 0.98275862 1. 1. - 0.96666667 1. 1. 1. ] - -mean value: 0.9949425287356322 - -key: test_recall -value: [0.85714286 0.71428571 0.28571429 0.71428571 0.83333333 0.66666667 - 0.5 0.83333333 0.83333333 0.66666667] - -mean value: 0.6904761904761905 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.96666667 0.86666667 0.8 0.93333333 0.93333333 0.86666667 - 0.9 0.93333333 0.93333333 0.9 ] - -mean value: 0.9033333333333335 - -key: train_accuracy -value: [1. 1. 1. 0.9962963 1. 1. - 0.99259259 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [0.92857143 0.8136646 0.62111801 0.85714286 0.89583333 0.79166667 - 0.75 0.89583333 0.89583333 0.8125 ] - -mean value: 0.8262163561076605 - -key: train_roc_auc -value: [1. 1. 1. 0.99765258 1. 1. - 0.99528302 1. 1. 1. ] - -mean value: 0.9992935601027548 - -key: test_jcc -value: [0.85714286 0.55555556 0.25 0.71428571 0.71428571 0.5 - 0.5 0.71428571 0.71428571 0.57142857] - -mean value: 0.6091269841269841 - -key: train_jcc -value: [1. 1. 1. 0.98275862 1. 1. - 0.96666667 1. 1. 1. ] - -mean value: 0.9949425287356322 - -key: TN -value: 227 - -mean value: 227.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 3 -Model_name: Gaussian NB -Model func: GaussianNB() - -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)) -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01280761 0.0125072 0.00945711 0.01048613 0.00934339 0.00971079 - 0.00998425 0.00970817 0.00902152 0.00909138] - -mean value: 0.010211753845214843 - -key: score_time -value: [0.01178479 0.01134276 0.00900006 0.00981688 0.00934839 0.00933266 - 0.00949502 0.00865149 0.008708 0.00873661] - -mean value: 0.009621667861938476 - -key: test_mcc -value: [ 0.8136646 0.3539192 0.44099379 -0.15478181 0.16666667 0.31524416 - 0.2236068 0.1767767 0.1767767 0.07537784] - -mean value: 0.25882446262811765 - -key: train_mcc -value: [0.56619289 0.58730807 0.53804925 0.34313622 0.5896613 0.62833238 - 0.60422007 0.59087082 0.56908335 0.54107098] - -mean value: 0.5557925334413916 - -key: test_fscore -value: [0.85714286 0.52631579 0.57142857 0.28571429 0.33333333 0.46153846 - 0.36363636 0.375 0.375 0.28571429] - -mean value: 0.4434823947981842 - -key: train_fscore -value: [0.6618705 0.67605634 0.62135922 0.47413793 0.68085106 0.71014493 - 0.68965517 0.68055556 0.66666667 0.6375 ] - -mean value: 0.649879738196278 - -key: test_precision -value: [0.85714286 0.41666667 0.57142857 0.19047619 0.33333333 0.42857143 - 0.4 0.3 0.3 0.25 ] - -mean value: 0.40476190476190477 - -key: train_precision -value: [0.56097561 0.56470588 0.69565217 0.31428571 0.57831325 0.6125 - 0.57471264 0.56976744 0.575 0.5 ] - -mean value: 0.5545912718858471 - -key: test_recall -value: [0.85714286 0.71428571 0.57142857 0.57142857 0.33333333 0.5 - 0.33333333 0.5 0.5 0.33333333] - -mean value: 0.5214285714285715 - -key: train_recall -value: [0.80701754 0.84210526 0.56140351 0.96491228 0.82758621 0.84482759 - 0.86206897 0.84482759 0.79310345 0.87931034] - -mean value: 0.8227162734422262 - -key: test_accuracy -value: [0.93333333 0.7 0.8 0.33333333 0.73333333 0.76666667 - 0.76666667 0.66666667 0.66666667 0.66666667] - -mean value: 0.7033333333333334 - -key: train_accuracy -value: [0.82592593 0.82962963 0.85555556 0.54814815 0.83333333 0.85185185 - 0.83333333 0.82962963 0.82962963 0.78518519] - -mean value: 0.8022222222222222 - -key: test_roc_auc -value: [0.9068323 0.70496894 0.72049689 0.41614907 0.58333333 0.66666667 - 0.60416667 0.60416667 0.60416667 0.54166667] - -mean value: 0.6352613871635612 - -key: train_roc_auc -value: [0.81900173 0.83419817 0.7478379 0.700766 0.83124593 0.84930059 - 0.84377033 0.83514964 0.81636304 0.81937215] - -mean value: 0.8097005496894892 - -key: test_jcc -value: [0.75 0.35714286 0.4 0.16666667 0.2 0.3 - 0.22222222 0.23076923 0.23076923 0.16666667] - -mean value: 0.3024236874236874 - -key: train_jcc -value: [0.49462366 0.5106383 0.45070423 0.31073446 0.51612903 0.5505618 - 0.52631579 0.51578947 0.5 0.46788991] - -mean value: 0.48433866438409173 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 59 - -mean value: 59.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.3 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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 0.01021528 0.01006198 0.00915194 0.01011467 0.01024914 - 0.01006627 0.01004815 0.00904727 0.01015615] - -mean value: 0.009832644462585449 - -key: score_time -value: [0.00920868 0.00943947 0.00940347 0.00901794 0.00946045 0.00941467 - 0.00939584 0.00885057 0.00862551 0.00935102] - -mean value: 0.009216761589050293 - -key: test_mcc -value: [ 0. 0.17622684 -0.14744196 0.07881104 0.38888889 0.16666667 - -0.13363062 -0.09284767 0. -0.13363062] - -mean value: 0.030304257323617856 - -key: train_mcc -value: [0.26184314 0.26021557 0.28952646 0.24250501 0.25544197 0.33070969 - 0.26577992 0.32080672 0.27236552 0.22804921] - -mean value: 0.2727243221543163 - -key: test_fscore -value: [0. 0.33333333 0. 0.2 0.44444444 0.33333333 - 0. 0. 0.18181818 0. ] - -mean value: 0.14929292929292928 - -key: train_fscore -value: [0.3373494 0.34883721 0.37209302 0.36170213 0.34482759 0.38554217 - 0.34883721 0.40449438 0.38297872 0.2962963 ] - -mean value: 0.358295812371502 - -key: test_precision -value: [0. 0.4 0. 0.33333333 0.66666667 0.33333333 - 0. 0. 0.2 0. ] - -mean value: 0.19333333333333333 - -key: train_precision -value: [0.53846154 0.51724138 0.55172414 0.45945946 0.51724138 0.64 - 0.53571429 0.58064516 0.5 0.52173913] - -mean value: 0.5362226471912113 - -key: test_recall -value: [0. 0.28571429 0. 0.14285714 0.33333333 0.33333333 - 0. 0. 0.16666667 0. ] - -mean value: 0.1261904761904762 - -key: train_recall -value: [0.24561404 0.26315789 0.28070175 0.29824561 0.25862069 0.27586207 - 0.25862069 0.31034483 0.31034483 0.20689655] - -mean value: 0.2708408953418028 - -key: test_accuracy -value: [0.76666667 0.73333333 0.7 0.73333333 0.83333333 0.73333333 - 0.73333333 0.76666667 0.7 0.73333333] - -mean value: 0.7433333333333334 - -key: train_accuracy -value: [0.7962963 0.79259259 0.8 0.77777778 0.78888889 0.81111111 - 0.79259259 0.8037037 0.78518519 0.78888889] - -mean value: 0.7937037037037038 - -key: test_roc_auc -value: [0.5 0.57763975 0.45652174 0.52795031 0.64583333 0.58333333 - 0.45833333 0.47916667 0.5 0.45833333] - -mean value: 0.5187111801242236 - -key: train_roc_auc -value: [0.594638 0.5987151 0.60983445 0.60217445 0.59629148 0.61670462 - 0.59864997 0.62451204 0.61271958 0.57750488] - -mean value: 0.6031744559975911 - -key: test_jcc -value: [0. 0.2 0. 0.11111111 0.28571429 0.2 - 0. 0. 0.1 0. ] - -mean value: 0.08968253968253968 - -key: train_jcc -value: [0.20289855 0.21126761 0.22857143 0.22077922 0.20833333 0.23880597 - 0.21126761 0.25352113 0.23684211 0.17391304] - -mean value: 0.21861999903274615 - -key: TN -value: 215 - -mean value: 215.0 - -key: FP -value: 56 - -mean value: 56.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 8 - -mean value: 8.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 -/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)) -/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)) -/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)) -/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)) -/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)) - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.7 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00965452 0.00992346 0.00971246 0.00962901 0.00954032 0.00851202 - 0.00849891 0.00883889 0.00885677 0.00982833] - -mean value: 0.009299468994140626 - -key: score_time -value: [0.05858684 0.01619411 0.01401067 0.01489687 0.01341724 0.01285982 - 0.01148534 0.01255536 0.01079416 0.01213264] - -mean value: 0.017693305015563966 - -key: test_mcc -value: [ 0. -0.14744196 0. -0.1024439 -0.09284767 -0.13363062 - 0. 0.37139068 -0.13363062 -0.16666667] - -mean value: -0.040527075735360685 - -key: train_mcc -value: [0.32926729 0.25784885 0.29298224 0.29298224 0.28886635 0.32507645 - 0.25067149 0.36857928 0.34558167 0.3531848 ] - -mean value: 0.3105040658430679 - -key: test_fscore -value: [0. 0. 0. 0. 0. 0. - 0. 0.28571429 0. 0. ] - -mean value: 0.02857142857142857 - -key: train_fscore -value: [0.30985915 0.21212121 0.30136986 0.30136986 0.2972973 0.30555556 - 0.27027027 0.38461538 0.32876712 0.37974684] - -mean value: 0.30909725595474036 - -key: test_precision -value: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] - -mean value: 0.1 - -key: train_precision -value: [0.78571429 0.77777778 0.6875 0.6875 0.6875 0.78571429 - 0.625 0.75 0.8 0.71428571] - -mean value: 0.7300992063492063 - -key: test_recall -value: [0. 0. 0. 0. 0. 0. - 0. 0.16666667 0. 0. ] - -mean value: 0.016666666666666666 - -key: train_recall -value: [0.19298246 0.12280702 0.19298246 0.19298246 0.18965517 0.18965517 - 0.17241379 0.25862069 0.20689655 0.25862069] - -mean value: 0.19776164549304293 - -key: test_accuracy -value: [0.76666667 0.7 0.76666667 0.73333333 0.76666667 0.73333333 - 0.8 0.83333333 0.73333333 0.7 ] - -mean value: 0.7533333333333333 - -key: train_accuracy -value: [0.81851852 0.80740741 0.81111111 0.81111111 0.80740741 0.81481481 - 0.8 0.82222222 0.81851852 0.81851852] - -mean value: 0.812962962962963 - -key: test_roc_auc -value: [0.5 0.45652174 0.5 0.47826087 0.47916667 0.45833333 - 0.5 0.58333333 0.45833333 0.4375 ] - -mean value: 0.48514492753623195 - -key: train_roc_auc -value: [0.58944897 0.55670867 0.58475414 0.58475414 0.58303513 0.58775211 - 0.57205595 0.61751789 0.5963728 0.6151594 ] - -mean value: 0.5887559224010523 - -key: test_jcc -value: [0. 0. 0. 0. 0. 0. - 0. 0.16666667 0. 0. ] - -mean value: 0.016666666666666666 - -key: train_jcc -value: [0.18333333 0.11864407 0.17741935 0.17741935 0.17460317 0.18032787 - 0.15625 0.23809524 0.19672131 0.234375 ] - -mean value: 0.18371887038336446 - -key: TN -value: 225 - -mean value: 225.0 - -key: FP -value: 63 - -mean value: 63.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 1 - -mean value: 1.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.79 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01497412 0.0135026 0.0124414 0.01308274 0.01273632 0.01267242 - 0.01280069 0.01290011 0.01269555 0.01251149] - -mean value: 0.013031744956970214 - -key: score_time -value: [0.01012492 0.0097959 0.00969124 0.0095942 0.00979686 0.00987744 - 0.00988817 0.00966811 0.00955296 0.0096035 ] - -mean value: 0.009759330749511718 - -key: test_mcc -value: [ 0. 0. 0. 0. 0. 0. - 0. 0. -0.09284767 0. ] - -mean value: -0.009284766908852594 - -key: train_mcc -value: [0.16699366 0.20490727 0.20490727 0.26553052 0.20265575 0.20265575 - 0.20265575 0.20265575 0.23444615 0.26261287] - -mean value: 0.21500207340515193 - -key: test_fscore -value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] - -mean value: 0.0 - -key: train_fscore -value: [0.06779661 0.1 0.1 0.16129032 0.09836066 0.09836066 - 0.09836066 0.09836066 0.12903226 0.15873016] - -mean value: 0.11102919724956313 - -key: test_precision -value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] - -mean value: 0.0 - -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. 0. 0. 0. 0. 0. 0.] - -mean value: 0.0 - -key: train_recall -value: [0.03508772 0.05263158 0.05263158 0.0877193 0.05172414 0.05172414 - 0.05172414 0.05172414 0.06896552 0.0862069 ] - -mean value: 0.059013914095583785 - -key: test_accuracy -value: [0.76666667 0.76666667 0.76666667 0.76666667 0.8 0.8 - 0.8 0.8 0.76666667 0.8 ] - -mean value: 0.7833333333333334 - -key: train_accuracy -value: [0.7962963 0.8 0.8 0.80740741 0.7962963 0.7962963 - 0.7962963 0.7962963 0.8 0.8037037 ] - -mean value: 0.7992592592592593 - -key: test_roc_auc -value: [0.5 0.5 0.5 0.5 0.5 0.5 - 0.5 0.5 0.47916667 0.5 ] - -mean value: 0.4979166666666667 - -key: train_roc_auc -value: [0.51754386 0.52631579 0.52631579 0.54385965 0.52586207 0.52586207 - 0.52586207 0.52586207 0.53448276 0.54310345] - -mean value: 0.5295069570477919 - -key: test_jcc -value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] - -mean value: 0.0 - -key: train_jcc -value: [0.03508772 0.05263158 0.05263158 0.0877193 0.05172414 0.05172414 - 0.05172414 0.05172414 0.06896552 0.0862069 ] - -mean value: 0.059013914095583785 - -key: TN -value: 235 - -mean value: 235.0 - -key: FP -value: 64 - -mean value: 64.0 - -key: FN -value: 1 - -mean value: 1.0 - -key: TP -value: 0 - -mean value: 0.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.79 - -Running classifier: 7 -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/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.28935218 1.11154175 1.17469454 0.87591124 1.31645083 1.11882138 - 1.28768206 1.17197323 1.22777867 1.14895582] - -mean value: 1.1723161697387696 - -key: score_time -value: [0.01478457 0.01461887 0.01219559 0.01212907 0.0145092 0.01444316 - 0.01373696 0.01357079 0.01454949 0.01462388] - -mean value: 0.01391615867614746 - -key: test_mcc -value: [0.48445214 0.44099379 0.51227176 0.34151451 0.66666667 0.20044593 - 0.53452248 0.4472136 0.51227176 0.53931937] - -mean value: 0.4679672017008586 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. - 0.989012 1. ] - -mean value: 0.9989011997122217 - -key: test_fscore -value: [0.44444444 0.57142857 0.61538462 0.4 0.66666667 0.25 - 0.5 0.54545455 0.61538462 0.6 ] - -mean value: 0.5208763458763458 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.99130435 1. ] - -mean value: 0.9991304347826088 - -key: test_precision -value: [1. 0.57142857 0.66666667 0.66666667 1. 0.5 - 1. 0.6 0.57142857 0.75 ] - -mean value: 0.7326190476190476 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.28571429 0.57142857 0.57142857 0.28571429 0.5 0.16666667 - 0.33333333 0.5 0.66666667 0.5 ] - -mean value: 0.43809523809523815 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.98275862 1. ] - -mean value: 0.9982758620689655 - -key: test_accuracy -value: [0.83333333 0.8 0.83333333 0.8 0.9 0.8 - 0.86666667 0.83333333 0.83333333 0.86666667] - -mean value: 0.8366666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. - 1. 0.9962963 1. ] - -mean value: 0.9996296296296296 - -key: test_roc_auc -value: [0.64285714 0.72049689 0.74223602 0.62111801 0.75 0.5625 - 0.66666667 0.70833333 0.77083333 0.72916667] - -mean value: 0.6914208074534162 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.99137931 1. ] - -mean value: 0.9991379310344828 - -key: test_jcc -value: [0.28571429 0.4 0.44444444 0.25 0.5 0.14285714 - 0.33333333 0.375 0.44444444 0.42857143] - -mean value: 0.3604365079365079 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.98275862 1. ] - -mean value: 0.9982758620689655 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.62 - -Accuracy on Blind test: 0.88 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02025032 0.01830864 0.01398182 0.01478696 0.01364207 0.01290917 - 0.01366997 0.01493931 0.01325536 0.01472449] - -mean value: 0.015046811103820801 - -key: score_time -value: [0.01177716 0.00908613 0.00855756 0.00851798 0.00853229 0.00851965 - 0.00853705 0.0085578 0.00852776 0.00925159] - -mean value: 0.008986496925354004 - -key: test_mcc -value: [1. 0.84270097 0.81064348 0.90632697 0.89442719 0.70929937 - 0.66666667 0.8291562 0.79166667 0.79166667] - -mean value: 0.8242554176357084 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.875 0.83333333 0.92307692 0.90909091 0.76923077 - 0.66666667 0.85714286 0.83333333 0.83333333] - -mean value: 0.8500208125208125 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.77777778 1. 1. 1. 0.71428571 - 1. 0.75 0.83333333 0.83333333] - -mean value: 0.890873015873016 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.71428571 0.85714286 0.83333333 0.83333333 - 0.5 1. 0.83333333 0.83333333] - -mean value: 0.8404761904761905 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.93333333 0.93333333 0.96666667 0.96666667 0.9 - 0.9 0.93333333 0.93333333 0.93333333] - -mean value: 0.9400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95652174 0.85714286 0.92857143 0.91666667 0.875 - 0.75 0.95833333 0.89583333 0.89583333] - -mean value: 0.9033902691511388 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.77777778 0.71428571 0.85714286 0.83333333 0.625 - 0.5 0.75 0.71428571 0.71428571] - -mean value: 0.7486111111111111 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 54 - -mean value: 54.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.93 - -Running classifier: 9 -Model_name: Extra Trees -Model func: ExtraTreesClassifier(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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.10482979 0.10370564 0.10213518 0.10077429 0.10230589 0.10169768 - 0.10185742 0.10176086 0.10234618 0.10370636] - -mean value: 0.10251193046569824 - -key: score_time -value: [0.01763678 0.01745105 0.01738286 0.01766253 0.01771617 0.01758122 - 0.01727009 0.01756859 0.01742911 0.01736689] - -mean value: 0.0175065279006958 - -key: test_mcc -value: [ 0.33660139 0.47913962 0.38769906 0. 0. -0.13363062 - -0.09284767 0.37139068 0.11111111 0.11111111] - -mean value: 0.1570574668631064 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.25 0.54545455 0.5 0. 0. 0. - 0. 0.28571429 0.22222222 0.22222222] - -mean value: 0.20256132756132755 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.75 0.6 0. 0. 0. - 0. 1. 0.33333333 0.33333333] - -mean value: 0.40166666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.14285714 0.42857143 0.42857143 0. 0. 0. - 0. 0.16666667 0.16666667 0.16666667] - -mean value: 0.15000000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.83333333 0.8 0.76666667 0.8 0.73333333 - 0.76666667 0.83333333 0.76666667 0.76666667] - -mean value: 0.7866666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.57142857 0.69254658 0.67080745 0.5 0.5 0.45833333 - 0.47916667 0.58333333 0.54166667 0.54166667] - -mean value: 0.5538949275362319 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.14285714 0.375 0.33333333 0. 0. 0. - 0. 0.16666667 0.125 0.125 ] - -mean value: 0.12678571428571428 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 54 - -mean value: 54.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 10 - -mean value: 10.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.45 - -Accuracy on Blind test: 0.84 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01043725 0.01003408 0.01018763 0.01028419 0.00942492 0.01007652 - 0.00999904 0.01002502 0.01008296 0.00922561] - -mean value: 0.00997772216796875 - -key: score_time -value: [0.00968361 0.00917816 0.00944519 0.00942183 0.00889969 0.00936246 - 0.00931597 0.0093205 0.00927258 0.00865602] - -mean value: 0.009255599975585938 - -key: test_mcc -value: [-0.24671758 0.25465839 0.31524416 -0.27583864 -0.11306675 0. - 0.35355339 0.51227176 0.31524416 0.4472136 ] - -mean value: 0.15625624819351644 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0. 0.42857143 0.46153846 0. 0.14285714 0.18181818 - 0.5 0.61538462 0.46153846 0.54545455] - -mean value: 0.33371628371628376 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0. 0.42857143 0.5 0. 0.125 0.2 - 0.4 0.57142857 0.42857143 0.6 ] - -mean value: 0.32535714285714284 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0. 0.42857143 0.42857143 0. 0.16666667 0.16666667 - 0.66666667 0.66666667 0.5 0.5 ] - -mean value: 0.35238095238095235 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6 0.73333333 0.76666667 0.56666667 0.6 0.7 - 0.73333333 0.83333333 0.76666667 0.83333333] - -mean value: 0.7133333333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.39130435 0.62732919 0.64906832 0.36956522 0.4375 0.5 - 0.70833333 0.77083333 0.66666667 0.70833333] - -mean value: 0.5828933747412008 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0. 0.27272727 0.3 0. 0.07692308 0.1 - 0.33333333 0.44444444 0.3 0.375 ] - -mean value: 0.22024281274281274 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 192 - -mean value: 192.0 - -key: FP -value: 42 - -mean value: 42.0 - -key: FN -value: 44 - -mean value: 44.0 - -key: TP -value: 22 - -mean value: 22.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.75 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.42193198 1.48464036 1.5280869 1.39486265 1.38504148 1.38991213 - 1.4028635 1.4091928 1.39909554 1.406986 ]/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - - -mean value: 1.4222613334655763 - -key: score_time -value: [0.09638333 0.09634948 0.0968647 0.09420729 0.08841085 0.09504485 - 0.0951705 0.09646654 0.09620643 0.09695005] - -mean value: 0.09520540237426758 - -key: test_mcc -value: [0.60421798 0.51227176 0.60421798 0.48445214 0.53452248 0.38888889 - 0.66666667 0.53931937 0.67082039 0.67082039] - -mean value: 0.5676198059450792 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.6 0.61538462 0.6 0.44444444 0.5 0.44444444 - 0.66666667 0.6 0.72727273 0.72727273] - -mean value: 0.5925485625485626 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.66666667 1. 1. 1. 0.66666667 - 1. 0.75 0.8 0.8 ] - -mean value: 0.8683333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.42857143 0.57142857 0.42857143 0.28571429 0.33333333 0.33333333 - 0.5 0.5 0.66666667 0.66666667] - -mean value: 0.4714285714285714 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.86666667 0.83333333 0.86666667 0.83333333 0.86666667 0.83333333 - 0.9 0.86666667 0.9 0.9 ] - -mean value: 0.8666666666666668 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.71428571 0.74223602 0.71428571 0.64285714 0.66666667 0.64583333 - 0.75 0.72916667 0.8125 0.8125 ] - -mean value: 0.723033126293996 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.42857143 0.44444444 0.42857143 0.28571429 0.33333333 0.28571429 - 0.5 0.42857143 0.57142857 0.57142857] - -mean value: 0.42777777777777776 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 34 - -mean value: 34.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 30 - -mean value: 30.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.91 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.86156774 0.85981154 0.84925723 0.92530513 0.88639402 0.90727282 - 0.91255879 0.89210582 0.88635182 0.97684216] - -mean value: 0.8957467079162598 - -key: score_time -value: [0.18339157 0.14685249 0.18079305 0.21409535 0.17683268 0.17423201 - 0.16365194 0.18895435 0.19963431 0.14843202] - -mean value: 0.1776869773864746 - -key: test_mcc -value: [0. 0.60421798 0.60421798 0.33660139 0.37139068 0.37139068 - 0.37139068 0.38888889 0.20044593 0.37139068] - -mean value: 0.3619934867309984 - -key: train_mcc -value: [0.8760064 0.91023656 0.86450473 0.86352508 0.88822308 0.92214351 - 0.87770876 0.86638084 0.90023488 0.83209945] - -mean value: 0.8801063277277368 - -key: test_fscore -value: [0. 0.6 0.6 0.25 0.28571429 0.28571429 - 0.28571429 0.44444444 0.25 0.28571429] - -mean value: 0.3287301587301587 - -key: train_fscore -value: [0.89320388 0.9245283 0.88235294 0.88461538 0.90740741 0.93693694 - 0.8952381 0.88461538 0.91588785 0.85148515] - -mean value: 0.8976271334353759 - -key: test_precision -value: [0. 1. 1. 1. 1. 1. - 1. 0.66666667 0.5 1. ] - -mean value: 0.8166666666666667 - -key: train_precision -value: [1. 1. 1. 0.9787234 0.98 0.98113208 - 1. 1. 1. 1. ] - -mean value: 0.9939855479727017 - -key: test_recall -value: [0. 0.42857143 0.42857143 0.14285714 0.16666667 0.16666667 - 0.16666667 0.33333333 0.16666667 0.16666667] - -mean value: 0.21666666666666665 - -key: train_recall -value: [0.80701754 0.85964912 0.78947368 0.80701754 0.84482759 0.89655172 - 0.81034483 0.79310345 0.84482759 0.74137931] - -mean value: 0.8194192377495464 - -key: test_accuracy -value: [0.76666667 0.86666667 0.86666667 0.8 0.83333333 0.83333333 - 0.83333333 0.83333333 0.8 0.83333333] - -mean value: 0.8266666666666665 - -key: train_accuracy -value: [0.95925926 0.97037037 0.95555556 0.95555556 0.96296296 0.97407407 - 0.95925926 0.95555556 0.96666667 0.94444444] - -mean value: 0.9603703703703704 - -key: test_roc_auc -value: [0.5 0.71428571 0.71428571 0.57142857 0.58333333 0.58333333 - 0.58333333 0.64583333 0.5625 0.58333333] - -mean value: 0.6041666666666666 - -key: train_roc_auc -value: [0.90350877 0.92982456 0.89473684 0.90116135 0.9200553 0.94591737 - 0.90517241 0.89655172 0.92241379 0.87068966] - -mean value: 0.9090031789775281 - -key: test_jcc -value: [0. 0.42857143 0.42857143 0.14285714 0.16666667 0.16666667 - 0.16666667 0.28571429 0.14285714 0.16666667] - -mean value: 0.2095238095238095 - -key: train_jcc -value: [0.80701754 0.85964912 0.78947368 0.79310345 0.83050847 0.88135593 - 0.81034483 0.79310345 0.84482759 0.74137931] - -mean value: 0.8150763378346509 - -key: TN -value: 234 - -mean value: 234.0 - -key: FP -value: 50 - -mean value: 50.0 - -key: FN -value: 2 - -mean value: 2.0 - -key: TP -value: 14 - -mean value: 14.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.85 - -Running classifier: 13 -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=None, 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/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 -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.10199094 0.05092549 0.05280399 0.05130363 0.05411649 0.05062795 - 0.05282617 0.08380771 0.21290278 0.04891729] - -mean value: 0.07602224349975586 - -key: score_time -value: [0.01059747 0.01039028 0.01032424 0.01030087 0.01046681 0.01035547 - 0.01033735 0.01081228 0.01085591 0.01062322] - -mean value: 0.010506391525268555 - -key: test_mcc -value: [1. 0.91485328 1. 1. 1. 0.70929937 - 0.66666667 0.90632697 0.79166667 1. ] - -mean value: 0.8988812944927872 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.93333333 1. 1. 1. 0.76923077 - 0.66666667 0.92307692 0.83333333 1. ] - -mean value: 0.9125641025641025 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.875 1. 1. 1. 0.71428571 - 1. 0.85714286 0.83333333 1. ] - -mean value: 0.9279761904761905 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.83333333 - 0.5 1. 0.83333333 1. ] - -mean value: 0.9166666666666667 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.96666667 1. 1. 1. 0.9 - 0.9 0.96666667 0.93333333 1. ] - -mean value: 0.9666666666666666 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97826087 1. 1. 1. 0.875 - 0.75 0.97916667 0.89583333 1. ] - -mean value: 0.9478260869565218 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.875 1. 1. 1. 0.625 - 0.5 0.85714286 0.71428571 1. ] - -mean value: 0.8571428571428573 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 231 - -mean value: 231.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03237653 0.06756616 0.05314088 0.05011415 0.05858469 0.06775379 - 0.06915903 0.06523108 0.05823874 0.06052685] - -mean value: 0.058269190788269046 - -key: score_time -value: [0.02251053 0.02317214 0.0225575 0.01193118 0.0209291 0.02144742 - 0.02248287 0.02148151 0.0118711 0.02320576] - -mean value: 0.020158910751342775 - -key: test_mcc -value: [0.59917127 0.44099379 0.59917127 0.07881104 0.58333333 0.58333333 - 0.67082039 0.76376262 0.53033009 0.4472136 ] - -mean value: 0.5296940727221642 - -key: train_mcc -value: [0.93435553 0.95631739 0.94596128 0.95753763 0.93515969 0.93412492 - 0.95685154 0.95685154 0.95685154 0.94661124] - -mean value: 0.9480622310357543 - -key: test_fscore -value: [0.66666667 0.57142857 0.66666667 0.2 0.66666667 0.66666667 - 0.72727273 0.8 0.625 0.54545455] - -mean value: 0.6135822510822511 - -key: train_fscore -value: [0.94827586 0.96551724 0.95726496 0.96610169 0.94915254 0.94827586 - 0.96610169 0.96610169 0.96610169 0.95798319] - -mean value: 0.9590876438093409 - -key: test_precision -value: [0.8 0.57142857 0.8 0.33333333 0.66666667 0.66666667 - 0.8 0.66666667 0.5 0.6 ] - -mean value: 0.6404761904761904 - -key: train_precision -value: [0.93220339 0.94915254 0.93333333 0.93442623 0.93333333 0.94827586 - 0.95 0.95 0.95 0.93442623] - -mean value: 0.9415150919955415 - -key: test_recall -value: [0.57142857 0.57142857 0.57142857 0.14285714 0.66666667 0.66666667 - 0.66666667 1. 0.83333333 0.5 ] - -mean value: 0.619047619047619 - -key: train_recall -value: [0.96491228 0.98245614 0.98245614 1. 0.96551724 0.94827586 - 0.98275862 0.98275862 0.98275862 0.98275862] - -mean value: 0.9774652147610405 - -key: test_accuracy -value: [0.86666667 0.8 0.86666667 0.73333333 0.86666667 0.86666667 - 0.9 0.9 0.8 0.83333333] - -mean value: 0.8433333333333334 - -key: train_accuracy -value: [0.97777778 0.98518519 0.98148148 0.98518519 0.97777778 0.97777778 - 0.98518519 0.98518519 0.98518519 0.98148148] - -mean value: 0.9822222222222221 - -key: test_roc_auc -value: [0.76397516 0.72049689 0.76397516 0.52795031 0.79166667 0.79166667 - 0.8125 0.9375 0.8125 0.70833333] - -mean value: 0.7630564182194617 - -key: train_roc_auc -value: [0.97306647 0.98418582 0.9818384 0.99061033 0.97332466 0.96706246 - 0.98430384 0.98430384 0.98430384 0.98194535] - -mean value: 0.9804944994878813 - -key: test_jcc -value: [0.5 0.4 0.5 0.11111111 0.5 0.5 - 0.57142857 0.66666667 0.45454545 0.375 ] - -mean value: 0.4578751803751803 - -key: train_jcc -value: [0.90163934 0.93333333 0.91803279 0.93442623 0.90322581 0.90163934 - 0.93442623 0.93442623 0.93442623 0.91935484] - -mean value: 0.9214930371937248 - -key: TN -value: 214 - -mean value: 214.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 39 - -mean value: 39.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.62 - -Accuracy on Blind test: 0.87 - -Running classifier: 15 -Model_name: Multinomial -Model func: MultinomialNB() - -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)) -/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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02837014 0.01165652 0.00915122 0.00917482 0.00916696 0.00895834 - 0.00898743 0.00897956 0.00888681 0.00905609] - -mean value: 0.011238789558410645 - -key: score_time -value: [0.01515555 0.00896788 0.00874496 0.00878382 0.00883079 0.00866342 - 0.00871348 0.0085988 0.00838351 0.00861454] - -mean value: 0.009345674514770507 - -key: test_mcc -value: [0. 0.16850509 0.07881104 0. 0. 0. - 0. 0. 0.20044593 0.37139068] - -mean value: 0.0819152741160786 - -key: train_mcc -value: [0.25784885 0.18040749 0.39237451 0.29029468 0.28103906 0.25454388 - 0.1745244 0.16586378 0.25714998 0.25454388] - -mean value: 0.2508590529267264 - -key: test_fscore -value: [0. 0.22222222 0.2 0. 0. 0. - 0. 0. 0.25 0.28571429] - -mean value: 0.09579365079365079 - -key: train_fscore -value: [0.21212121 0.2 0.34285714 0.33333333 0.25714286 0.20895522 - 0.15151515 0.125 0.23188406 0.20895522] - -mean value: 0.22717642027019055 - -key: test_precision -value: [0. 0.5 0.33333333 0. 0. 0. - 0. 0. 0.5 1. ] - -mean value: 0.2333333333333333 - -key: train_precision -value: [0.77777778 0.53846154 0.92307692 0.61904762 0.75 0.77777778 - 0.625 0.66666667 0.72727273 0.77777778] - -mean value: 0.7182858807858807 - -key: test_recall -value: [0. 0.14285714 0.14285714 0. 0. 0. - 0. 0. 0.16666667 0.16666667] - -mean value: 0.06190476190476189 - -key: train_recall -value: [0.12280702 0.12280702 0.21052632 0.22807018 0.15517241 0.12068966 - 0.0862069 0.06896552 0.13793103 0.12068966] - -mean value: 0.13738656987295822 - -key: test_accuracy -value: [0.76666667 0.76666667 0.73333333 0.76666667 0.8 0.8 - 0.8 0.8 0.8 0.83333333] - -mean value: 0.7866666666666666 - -key: train_accuracy -value: [0.80740741 0.79259259 0.82962963 0.80740741 0.80740741 0.8037037 - 0.79259259 0.79259259 0.8037037 0.8037037 ] - -mean value: 0.8040740740740742 - -key: test_roc_auc -value: [0.5 0.54968944 0.52795031 0.5 0.5 0.5 - 0.5 0.5 0.5625 0.58333333] - -mean value: 0.5223473084886128 - -key: train_roc_auc -value: [0.55670867 0.547319 0.60291574 0.59525574 0.57051074 0.55562785 - 0.53602798 0.52976578 0.56189005 0.55562785] - -mean value: 0.561164938758784 - -key: test_jcc -value: [0. 0.125 0.11111111 0. 0. 0. - 0. 0. 0.14285714 0.16666667] - -mean value: 0.05456349206349206 - -key: train_jcc -value: [0.11864407 0.11111111 0.20689655 0.2 0.14754098 0.11666667 - 0.08196721 0.06666667 0.13114754 0.11666667] - -mean value: 0.12973074683367772 - -key: TN -value: 232 - -mean value: 232.0 - -key: FP -value: 60 - -mean value: 60.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 4 - -mean value: 4.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.78 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01243377 0.01764393 0.01851797 0.01582813 0.02090073 0.01800299 - 0.0202446 0.02028775 0.019696 0.01619339] - -mean value: 0.01797492504119873 - -key: score_time -value: [0.00890422 0.01080108 0.01113772 0.01139307 0.01157093 0.01157641 - 0.01155877 0.01151228 0.01154518 0.0116353 ] - -mean value: 0.01116349697113037 - -key: test_mcc -value: [0.33660139 0.55841694 0.47913962 0.34151451 0.78446454 0.79166667 - 0.66666667 0.79166667 0.58333333 0.53452248] - -mean value: 0.5867992806326103 - -key: train_mcc -value: [0.60379629 0.94410289 0.95753763 0.86329914 0.95608328 0.93412492 - 0.96817595 0.97854339 0.933743 0.80660901] - -mean value: 0.894601550428449 - -key: test_fscore -value: [0.25 0.66666667 0.54545455 0.4 0.8 0.83333333 - 0.66666667 0.83333333 0.66666667 0.5 ] - -mean value: 0.6162121212121212 - -key: train_fscore -value: [0.59259259 0.95575221 0.96610169 0.88888889 0.96551724 0.94827586 - 0.97478992 0.98305085 0.94545455 0.83495146] - -mean value: 0.9055375257423632 - -key: test_precision -value: [1. 0.625 0.75 0.66666667 1. 0.83333333 - 1. 0.83333333 0.66666667 1. ] - -mean value: 0.8375 - -key: train_precision -value: [1. 0.96428571 0.93442623 0.94117647 0.96551724 0.94827586 - 0.95081967 0.96666667 1. 0.95555556] - -mean value: 0.9626723412183793 - -key: test_recall -value: [0.14285714 0.71428571 0.42857143 0.28571429 0.66666667 0.83333333 - 0.5 0.83333333 0.66666667 0.33333333] - -mean value: 0.5404761904761906 - -key: train_recall -value: [0.42105263 0.94736842 1. 0.84210526 0.96551724 0.94827586 - 1. 1. 0.89655172 0.74137931] - -mean value: 0.8762250453720508 - -key: test_accuracy -value: [0.8 0.83333333 0.83333333 0.8 0.93333333 0.93333333 - 0.9 0.93333333 0.86666667 0.86666667] - -mean value: 0.8700000000000001 - -key: train_accuracy -value: [0.87777778 0.98148148 0.98518519 0.95555556 0.98518519 0.97777778 - 0.98888889 0.99259259 0.97777778 0.93703704] - -mean value: 0.9659259259259259 - -key: test_roc_auc -value: [0.57142857 0.79192547 0.69254658 0.62111801 0.83333333 0.89583333 - 0.75 0.89583333 0.79166667 0.66666667] - -mean value: 0.7510351966873706 - -key: train_roc_auc -value: [0.71052632 0.96898937 0.99061033 0.91401038 0.97804164 0.96706246 - 0.99292453 0.99528302 0.94827586 0.86597267] - -mean value: 0.933169657950442 - -key: test_jcc -value: [0.14285714 0.5 0.375 0.25 0.66666667 0.71428571 - 0.5 0.71428571 0.5 0.33333333] - -mean value: 0.46964285714285714 - -key: train_jcc -value: [0.42105263 0.91525424 0.93442623 0.8 0.93333333 0.90163934 - 0.95081967 0.96666667 0.89655172 0.71666667] - -mean value: 0.8436410505573321 - -key: TN -value: 227 - -mean value: 227.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. - _warn_prf(average, modifier, msg_start, len(result)) -blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.8 - -Accuracy on Blind test: 0.93 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01539302 0.01551223 0.01514006 0.0147841 0.01656079 0.01537895 - 0.01675487 0.01508403 0.01645827 0.01462555] - -mean value: 0.015569186210632325 - -key: score_time -value: [0.01136613 0.01145267 0.0115869 0.01156878 0.01143694 0.01207328 - 0.01174545 0.01152802 0.01147032 0.01155066] - -mean value: 0.011577916145324708 - -key: test_mcc -value: [0.71098137 0.51604685 0.34151451 0.34151451 0.53452248 0. - 0.58191437 0.67082039 0.58333333 0.58333333] - -mean value: 0.48639811508822567 - -key: train_mcc -value: [0.93435553 0.65869937 0.79428925 0.88679821 0.46369186 0.41232029 - 0.7215912 0.84179128 0.90023488 0.9034818 ] - -mean value: 0.7517253668919773 - -key: test_fscore -value: [0.72727273 0.60869565 0.4 0.4 0.5 0. - 0.66666667 0.72727273 0.66666667 0.66666667] - -mean value: 0.5363241106719367 - -key: train_fscore -value: [0.94827586 0.7125 0.8125 0.90909091 0.4109589 0.34285714 - 0.76821192 0.87037037 0.91588785 0.92436975] - -mean value: 0.7615022707393229 - -key: test_precision -value: [1. 0.4375 0.66666667 0.66666667 1. 0. - 0.55555556 0.8 0.66666667 0.66666667] - -mean value: 0.6459722222222222 - -key: train_precision -value: [0.93220339 0.55339806 1. 0.94339623 1. 1. - 0.62365591 0.94 1. 0.90163934] - -mean value: 0.889429293273882 - -key: test_recall -value: [0.57142857 1. 0.28571429 0.28571429 0.33333333 0. - 0.83333333 0.66666667 0.66666667 0.66666667] - -mean value: 0.5309523809523811 - -key: train_recall -value: [0.96491228 1. 0.68421053 0.87719298 0.25862069 0.20689655 - 1. 0.81034483 0.84482759 0.94827586] - -mean value: 0.7595281306715063 - -key: test_accuracy -value: [0.9 0.7 0.8 0.8 0.86666667 0.8 - 0.83333333 0.9 0.86666667 0.86666667] - -mean value: 0.8333333333333334 - -key: train_accuracy -value: [0.97777778 0.82962963 0.93333333 0.96296296 0.84074074 0.82962963 - 0.87037037 0.94814815 0.96666667 0.96666667] - -mean value: 0.9125925925925926 - -key: test_roc_auc -value: [0.78571429 0.80434783 0.62111801 0.62111801 0.66666667 0.5 - 0.83333333 0.8125 0.79166667 0.79166667] - -mean value: 0.7228131469979296 - -key: train_roc_auc -value: [0.97306647 0.89201878 0.84210526 0.93155424 0.62931034 0.60344828 - 0.91745283 0.89809694 0.92241379 0.95998699] - -mean value: 0.8569453922911965 - -key: test_jcc -value: [0.57142857 0.4375 0.25 0.25 0.33333333 0. - 0.5 0.57142857 0.5 0.5 ] - -mean value: 0.3913690476190476 - -key: train_jcc -value: [0.90163934 0.55339806 0.68421053 0.83333333 0.25862069 0.20689655 - 0.62365591 0.7704918 0.84482759 0.859375 ] - -mean value: 0.6536448807007236 - -key: TN -value: 216 - -mean value: 216.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.64 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.14356923 0.12530208 0.12540674 0.12698865 0.12595773 0.12572432 - 0.12548542 0.125597 0.12431383 0.12713146] - -mean value: 0.12754764556884765 - -key: score_time -value: [0.01492476 0.0147512 0.01494718 0.01527262 0.01499748 0.01501465 - 0.014992 0.01485825 0.01556373 0.01499319] - -mean value: 0.01503150463104248 - -key: test_mcc -value: [1. 0.84270097 0.90632697 0.90632697 1. 0.70929937 - 0.78446454 0.90632697 0.79166667 1. ] - -mean value: 0.8847112445959876 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.875 0.92307692 0.92307692 1. 0.76923077 - 0.8 0.92307692 0.83333333 1. ] - -mean value: 0.9046794871794871 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.77777778 1. 1. 1. 0.71428571 - 1. 0.85714286 0.83333333 1. ] - -mean value: 0.9182539682539683 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.85714286 0.85714286 1. 0.83333333 - 0.66666667 1. 0.83333333 1. ] - -mean value: 0.9047619047619048 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.93333333 0.96666667 0.96666667 1. 0.9 - 0.93333333 0.96666667 0.93333333 1. ] - -mean value: 0.96 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95652174 0.92857143 0.92857143 1. 0.875 - 0.83333333 0.97916667 0.89583333 1. ] - -mean value: 0.9396997929606625 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.77777778 0.85714286 0.85714286 1. 0.625 - 0.66666667 0.85714286 0.71428571 1. ] - -mean value: 0.8355158730158732 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, oob_score=True, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03868628 0.03752804 0.03799224 0.03717208 0.03749418 0.03673482 - 0.0372963 0.03851962 0.03842163 0.04086733] - -mean value: 0.03807125091552734 - -key: score_time -value: [0.0216217 0.01853013 0.02497888 0.02284074 0.01729608 0.02156329 - 0.01863456 0.02311659 0.01805592 0.02187967] - -mean value: 0.020851755142211915 - -key: test_mcc -value: [1. 0.91485328 0.90632697 1. 0.66666667 0.70929937 - 0.89442719 0.90632697 0.79166667 1. ] - -mean value: 0.8789567103102754 - -key: train_mcc -value: [0.96691391 0.96648679 0.96648679 0.96648679 0.96691391 0.97800497 - 0.95608328 0.96691391 0.97804164 0.97804164] - -mean value: 0.9690373628729612 - -key: test_fscore -value: [1. 0.93333333 0.92307692 1. 0.66666667 0.76923077 - 0.90909091 0.92307692 0.83333333 1. ] - -mean value: 0.8957808857808857 - -key: train_fscore -value: [0.97391304 0.97345133 0.97345133 0.97345133 0.97391304 0.98245614 - 0.96551724 0.97391304 0.98275862 0.98275862] - -mean value: 0.9755583735845164 - -key: test_precision -value: [1. 0.875 1. 1. 1. 0.71428571 - 1. 0.85714286 0.83333333 1. ] - -mean value: 0.9279761904761905 - -key: train_precision -value: [0.96551724 0.98214286 0.98214286 0.98214286 0.98245614 1. - 0.96551724 0.98245614 0.98275862 0.98275862] - -mean value: 0.9807892576268257 - -key: test_recall -value: [1. 1. 0.85714286 1. 0.5 0.83333333 - 0.83333333 1. 0.83333333 1. ] - -mean value: 0.8857142857142858 - -key: train_recall -value: [0.98245614 0.96491228 0.96491228 0.96491228 0.96551724 0.96551724 - 0.96551724 0.96551724 0.98275862 0.98275862] - -mean value: 0.9704779189352692 - -key: test_accuracy -value: [1. 0.96666667 0.96666667 1. 0.9 0.9 - 0.96666667 0.96666667 0.93333333 1. ] - -mean value: 0.96 - -key: train_accuracy -value: [0.98888889 0.98888889 0.98888889 0.98888889 0.98888889 0.99259259 - 0.98518519 0.98888889 0.99259259 0.99259259] - -mean value: 0.9896296296296295 - -key: test_roc_auc -value: [1. 0.97826087 0.92857143 1. 0.75 0.875 - 0.91666667 0.97916667 0.89583333 1. ] - -mean value: 0.9323498964803314 - -key: train_roc_auc -value: [0.98653323 0.98010872 0.98010872 0.98010872 0.98040013 0.98275862 - 0.97804164 0.98040013 0.98902082 0.98902082] - -mean value: 0.9826501562078243 - -key: test_jcc -value: [1. 0.875 0.85714286 1. 0.5 0.625 - 0.83333333 0.85714286 0.71428571 1. ] - -mean value: 0.8261904761904763 - -key: train_jcc -value: [0.94915254 0.94827586 0.94827586 0.94827586 0.94915254 0.96551724 - 0.93333333 0.94915254 0.96610169 0.96610169] - -mean value: 0.9523339177868693 - -key: TN -value: 231 - -mean value: 231.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.82 - -Accuracy on Blind test: 0.94 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0600543 0.10853124 0.06738281 0.04305983 0.0710392 0.10982752 - 0.06292367 0.03897572 0.03897929 0.08064604] - -mean value: 0.06814196109771728 - -key: score_time -value: [0.02326965 0.02519727 0.01338458 0.02152944 0.02401328 0.03619766 - 0.01291752 0.01300049 0.01302505 0.02103305] - -mean value: 0.02035679817199707 - -key: test_mcc -value: [ 0. 0.07881104 0.16850509 -0.1024439 0. 0.20044593 - -0.09284767 -0.09284767 -0.16666667 -0.16666667] - -mean value: -0.01737105065888995 - -key: train_mcc -value: [0.77032728 0.78234837 0.81794963 0.82967804 0.82056154 0.83209945 - 0.808962 0.7855616 0.808962 0.808962 ] - -mean value: 0.8065411893021752 - -key: test_fscore -value: [0. 0.2 0.22222222 0. 0. 0.25 - 0. 0. 0. 0. ] - -mean value: 0.06722222222222222 - -key: train_fscore -value: [0.78723404 0.8 0.83673469 0.84848485 0.84 0.85148515 - 0.82828283 0.80412371 0.82828283 0.82828283] - -mean value: 0.8252910929619134 - -key: test_precision -value: [0. 0.33333333 0.5 0. 0. 0.5 - 0. 0. 0. 0. ] - -mean value: 0.13333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0. 0.14285714 0.14285714 0. 0. 0.16666667 - 0. 0. 0. 0. ] - -mean value: 0.04523809523809523 - -key: train_recall -value: [0.64912281 0.66666667 0.71929825 0.73684211 0.72413793 0.74137931 - 0.70689655 0.67241379 0.70689655 0.70689655] - -mean value: 0.7030550514216577 - -key: test_accuracy -value: [0.76666667 0.73333333 0.76666667 0.73333333 0.8 0.8 - 0.76666667 0.76666667 0.7 0.7 ] - -mean value: 0.7533333333333334 - -key: train_accuracy -value: [0.92592593 0.92962963 0.94074074 0.94444444 0.94074074 0.94444444 - 0.93703704 0.92962963 0.93703704 0.93703704] - -mean value: 0.9366666666666668 - -key: test_roc_auc -value: [0.5 0.52795031 0.54968944 0.47826087 0.5 0.5625 - 0.47916667 0.47916667 0.4375 0.4375 ] - -mean value: 0.49517339544513456 - -key: train_roc_auc -value: [0.8245614 0.83333333 0.85964912 0.86842105 0.86206897 0.87068966 - 0.85344828 0.8362069 0.85344828 0.85344828] - -mean value: 0.8515275257108289 - -key: test_jcc -value: [0. 0.11111111 0.125 0. 0. 0.14285714 - 0. 0. 0. 0. ] - -mean value: 0.037896825396825394 - -key: train_jcc -value: [0.64912281 0.66666667 0.71929825 0.73684211 0.72413793 0.74137931 - 0.70689655 0.67241379 0.70689655 0.70689655] - -mean value: 0.7030550514216577 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 61 - -mean value: 61.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 3 - -mean value: 3.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -mean value: 31.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.8 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.44956064 0.42286658 0.42664719 0.4276886 0.42576098 0.44159317 - 0.42826962 0.42824292 0.42826033 0.42644954] - -mean value: 0.4305339574813843 - -key: score_time -value: [0.00901651 0.00893927 0.00908875 0.00892687 0.00897551 0.00908875 - 0.00901818 0.00894594 0.00910378 0.00907588] - -mean value: 0.0090179443359375 - -key: test_mcc -value: [1. 0.84270097 0.8136646 1. 0.79166667 0.70929937 - 0.89442719 0.90632697 0.79166667 1. ] - -mean value: 0.8749752424997082 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.875 0.85714286 1. 0.83333333 0.76923077 - 0.90909091 0.92307692 0.83333333 1. ] - -mean value: 0.9000208125208126 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.77777778 0.85714286 1. 0.83333333 0.71428571 - 1. 0.85714286 0.83333333 1. ] - -mean value: 0.8873015873015874 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.85714286 1. 0.83333333 0.83333333 - 0.83333333 1. 0.83333333 1. ] - -mean value: 0.9190476190476191 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.93333333 0.93333333 1. 0.93333333 0.9 - 0.96666667 0.96666667 0.93333333 1. ] - -mean value: 0.9566666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95652174 0.9068323 1. 0.89583333 0.875 - 0.91666667 0.97916667 0.89583333 1. ] - -mean value: 0.9425854037267081 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.77777778 0.75 1. 0.71428571 0.625 - 0.83333333 0.85714286 0.71428571 1. ] - -mean value: 0.8271825396825397 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01944017 0.02147317 0.02204108 0.02193952 0.02178168 0.02203751 - 0.0221312 0.02462649 0.02226901 0.02220225] - -mean value: 0.02199420928955078 - -key: score_time -value: [0.01194978 0.01285076 0.01210022 0.01319599 0.01441765 0.0147438 - 0.01435232 0.01223373 0.01328063 0.01389551] - -mean value: 0.013302040100097657 - -key: test_mcc -value: [ 0.01545612 -0.03524537 0.16850509 0.01545612 -0.13363062 0. - 0.20044593 0.11111111 -0.16666667 -0.13363062] - -mean value: 0.004180109125903944 - -key: train_mcc -value: [0.29142448 0.26553052 0.31537228 0.31537228 0.28822231 0.33407831 - 0.28822231 0.31190697 0.33407831 0.28822231] - -mean value: 0.303243008578363 - -key: test_fscore -value: [0.18181818 0.16666667 0.22222222 0.18181818 0. 0.18181818 - 0.25 0.22222222 0. 0. ] - -mean value: 0.14065656565656565 - -key: train_fscore -value: [0.19047619 0.16129032 0.21875 0.21875 0.1875 0.24242424 - 0.1875 0.21538462 0.24242424 0.1875 ] - -mean value: 0.20519996132899357 - -key: test_precision -value: [0.25 0.2 0.5 0.25 0. 0.2 - 0.5 0.33333333 0. 0. ] - -mean value: 0.22333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.14285714 0.14285714 0.14285714 0.14285714 0. 0.16666667 - 0.16666667 0.16666667 0. 0. ] - -mean value: 0.10714285714285714 - -key: train_recall -value: [0.10526316 0.0877193 0.12280702 0.12280702 0.10344828 0.13793103 - 0.10344828 0.12068966 0.13793103 0.10344828] - -mean value: 0.11454930429522081 - -key: test_accuracy -value: [0.7 0.66666667 0.76666667 0.7 0.73333333 0.7 - 0.8 0.76666667 0.7 0.73333333] - -mean value: 0.7266666666666668 - -key: train_accuracy -value: [0.81111111 0.80740741 0.81481481 0.81481481 0.80740741 0.81481481 - 0.80740741 0.81111111 0.81481481 0.80740741] - -mean value: 0.8111111111111111 - -key: test_roc_auc -value: [0.50621118 0.48447205 0.54968944 0.50621118 0.45833333 0.5 - 0.5625 0.54166667 0.4375 0.45833333] - -mean value: 0.500491718426501 - -key: train_roc_auc -value: [0.55263158 0.54385965 0.56140351 0.56140351 0.55172414 0.56896552 - 0.55172414 0.56034483 0.56896552 0.55172414] - -mean value: 0.5572746521476104 - -key: test_jcc -value: [0.1 0.09090909 0.125 0.1 0. 0.1 - 0.14285714 0.125 0. 0. ] - -mean value: 0.07837662337662338 - -key: train_jcc -value: [0.10526316 0.0877193 0.12280702 0.12280702 0.10344828 0.13793103 - 0.10344828 0.12068966 0.13793103 0.10344828] - -mean value: 0.11454930429522081 - -key: TN -value: 211 - -mean value: 211.0 - -key: FP -value: 57 - -mean value: 57.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 7 - -mean value: 7.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.08 - -Accuracy on Blind test: 0.73 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02265096 0.03459263 0.03272867 0.03490329 0.03444433 0.03439617 - 0.03457141 0.03514528 0.03444648 0.03956985] - -mean value: 0.03374490737915039 - -key: score_time -value: [0.02268505 0.02258205 0.02024865 0.02170777 0.02348852 0.02161622 - 0.02335501 0.02056766 0.02193522 0.02281809] - -mean value: 0.022100424766540526 - -key: test_mcc -value: [0.60421798 0.55841694 0.34151451 0.16850509 0.78446454 0.79166667 - 0.78446454 0.90632697 0.58333333 0.53931937] - -mean value: 0.6062229936558883 - -key: train_mcc -value: [0.88679821 0.89847907 0.91011834 0.89847907 0.87741118 0.92271775 - 0.88828904 0.94481583 0.91083445 0.87741118] - -mean value: 0.9015354123498576 - -key: test_fscore -value: [0.6 0.66666667 0.4 0.22222222 0.8 0.83333333 - 0.8 0.92307692 0.66666667 0.6 ] - -mean value: 0.6511965811965812 - -key: train_fscore -value: [0.90909091 0.91891892 0.92857143 0.91891892 0.90265487 0.93913043 - 0.91071429 0.95652174 0.92857143 0.90265487] - -mean value: 0.9215747798212208 - -key: test_precision -value: [1. 0.625 0.66666667 0.5 1. 0.83333333 - 1. 0.85714286 0.66666667 0.75 ] - -mean value: 0.7898809523809525 - -key: train_precision -value: [0.94339623 0.94444444 0.94545455 0.94444444 0.92727273 0.94736842 - 0.94444444 0.96491228 0.96296296 0.92727273] - -mean value: 0.9451973224465778 - -key: test_recall -value: [0.42857143 0.71428571 0.28571429 0.14285714 0.66666667 0.83333333 - 0.66666667 1. 0.66666667 0.5 ] - -mean value: 0.5904761904761905 - -key: train_recall -value: [0.87719298 0.89473684 0.9122807 0.89473684 0.87931034 0.93103448 - 0.87931034 0.94827586 0.89655172 0.87931034] - -mean value: 0.8992740471869327 - -key: test_accuracy -value: [0.86666667 0.83333333 0.8 0.76666667 0.93333333 0.93333333 - 0.93333333 0.96666667 0.86666667 0.86666667] - -mean value: 0.8766666666666667 - -key: train_accuracy -value: [0.96296296 0.96666667 0.97037037 0.96666667 0.95925926 0.97407407 - 0.96296296 0.98148148 0.97037037 0.95925926] - -mean value: 0.9674074074074074 - -key: test_roc_auc -value: [0.71428571 0.79192547 0.62111801 0.54968944 0.83333333 0.89583333 - 0.83333333 0.97916667 0.79166667 0.72916667] - -mean value: 0.7739518633540373 - -key: train_roc_auc -value: [0.93155424 0.94032617 0.9490981 0.94032617 0.93022121 0.95844177 - 0.9325797 0.96942095 0.94355888 0.93022121] - -mean value: 0.9425748391661479 - -key: test_jcc -value: [0.42857143 0.5 0.25 0.125 0.66666667 0.71428571 - 0.66666667 0.85714286 0.5 0.42857143] - -mean value: 0.5136904761904761 - -key: train_jcc -value: [0.83333333 0.85 0.86666667 0.85 0.82258065 0.8852459 - 0.83606557 0.91666667 0.86666667 0.82258065] - -mean value: 0.854980609906575 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.9 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.24151182 0.25762248 0.23883343 0.24260068 0.24528241 0.24203515 - 0.33177257 0.24591899 0.2431798 0.36651111] - -mean value: 0.2655268430709839 - -key: score_time -value: [0.02142286 0.02139711 0.02129865 0.02379942 0.02299094 0.02134085 - 0.02335072 0.02251148 0.02318573 0.01824236] - -mean value: 0.02195401191711426 - -key: test_mcc -value: [0.60421798 0.55841694 0.34151451 0.16850509 0.67082039 0.79166667 - 0.78446454 0.76376262 0.58191437 0.53931937] - -mean value: 0.5804602478536405 - -key: train_mcc -value: [0.88679821 0.89847907 0.91011834 0.89847907 0.93515969 0.92271775 - 0.88828904 0.95685154 0.93412492 0.87741118] - -mean value: 0.9108428819808394 - -key: test_fscore -value: [0.6 0.66666667 0.4 0.22222222 0.72727273 0.83333333 - 0.8 0.8 0.66666667 0.6 ] - -mean value: 0.6316161616161616 - -key: train_fscore -value: [0.90909091 0.91891892 0.92857143 0.91891892 0.94915254 0.93913043 - 0.91071429 0.96610169 0.94827586 0.90265487] - -mean value: 0.9291529862610808 - -key: test_precision -value: [1. 0.625 0.66666667 0.5 0.8 0.83333333 - 1. 0.66666667 0.55555556 0.75 ] - -mean value: 0.7397222222222222 - -key: train_precision -value: [0.94339623 0.94444444 0.94545455 0.94444444 0.93333333 0.94736842 - 0.94444444 0.95 0.94827586 0.92727273] - -mean value: 0.9428434448930633 - -key: test_recall -value: [0.42857143 0.71428571 0.28571429 0.14285714 0.66666667 0.83333333 - 0.66666667 1. 0.83333333 0.5 ] - -mean value: 0.6071428571428571 - -key: train_recall -value: [0.87719298 0.89473684 0.9122807 0.89473684 0.96551724 0.93103448 - 0.87931034 0.98275862 0.94827586 0.87931034] - -mean value: 0.9165154264972776 - -key: test_accuracy -value: [0.86666667 0.83333333 0.8 0.76666667 0.9 0.93333333 - 0.93333333 0.9 0.83333333 0.86666667] - -mean value: 0.8633333333333333 - -key: train_accuracy -value: [0.96296296 0.96666667 0.97037037 0.96666667 0.97777778 0.97407407 - 0.96296296 0.98518519 0.97777778 0.95925926] - -mean value: 0.9703703703703702 - -key: test_roc_auc -value: [0.71428571 0.79192547 0.62111801 0.54968944 0.8125 0.89583333 - 0.83333333 0.9375 0.83333333 0.72916667] - -mean value: 0.771868530020704 - -key: train_roc_auc -value: [0.93155424 0.94032617 0.9490981 0.94032617 0.97332466 0.95844177 - 0.9325797 0.98430384 0.96706246 0.93022121] - -mean value: 0.9507238307081127 - -key: test_jcc -value: [0.42857143 0.5 0.25 0.125 0.57142857 0.71428571 - 0.66666667 0.66666667 0.5 0.42857143] - -mean value: 0.4851190476190476 - -key: train_jcc -value: [0.83333333 0.85 0.86666667 0.85 0.90322581 0.8852459 - 0.83606557 0.93442623 0.90163934 0.82258065] - -mean value: 0.8683183500793232 - -key: TN -value: 221 - -mean value: 221.0 - -key: FP -value: 26 - -mean value: 26.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 38 - -mean value: 38.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: 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 - baseline_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: 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 - baseline_CV['Resampling'] = rs_none -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: 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 - baseline_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: 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 - baseline_BT['Resampling'] = rs_none -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.9 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.07176328 0.12339711 0.08598828 0.03456306 0.04575658 0.054353 - 0.05356979 0.05598712 0.03727078 0.03516769] - -mean value: 0.059781670570373535 - -key: score_time -value: [0.01241064 0.01525736 0.01444387 0.01452112 0.01287413 0.0130868 - 0.01322174 0.01300502 0.013304 0.01292682] - -mean value: 0.013505148887634277 - -key: test_mcc -value: [0.91666667 0.91986621 0.95825929 0.8729597 0.95833333 0.95825929 - 0.91485507 0.87979456 0.7876601 0.79308818] - -mean value: 0.8959742397987605 - -key: train_mcc -value: [0.95287259 0.95287259 0.94357214 0.9576579 0.95298417 0.93883426 - 0.9483278 0.95294092 0.95765696 0.93891474] - -mean value: 0.9496634068713254 - -key: test_fscore -value: [0.95833333 0.96 0.97959184 0.93877551 0.9787234 0.97959184 - 0.95652174 0.93877551 0.88888889 0.88372093] - -mean value: 0.9462922989718086 - -key: train_fscore -value: [0.97652582 0.97652582 0.97183099 0.97882353 0.97652582 0.96941176 - 0.97435897 0.97652582 0.97892272 0.96969697] - -mean value: 0.9749148227101694 - -key: test_precision -value: [0.95833333 0.92307692 0.96 0.92 1. 0.96 - 0.95652174 0.88461538 0.90909091 0.95 ] - -mean value: 0.9421638289246983 - -key: train_precision -value: [0.97196262 0.97196262 0.96728972 0.97652582 0.97196262 0.96713615 - 0.96759259 0.97652582 0.97663551 0.96296296] - -mean value: 0.9710556433094937 - -key: test_recall -value: [0.95833333 1. 1. 0.95833333 0.95833333 1. - 0.95652174 1. 0.86956522 0.82608696] - -mean value: 0.9527173913043478 - -key: train_recall -value: [0.98113208 0.98113208 0.97641509 0.98113208 0.98113208 0.97169811 - 0.98122066 0.97652582 0.98122066 0.97652582] - -mean value: 0.9788134467180442 - -key: test_accuracy -value: [0.95833333 0.95833333 0.9787234 0.93617021 0.9787234 0.9787234 - 0.95744681 0.93617021 0.89361702 0.89361702] - -mean value: 0.9469858156028369 - -key: train_accuracy -value: [0.97641509 0.97641509 0.97176471 0.97882353 0.97647059 0.96941176 - 0.97411765 0.97647059 0.97882353 0.96941176] - -mean value: 0.9748124306326303 - -key: test_roc_auc -value: [0.95833333 0.95833333 0.97826087 0.93568841 0.97916667 0.97826087 - 0.95742754 0.9375 0.89311594 0.89221014] - -mean value: 0.9468297101449276 - -key: train_roc_auc -value: [0.97641509 0.97641509 0.97177562 0.97882895 0.97648153 0.96941713 - 0.97410089 0.97647046 0.97881788 0.96939499] - -mean value: 0.9748117636637434 - -key: test_jcc -value: [0.92 0.92307692 0.96 0.88461538 0.95833333 0.96 - 0.91666667 0.88461538 0.8 0.79166667] - -mean value: 0.8998974358974359 - -key: train_jcc -value: [0.95412844 0.95412844 0.94520548 0.95852535 0.95412844 0.94063927 - 0.95 0.95412844 0.9587156 0.94117647] - -mean value: 0.9510775922866967 - -key: TN -value: 222 - -mean value: 222.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 225 - -mean value: 225.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.9 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.77030396 0.77836871 0.9878099 0.79601216 0.92038512 0.77858377 - 0.77486515 0.94918537 0.7837646 0.77919817] - -mean value: 0.8318476915359497 - -key: score_time -value: [0.0130918 0.01311946 0.01454043 0.01379514 0.01311183 0.01433945 - 0.01438642 0.01442361 0.01438904 0.01400876] - -mean value: 0.01392059326171875 - -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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.9591663 0.9591663 0.95825929 0.91804649 0.91804649 0.95825929 - 1. 0.91833182 0.91833182 0.91804649] - -mean value: 0.942565431502137 - -key: train_mcc -value: [0.98594778 1. 0.985981 1. 1. 1. - 1. 0.98598008 1. 0.99063185] - -mean value: 0.994854070833143 - -key: test_fscore -value: [0.97959184 0.97959184 0.97959184 0.96 0.96 0.97959184 - 1. 0.95833333 0.95833333 0.95454545] - -mean value: 0.9709579468150897 - -key: train_fscore -value: [0.99297424 1. 0.99297424 1. 1. 1. - 1. 0.99300699 1. 0.9953271 ] - -mean value: 0.9974282573562487 - -key: test_precision -value: [0.96 0.96 0.96 0.92307692 0.92307692 0.96 - 1. 0.92 0.92 1. ] - -mean value: 0.9526153846153846 - -key: train_precision -value: [0.98604651 1. 0.98604651 1. 1. 1. - 1. 0.98611111 1. 0.99069767] - -mean value: 0.9948901808785531 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.91304348] - -mean value: 0.9913043478260869 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.97916667 0.9787234 0.95744681 0.95744681 0.9787234 - 1. 0.95744681 0.95744681 0.95744681] - -mean value: 0.9703014184397164 - -key: train_accuracy -value: [0.99292453 1. 0.99294118 1. 1. 1. - 1. 0.99294118 1. 0.99529412] - -mean value: 0.9974100998890123 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.97826087 0.95652174 0.95652174 0.97826087 - 1. 0.95833333 0.95833333 0.95652174] - -mean value: 0.9701086956521741 - -key: train_roc_auc -value: [0.99292453 1. 0.99295775 1. 1. 1. - 1. 0.99292453 1. 0.99528302] - -mean value: 0.9974089821950571 - -key: test_jcc -value: [0.96 0.96 0.96 0.92307692 0.92307692 0.96 - 1. 0.92 0.92 0.91304348] - -mean value: 0.9439197324414715 - -key: train_jcc -value: [0.98604651 1. 0.98604651 1. 1. 1. - 1. 0.98611111 1. 0.99069767] - -mean value: 0.9948901808785531 - -key: TN -value: 225 - -mean value: 225.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 233 - -mean value: 233.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01458406 0.01315212 0.01017809 0.01075673 0.00951028 0.00990939 - 0.00977349 0.00975919 0.00944519 0.00961947] - -mean value: 0.010668802261352538 - -key: score_time -value: [0.01208496 0.00924253 0.00898147 0.00877953 0.00888085 0.00893021 - 0.00886679 0.00882578 0.00890112 0.00885224] - -mean value: 0.00923454761505127 - -key: test_mcc -value: [0.46195658 0.50709255 0.49454913 0.53734864 0.37458096 0.58127976 - 0.20543379 0.49819858 0.5326087 0.32123465] - -mean value: 0.4514283323551038 - -key: train_mcc -value: [0.56475627 0.58494523 0.50437209 0.61504595 0.53174355 0.51048267 - 0.52595168 0.5537337 0.53183435 0.53454055] - -mean value: 0.5457406041468285 - -key: test_fscore -value: [0.74509804 0.76923077 0.76923077 0.75555556 0.72727273 0.80769231 - 0.64150943 0.76 0.76595745 0.66666667] - -mean value: 0.7408213715635257 - -key: train_fscore -value: [0.79744136 0.80519481 0.76789588 0.80097087 0.78205128 0.77378436 - 0.7803838 0.79229122 0.78496868 0.78372591] - -mean value: 0.7868708170032758 - -key: test_precision -value: [0.7037037 0.71428571 0.71428571 0.80952381 0.64516129 0.75 - 0.56666667 0.7037037 0.75 0.64 ] - -mean value: 0.6997330602491891 - -key: train_precision -value: [0.72762646 0.744 0.71084337 0.825 0.71484375 0.70114943 - 0.71484375 0.72834646 0.70676692 0.72047244] - -mean value: 0.7293892572856329 - -key: test_recall -value: [0.79166667 0.83333333 0.83333333 0.70833333 0.83333333 0.875 - 0.73913043 0.82608696 0.7826087 0.69565217] - -mean value: 0.7918478260869565 - -key: train_recall -value: [0.88207547 0.87735849 0.83490566 0.77830189 0.86320755 0.86320755 - 0.85915493 0.8685446 0.88262911 0.85915493] - -mean value: 0.8568540171848703 - -key: test_accuracy -value: [0.72916667 0.75 0.74468085 0.76595745 0.68085106 0.78723404 - 0.59574468 0.74468085 0.76595745 0.65957447] - -mean value: 0.7223847517730496 - -key: train_accuracy -value: [0.7759434 0.78773585 0.74823529 0.80705882 0.76 0.74823529 - 0.75764706 0.77176471 0.75764706 0.76235294] - -mean value: 0.7676620421753607 - -key: test_roc_auc -value: [0.72916667 0.75 0.74275362 0.76721014 0.67753623 0.78532609 - 0.59873188 0.74637681 0.76630435 0.66032609] - -mean value: 0.7223731884057971 - -key: train_roc_auc -value: [0.7759434 0.78773585 0.74843875 0.80699132 0.76024227 0.74850518 - 0.75740765 0.77153645 0.75735229 0.76212463] - -mean value: 0.7676277792541413 - -key: test_jcc -value: [0.59375 0.625 0.625 0.60714286 0.57142857 0.67741935 - 0.47222222 0.61290323 0.62068966 0.5 ] - -mean value: 0.5905555886611226 - -key: train_jcc -value: [0.66312057 0.67391304 0.62323944 0.66801619 0.64210526 0.63103448 - 0.63986014 0.65602837 0.64604811 0.6443662 ] - -mean value: 0.6487731803525566 - -key: TN -value: 154 - -mean value: 154.0 - -key: FP -value: 49 - -mean value: 49.0 - -key: FN -value: 82 - -mean value: 82.0 - -key: TP -value: 187 - -mean value: 187.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.59 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00972295 0.0105269 0.01007748 0.00972223 0.00964475 0.00968885 - 0.01084328 0.00980568 0.01090193 0.00983763] - -mean value: 0.010077166557312011 - -key: score_time -value: [0.00889754 0.0095396 0.00877905 0.00867987 0.00916791 0.00882196 - 0.00956964 0.0089047 0.00913191 0.00880623] - -mean value: 0.009029841423034668 - -key: test_mcc -value: [0.33333333 0.54213748 0.27657348 0.44874504 0.23593505 0.40653424 - 0.45948781 0.40398551 0.40437762 0.23435724] - -mean value: 0.37454668007011227 - -key: train_mcc -value: [0.44815808 0.45301166 0.46390277 0.44950269 0.4494076 0.45881962 - 0.46352644 0.44975749 0.46352644 0.49669838] - -mean value: 0.4596311163425316 - -key: test_fscore -value: [0.66666667 0.7755102 0.66666667 0.74509804 0.60869565 0.69565217 - 0.74509804 0.69565217 0.68181818 0.57142857] - -mean value: 0.6852286369093091 - -key: train_fscore -value: [0.72599532 0.73023256 0.73611111 0.72076372 0.72340426 0.72813239 - 0.73239437 0.73103448 0.73239437 0.75288684] - -mean value: 0.731334940276696 - -key: test_precision -value: [0.66666667 0.76 0.62962963 0.7037037 0.63636364 0.72727273 - 0.67857143 0.69565217 0.71428571 0.63157895] - -mean value: 0.6843724627774972 - -key: train_precision -value: [0.72093023 0.72018349 0.72272727 0.7294686 0.72511848 0.72985782 - 0.73239437 0.71621622 0.73239437 0.74090909] - -mean value: 0.727019993339497 - -key: test_recall -value: [0.66666667 0.79166667 0.70833333 0.79166667 0.58333333 0.66666667 - 0.82608696 0.69565217 0.65217391 0.52173913] - -mean value: 0.6903985507246376 - -key: train_recall -value: [0.73113208 0.74056604 0.75 0.71226415 0.72169811 0.72641509 - 0.73239437 0.74647887 0.73239437 0.76525822] - -mean value: 0.7358601293294358 - -key: test_accuracy -value: [0.66666667 0.77083333 0.63829787 0.72340426 0.61702128 0.70212766 - 0.72340426 0.70212766 0.70212766 0.61702128] - -mean value: 0.6863031914893617 - -key: train_accuracy -value: [0.7240566 0.72641509 0.73176471 0.72470588 0.72470588 0.72941176 - 0.73176471 0.72470588 0.73176471 0.74823529] - -mean value: 0.7297530521642619 - -key: test_roc_auc -value: [0.66666667 0.77083333 0.63677536 0.72192029 0.61775362 0.70289855 - 0.72554348 0.70199275 0.70108696 0.61503623] - -mean value: 0.6860507246376811 - -key: train_roc_auc -value: [0.7240566 0.72641509 0.73180751 0.72467668 0.72469882 0.72940473 - 0.73176322 0.72465453 0.73176322 0.74819515] - -mean value: 0.7297435556736646 - -key: test_jcc -value: [0.5 0.63333333 0.5 0.59375 0.4375 0.53333333 - 0.59375 0.53333333 0.51724138 0.4 ] - -mean value: 0.5242241379310345 - -key: train_jcc -value: [0.56985294 0.57509158 0.58241758 0.56343284 0.56666667 0.57249071 - 0.57777778 0.57608696 0.57777778 0.6037037 ] - -mean value: 0.5765298523273891 - -key: TN -value: 161 - -mean value: 161.0 - -key: FP -value: 73 - -mean value: 73.0 - -key: FN -value: 75 - -mean value: 75.0 - -key: TP -value: 163 - -mean value: 163.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.6 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00896263 0.00907159 0.00911069 0.00904894 0.00904322 0.00904942 - 0.00902128 0.00901937 0.00896335 0.00900316] - -mean value: 0.009029364585876465 - -key: score_time -value: [0.01160336 0.0112009 0.01140761 0.01143956 0.01129055 0.01132679 - 0.01131845 0.01161385 0.01135063 0.01171207] - -mean value: 0.011426377296447753 - -key: test_mcc -value: [0.45873171 0.58333333 0.62966842 0.57560058 0.5326087 0.58127976 - 0.23593505 0.45948781 0.58428436 0.57560058] - -mean value: 0.5216530296075821 - -key: train_mcc -value: [0.68944668 0.68433541 0.6853504 0.70393272 0.69963015 0.70853552 - 0.71355137 0.6944692 0.68104277 0.7062955 ] - -mean value: 0.6966589720547952 - -key: test_fscore -value: [0.73469388 0.79166667 0.83018868 0.8 0.76595745 0.80769231 - 0.625 0.74509804 0.8 0.77272727] - -mean value: 0.7673024289906747 - -key: train_fscore -value: [0.84792627 0.84454756 0.84526559 0.85382831 0.85253456 0.85581395 - 0.85977011 0.84988453 0.84474886 0.85842697] - -mean value: 0.8512746708206658 - -key: test_precision -value: [0.72 0.79166667 0.75862069 0.76923077 0.7826087 0.75 - 0.6 0.67857143 0.74074074 0.80952381] - -mean value: 0.740096280004076 - -key: train_precision -value: [0.82882883 0.83105023 0.8280543 0.84018265 0.83333333 0.8440367 - 0.84234234 0.83636364 0.82222222 0.82327586] - -mean value: 0.8329690097761897 - -key: test_recall -value: [0.75 0.79166667 0.91666667 0.83333333 0.75 0.875 - 0.65217391 0.82608696 0.86956522 0.73913043] - -mean value: 0.8003623188405797 - -key: train_recall -value: [0.86792453 0.85849057 0.86320755 0.86792453 0.87264151 0.86792453 - 0.87793427 0.86384977 0.8685446 0.89671362] - -mean value: 0.8705155461068296 - -key: test_accuracy -value: [0.72916667 0.79166667 0.80851064 0.78723404 0.76595745 0.78723404 - 0.61702128 0.72340426 0.78723404 0.78723404] - -mean value: 0.7584663120567378 - -key: train_accuracy -value: [0.84433962 0.84198113 0.84235294 0.85176471 0.84941176 0.85411765 - 0.85647059 0.84705882 0.84 0.85176471] - -mean value: 0.8479261931187569 - -key: test_roc_auc -value: [0.72916667 0.79166667 0.80615942 0.78623188 0.76630435 0.78532609 - 0.61775362 0.72554348 0.78894928 0.78623188] - -mean value: 0.7583333333333332 - -key: train_roc_auc -value: [0.84433962 0.84198113 0.8424019 0.85180264 0.84946629 0.85415006 - 0.85641997 0.84701922 0.83993268 0.85165869] - -mean value: 0.8479172203029497 - -key: test_jcc -value: [0.58064516 0.65517241 0.70967742 0.66666667 0.62068966 0.67741935 - 0.45454545 0.59375 0.66666667 0.62962963] - -mean value: 0.6254862421957805 - -key: train_jcc -value: [0.736 0.73092369 0.732 0.74493927 0.74297189 0.74796748 - 0.75403226 0.73895582 0.7312253 0.7519685 ] - -mean value: 0.7410984214996559 - -key: TN -value: 169 - -mean value: 169.0 - -key: FP -value: 47 - -mean value: 47.0 - -key: FN -value: 67 - -mean value: 67.0 - -key: TP -value: 189 - -mean value: 189.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.08 - -Accuracy on Blind test: 0.64 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02503657 0.01970744 0.01938605 0.01980352 0.02213597 0.01945448 - 0.01934409 0.0216291 0.01975822 0.01986694] - -mean value: 0.020612239837646484 - -key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - -value: [0.01221991 0.01162744 0.01127386 0.01149464 0.01130176 0.01127148 - 0.01140594 0.01146197 0.01157188 0.0117116 ] - -mean value: 0.01153404712677002 - -key: test_mcc -value: [0.79235477 0.79235477 0.8729597 0.78804348 0.84254172 0.7085716 - 0.61706091 0.70289855 0.79308818 0.66801039] - -mean value: 0.7577884061466912 - -key: train_mcc -value: [0.86367849 0.8823209 0.84973976 0.8594196 0.88721041 0.85456939 - 0.87906504 0.84585771 0.86886207 0.87768255] - -mean value: 0.8668405904224079 - -key: test_fscore -value: [0.89795918 0.89361702 0.93877551 0.89361702 0.90909091 0.84444444 - 0.8 0.85106383 0.88372093 0.80952381] - -mean value: 0.8721812659509698 - -key: train_fscore -value: [0.93045564 0.94033413 0.92344498 0.92788462 0.94285714 0.92565947 - 0.93719807 0.92048193 0.93301435 0.93925234] - -mean value: 0.9320582656969542 - -key: test_precision -value: [0.88 0.91304348 0.92 0.91304348 1. 0.9047619 - 0.81818182 0.83333333 0.95 0.89473684] - -mean value: 0.902710085490406 - -key: train_precision -value: [0.94634146 0.95169082 0.9368932 0.94607843 0.95192308 0.94146341 - 0.96517413 0.94554455 0.95121951 0.93488372] - -mean value: 0.9471212328417973 - -key: test_recall -value: [0.91666667 0.875 0.95833333 0.875 0.83333333 0.79166667 - 0.7826087 0.86956522 0.82608696 0.73913043] - -mean value: 0.8467391304347827 - -key: train_recall -value: [0.91509434 0.92924528 0.91037736 0.91037736 0.93396226 0.91037736 - 0.91079812 0.89671362 0.91549296 0.94366197] - -mean value: 0.9176100628930817 - -key: test_accuracy -value: [0.89583333 0.89583333 0.93617021 0.89361702 0.91489362 0.85106383 - 0.80851064 0.85106383 0.89361702 0.82978723] - -mean value: 0.8770390070921985 - -key: train_accuracy -value: [0.93160377 0.94103774 0.92470588 0.92941176 0.94352941 0.92705882 - 0.93882353 0.92235294 0.93411765 0.93882353] - -mean value: 0.9331465038845728 - -key: test_roc_auc -value: [0.89583333 0.89583333 0.93568841 0.89402174 0.91666667 0.85235507 - 0.80797101 0.85144928 0.89221014 0.82789855] - -mean value: 0.8769927536231885 - -key: train_roc_auc -value: [0.93160377 0.94103774 0.92467225 0.92936708 0.94350695 0.92701967 - 0.93888963 0.92241341 0.93416157 0.93881212] - -mean value: 0.9331484188147755 - -key: test_jcc -value: [0.81481481 0.80769231 0.88461538 0.80769231 0.83333333 0.73076923 - 0.66666667 0.74074074 0.79166667 0.68 ] - -mean value: 0.7757991452991453 - -key: train_jcc -value: [0.86995516 0.88738739 0.85777778 0.86547085 0.89189189 0.86160714 - 0.88181818 0.85267857 0.87443946 0.88546256] - -mean value: 0.8728488979079051 - -key: TN -value: 214 - -mean value: 214.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.43 - -Accuracy on Blind test: 0.81 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.92997265 1.80154014 1.83383083 0.84829307 0.87547112 1.53885913 - 1.83645678 1.82473302 1.93474245 1.84138298] - -mean value: 1.6265282154083252 - -key: score_time -value: [0.01523018 0.01491117 0.01497197 0.01244283 0.01243138 0.01260138 - 0.01497459 0.01503444 0.01501369 0.01504254] - -mean value: 0.01426541805267334 - -key: test_mcc -value: [0.91986621 1. 0.87917396 0.82971014 0.8729597 0.95825929 - 0.8729597 0.87318841 0.87318841 0.8047833 ] - -mean value: 0.8884089115461405 - -key: train_mcc -value: [0.99061012 0.99529409 0.99530516 0.93023921 0.95791435 0.99530516 - 1. 1. 0.99530506 1. ] - -mean value: 0.985997315463707 - -key: test_fscore -value: [0.96 1. 0.94117647 0.91666667 0.93877551 0.97959184 - 0.93333333 0.93617021 0.93617021 0.87804878] - -mean value: 0.9419933023546732 - -key: train_fscore -value: [0.99530516 0.99764706 0.99764706 0.96385542 0.97902098 0.99764706 - 1. 1. 0.99765808 1. ] - -mean value: 0.9928780821122857 - -key: test_precision -value: [0.92307692 1. 0.88888889 0.91666667 0.92 0.96 - 0.95454545 0.91666667 0.91666667 1. ] - -mean value: 0.9396511266511267 - -key: train_precision -value: [0.99065421 0.99530516 0.99530516 0.98522167 0.96774194 0.99530516 - 1. 1. 0.9953271 1. ] - -mean value: 0.992486041172968 - -key: test_recall -value: [1. 1. 1. 0.91666667 0.95833333 1. - 0.91304348 0.95652174 0.95652174 0.7826087 ] - -mean value: 0.9483695652173914 - -key: train_recall -value: [1. 1. 1. 0.94339623 0.99056604 1. - 1. 1. 1. 1. ] - -mean value: 0.9933962264150944 - -key: test_accuracy -value: [0.95833333 1. 0.93617021 0.91489362 0.93617021 0.9787234 - 0.93617021 0.93617021 0.93617021 0.89361702] - -mean value: 0.9426418439716313 - -key: train_accuracy -value: [0.99528302 0.99764151 0.99764706 0.96470588 0.97882353 0.99764706 - 1. 1. 0.99764706 1. ] - -mean value: 0.9929395116537181 - -key: test_roc_auc -value: [0.95833333 1. 0.93478261 0.91485507 0.93568841 0.97826087 - 0.93568841 0.9365942 0.9365942 0.89130435] - -mean value: 0.9422101449275363 - -key: train_roc_auc -value: [0.99528302 0.99764151 0.99765258 0.96465586 0.97885109 0.99765258 - 1. 1. 0.99764151 1. ] - -mean value: 0.9929378155726812 - -key: test_jcc -value: [0.92307692 1. 0.88888889 0.84615385 0.88461538 0.96 - 0.875 0.88 0.88 0.7826087 ] - -mean value: 0.8920343738387218 - -key: train_jcc -value: [0.99065421 0.99530516 0.99530516 0.93023256 0.95890411 0.99530516 - 1. 1. 0.9953271 1. ] - -mean value: 0.9861033469097537 - -key: TN -value: 221 - -mean value: 221.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 224 - -mean value: 224.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.91 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03088903 0.02440882 0.02251101 0.0216732 0.02350116 0.01992917 - 0.02361035 0.02364278 0.02026367 0.02351403] - -mean value: 0.023394322395324706 - -key: score_time -value: [0.01192951 0.00916386 0.00897527 0.00892878 0.00879836 0.00887275 - 0.00888252 0.00896573 0.00882912 0.00882077] - -mean value: 0.009216666221618652 - -key: test_mcc -value: [1. 0.9591663 0.91833182 0.91485507 0.95833333 0.91804649 - 0.95833333 1. 0.91833182 0.91804649] - -mean value: 0.946344467300326 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97959184 0.95652174 0.95833333 0.9787234 0.96 - 0.9787234 1. 0.95833333 0.95454545] - -mean value: 0.9724772505587888 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.96 1. 0.95833333 1. 0.92307692 - 0.95833333 1. 0.92 1. ] - -mean value: 0.971974358974359 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.91666667 0.95833333 0.95833333 1. - 1. 1. 1. 0.91304348] - -mean value: 0.9746376811594203 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 - 0.9787234 1. 0.95744681 0.95744681] - -mean value: 0.9723847517730497 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97916667 0.95833333 0.95742754 0.97916667 0.95652174 - 0.97916667 1. 0.95833333 0.95652174] - -mean value: 0.9724637681159422 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.96 0.91666667 0.92 0.95833333 0.92307692 - 0.95833333 1. 0.92 0.91304348] - -mean value: 0.9469453734671125 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 229 - -mean value: 229.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 230 - -mean value: 230.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.93 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.11807203 0.11743093 0.11652422 0.11758637 0.11543202 0.115242 - 0.11911607 0.11675596 0.1182425 0.11732697] - -mean value: 0.11717290878295898 - -key: score_time -value: [0.01863527 0.01757407 0.01749945 0.0174973 0.01749563 0.01756263 - 0.01906514 0.01817083 0.01755691 0.01753497] - -mean value: 0.017859220504760742 - -key: test_mcc -value: [0.87576054 0.91666667 0.8729597 0.87917396 0.87979456 0.91485507 - 0.74682354 0.84254172 0.87318841 0.87318841] - -mean value: 0.8674952558647572 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93877551 0.95833333 0.93877551 0.94117647 0.93333333 0.95833333 - 0.86363636 0.92 0.93617021 0.93617021] - -mean value: 0.9324704280164676 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.92 0.95833333 0.92 0.88888889 1. 0.95833333 - 0.9047619 0.85185185 0.91666667 0.91666667] - -mean value: 0.9235502645502646 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95833333 0.95833333 0.95833333 1. 0.875 0.95833333 - 0.82608696 1. 0.95652174 0.95652174] - -mean value: 0.9447463768115943 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.95833333 0.93617021 0.93617021 0.93617021 0.95744681 - 0.87234043 0.91489362 0.93617021 0.93617021] - -mean value: 0.932136524822695 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.95833333 0.93568841 0.93478261 0.9375 0.95742754 - 0.87137681 0.91666667 0.9365942 0.9365942 ] - -mean value: 0.9322463768115942 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.88461538 0.92 0.88461538 0.88888889 0.875 0.92 - 0.76 0.85185185 0.88 0.88 ] - -mean value: 0.874497150997151 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 217 - -mean value: 217.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 223 - -mean value: 223.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.84 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00970769 0.0100112 0.01110268 0.01015735 0.00985336 0.01009297 - 0.00997114 0.01000333 0.00984669 0.0098896 ] - -mean value: 0.010063600540161134 - -key:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - score_time -value: [0.00882339 0.00872707 0.00935602 0.008955 0.00875282 0.00874496 - 0.00857615 0.00883889 0.00879312 0.00873065] - -mean value: 0.008829808235168457 - -key: test_mcc -value: [0.55381862 0.62554324 0.66801039 0.53176131 0.50321854 0.57427536 - 0.48913043 0.45173716 0.23994123 0.70289855] - -mean value: 0.534033484076412 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.74418605 0.80851064 0.84615385 0.7755102 0.77777778 0.79166667 - 0.73913043 0.73469388 0.64 0.85106383] - -mean value: 0.7708693321610286 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.84210526 0.82608696 0.78571429 0.76 0.7 0.79166667 - 0.73913043 0.69230769 0.59259259 0.83333333] - -mean value: 0.7562937225076813 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.79166667 0.91666667 0.79166667 0.875 0.79166667 - 0.73913043 0.7826087 0.69565217 0.86956522] - -mean value: 0.7920289855072463 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.77083333 0.8125 0.82978723 0.76595745 0.74468085 0.78723404 - 0.74468085 0.72340426 0.61702128 0.85106383] - -mean value: 0.7647163120567376 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.77083333 0.8125 0.82789855 0.76539855 0.74184783 0.78713768 - 0.74456522 0.72463768 0.61865942 0.85144928] - -mean value: 0.7644927536231885 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.59259259 0.67857143 0.73333333 0.63333333 0.63636364 0.65517241 - 0.5862069 0.58064516 0.47058824 0.74074074] - -mean value: 0.6307547771864332 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 49 - -mean value: 49.0 - -key: FN -value: 62 - -mean value: 62.0 - -key: TP -value: 187 - -mean value: 187.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.2 - -Accuracy on Blind test: 0.69 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.85506344 1.83201098 1.86094165 1.80816388 1.83163047 1.80685401 - 1.81309462 1.77139688 1.80277801 1.81867528] - -mean value: 1.820060920715332 - -key: score_time -value: [0.09151292 0.09343147 0.09155345 0.09221554 0.09903717 0.09667325 - 0.08981729 0.08958125 0.09671235 0.09113455] - -mean value: 0.09316692352294922 - -key: test_mcc -value: [1. 0.9591663 0.95833333 0.95825929 1. 0.95825929 - 0.8729597 0.95833333 0.87318841 0.91804649] - -mean value: 0.9456546149002671 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.9787234 0.9787234 0.97959184 1. 0.97959184 - 0.93333333 0.9787234 0.93617021 0.95454545] - -mean value: 0.971940288688009 - -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. 0.96 1. 0.96 - 0.95454545 0.95833333 0.91666667 1. ] - -mean value: 0.9749545454545455 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95833333 0.95833333 1. 1. 1. - 0.91304348 1. 0.95652174 0.91304348] - -mean value: 0.9699275362318842 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97916667 0.9787234 0.9787234 1. 0.9787234 - 0.93617021 0.9787234 0.93617021 0.95744681] - -mean value: 0.9723847517730497 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97916667 0.97916667 0.97826087 1. 0.97826087 - 0.93568841 0.97916667 0.9365942 0.95652174] - -mean value: 0.9722826086956522 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.95833333 0.95833333 0.96 1. 0.96 - 0.875 0.95833333 0.88 0.91304348] - -mean value: 0.946304347826087 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 229 - -mean value: 229.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.91 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.9310863 0.99813843 0.94916224 0.92142129 0.94790554 0.9610889 - 0.94120288 0.96842337 0.9388566 1.01775265] - -mean value: 0.9575038194656372 - -key: score_time -value: [0.20758581 0.20433855 0.20573854 0.19960141 0.2133472 0.18238068 - 0.21888518 0.21074367 0.20781922 0.20387912] - -mean value: 0.20543193817138672 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.91485507 1. 0.95825929 - 0.8729597 0.91833182 0.87318841 0.84147165] - -mean value: 0.929656557184863 - -key: train_mcc -value: [0.97651287 0.98117574 0.98589335 0.97656856 0.98122024 0.99063227 - 0.98121941 0.97656701 0.98589304 0.98134942] - -mean value: 0.9817031903143704 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.95833333 1. 0.97959184 - 0.93333333 0.95833333 0.93617021 0.9047619 ] - -mean value: 0.9628839195252569 - -key: train_fscore -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( -[0.9882904 0.99061033 0.99294118 0.9882904 0.99061033 0.99530516 - 0.99065421 0.98834499 0.99297424 0.99069767] - -mean value: 0.9908718901566708 - -key: test_precision -value: [0.96 1. 1. 0.95833333 1. 0.96 - 0.95454545 0.92 0.91666667 1. ] - -mean value: 0.9669545454545455 - -key: train_precision -value: [0.98139535 0.98598131 0.99061033 0.98139535 0.98598131 0.99065421 - 0.98604651 0.98148148 0.99065421 0.98156682] - -mean value: 0.9855766867736186 - -key: test_recall -value: [1. 1. 0.95833333 0.95833333 1. 1. - 0.91304348 1. 0.95652174 0.82608696] - -mean value: 0.9612318840579711 - -key: train_recall -value: [0.99528302 0.99528302 0.99528302 0.99528302 0.99528302 1. - 0.99530516 0.99530516 0.99530516 1. ] - -mean value: 0.9962330587297368 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.95744681 1. 0.9787234 - 0.93617021 0.95744681 0.93617021 0.91489362] - -mean value: 0.9638741134751774 - -key: train_accuracy -value: [0.98820755 0.99056604 0.99294118 0.98823529 0.99058824 0.99529412 - 0.99058824 0.98823529 0.99294118 0.99058824] - -mean value: 0.9908185349611542 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.95742754 1. 0.97826087 - 0.93568841 0.95833333 0.9365942 0.91304348] - -mean value: 0.9637681159420289 - -key: train_roc_auc -value: [0.98820755 0.99056604 0.99294667 0.98825184 0.99059926 0.99530516 - 0.99057711 0.98821862 0.9929356 0.99056604] - -mean value: 0.9908173886083798 - -key: test_jcc -value: [0.96 1. 0.95833333 0.92 1. 0.96 - 0.875 0.92 0.88 0.82608696] - -mean value: 0.9299420289855073 - -key: train_jcc -value: [0.97685185 0.98139535 0.98598131 0.97685185 0.98139535 0.99065421 - 0.98148148 0.97695853 0.98604651 0.98156682] - -mean value: 0.9819183254128323 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 227 - -mean value: 227.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.9 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.08386326 0.06949043 0.07181358 0.06469345 0.0743928 0.06188083 - 0.06902647 0.23564076 0.05683398 0.06208444] - -mean value: 0.08497200012207032 - -key: score_time -value: [0.01136065 0.01149487 0.01105356 0.01126266 0.01093841 0.01075006 - 0.01065063 0.01099658 0.01124334 0.01101613] - -mean value: 0.011076688766479492 - -key: test_mcc -value: [1. 1. 0.91485507 0.95825929 1. 0.91804649 - 1. 1. 0.91833182 0.95825929] - -mean value: 0.9667751969880165 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.95833333 0.97959184 1. 0.96 - 1. 1. 0.95833333 0.97777778] - -mean value: 0.9834036281179138 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.95833333 0.96 1. 0.92307692 - 1. 1. 0.92 1. ] - -mean value: 0.9761410256410257 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 0.95652174] - -mean value: 0.991485507246377 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.95744681 0.9787234 1. 0.95744681 - 1. 1. 0.95744681 0.9787234 ] - -mean value: 0.9829787234042554 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.95742754 0.97826087 1. 0.95652174 - 1. 1. 0.95833333 0.97826087] - -mean value: 0.9828804347826086 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.92 0.96 1. 0.92307692 - 1. 1. 0.92 0.95652174] - -mean value: 0.9679598662207359 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 234 - -mean value: 234.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.0377264 0.06563306 0.05789685 0.07437158 0.06598306 0.07598257 - 0.0666585 0.07467961 0.06921768 0.06565714] - -mean value: 0.0653806447982788 - -key: score_time -value: [0.02289009 0.0222764 0.02233481 0.02446914 0.02388811 0.02250719 - 0.01784444 0.01587081 0.0215292 0.01247811] - -mean value: 0.02060883045196533 - -key: test_mcc -value: [0.9591663 0.9591663 0.95825929 0.84147165 0.91485507 0.91804649 - 1. 0.84254172 0.87979456 0.87979456] - -mean value: 0.915309594361706 - -key: train_mcc -value: [0.97187112 0.98130676 0.98135106 0.985981 0.97674215 0.985981 - 0.98134942 0.98134942 0.99063185 0.9767396 ] - -mean value: 0.9813303399182998 - -key: test_fscore -value: [0.97959184 0.97959184 0.97959184 0.92307692 0.95833333 0.96 - 1. 0.92 0.93877551 0.93877551] - -mean value: 0.95777367870225 - -key: train_fscore -value: [0.98598131 0.99065421 0.99065421 0.99297424 0.98834499 0.99297424 - 0.99069767 0.99069767 0.9953271 0.98839907] - -mean value: 0.9906704709289615 - -key: test_precision -value: [0.96 0.96 0.96 0.85714286 0.95833333 0.92307692 - 1. 0.85185185 0.88461538 0.88461538] - -mean value: 0.9239635734635735 - -key: train_precision -value: [0.97685185 0.98148148 0.98148148 0.98604651 0.97695853 0.98604651 - 0.98156682 0.98156682 0.99069767 0.97706422] - -mean value: 0.9819761898571338 - -key: test_recall -value: [1. 1. 1. 1. 0.95833333 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [0.99528302 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9995283018867924 - -key: test_accuracy -value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.95744681 - 1. 0.91489362 0.93617021 0.93617021] - -mean value: 0.9554078014184398 - -key: train_accuracy -value: [0.98584906 0.99056604 0.99058824 0.99294118 0.98823529 0.99294118 - 0.99058824 0.99058824 0.99529412 0.98823529] - -mean value: 0.9905826859045506 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.97826087 0.91304348 0.95742754 0.95652174 - 1. 0.91666667 0.9375 0.9375 ] - -mean value: 0.9555253623188407 - -key: train_roc_auc -value: [0.98584906 0.99056604 0.99061033 0.99295775 0.98826291 0.99295775 - 0.99056604 0.99056604 0.99528302 0.98820755] - -mean value: 0.9905826468243422 - -key: test_jcc -value: [0.96 0.96 0.96 0.85714286 0.92 0.92307692 - 1. 0.85185185 0.88461538 0.88461538] - -mean value: 0.9201302401302403 - -key: train_jcc -value: [0.97235023 0.98148148 0.98148148 0.98604651 0.97695853 0.98604651 - 0.98156682 0.98156682 0.99069767 0.97706422] - -mean value: 0.9815260277134232 - -key: TN -value: 216 - -mean value: 216.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.88 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02088475 0.00992966 0.00963473 0.01010895 0.00942349 0.00943804 - 0.00965643 0.00975943 0.00966811 0.00966311] - -mean value: 0.010816669464111328 - -key: score_time -value: [0.01168203 0.00901556 0.00874496 0.0086658 0.0086267 0.00870442 - 0.00942421 0.00881624 0.00882101 0.00985885] - -mean value: 0.009235978126525879 - -key: test_mcc -value: [0.58333333 0.46861576 0.44874504 0.4899891 0.19493499 0.36699609 - 0.27717391 0.19202899 0.19490273 0.32605546] - -mean value: 0.35427753956239433 - -key: train_mcc -value: [0.44436622 0.43444585 0.44575704 0.37891473 0.32915125 0.33634235 - 0.42648362 0.40951959 0.35491596 0.38647587] - -mean value: 0.3946372484323964 - -key: test_fscore -value: [0.79166667 0.75471698 0.74509804 0.76 0.65454545 0.71698113 - 0.63829787 0.59574468 0.6122449 0.6 ] - -mean value: 0.6869295724786029 - -key: train_fscore -value: [0.73059361 0.7235023 0.73059361 0.69158879 0.69098712 0.68995633 - 0.72146119 0.72 0.7 0.71081678] - -mean value: 0.7109499724403869 - -key: test_precision -value: [0.79166667 0.68965517 0.7037037 0.73076923 0.58064516 0.65517241 - 0.625 0.58333333 0.57692308 0.70588235] - -mean value: 0.6642751111834407 - -key: train_precision -value: [0.7079646 0.70720721 0.7079646 0.68518519 0.63385827 0.64227642 - 0.70222222 0.6835443 0.65182186 0.67083333] - -mean value: 0.679287800811418 - -key: test_recall -value: [0.79166667 0.83333333 0.79166667 0.79166667 0.75 0.79166667 - 0.65217391 0.60869565 0.65217391 0.52173913] - -mean value: 0.7184782608695651 - -key: train_recall -value: [0.75471698 0.74056604 0.75471698 0.69811321 0.75943396 0.74528302 - 0.74178404 0.76056338 0.75586854 0.75586854] - -mean value: 0.7466914695721499 - -key: test_accuracy -value: [0.79166667 0.72916667 0.72340426 0.74468085 0.59574468 0.68085106 - 0.63829787 0.59574468 0.59574468 0.65957447] - -mean value: 0.6754875886524822 - -key: train_accuracy -value: [0.72169811 0.71698113 0.72235294 0.68941176 0.66117647 0.66588235 - 0.71294118 0.70352941 0.67529412 0.69176471] - -mean value: 0.6961032186459489 - -key: test_roc_auc -value: [0.79166667 0.72916667 0.72192029 0.74365942 0.5923913 0.67844203 - 0.63858696 0.59601449 0.59692029 0.6567029 ] - -mean value: 0.6745471014492754 - -key: train_roc_auc -value: [0.72169811 0.71698113 0.72242891 0.68943219 0.66140712 0.66606874 - 0.71287315 0.7033949 0.67510408 0.69161352] - -mean value: 0.696100186021791 - -key: test_jcc -value: [0.65517241 0.60606061 0.59375 0.61290323 0.48648649 0.55882353 - 0.46875 0.42424242 0.44117647 0.42857143] - -mean value: 0.5275936584960501 - -key: train_jcc -value: [0.57553957 0.566787 0.57553957 0.52857143 0.52786885 0.52666667 - 0.56428571 0.5625 0.53846154 0.55136986] - -mean value: 0.5517590203758819 - -key: TN -value: 149 - -mean value: 149.0 - -key: FP -value: 66 - -mean value: 66.0 - -key: FN -value: 87 - -mean value: 87.0 - -key: TP -value: 170 - -mean value: 170.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.61 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01839757 0.0254066 0.02377772 0.02271795 0.02948928 0.02503753 - 0.02638626 0.02682137 0.02718377 0.02452397] - -mean value: 0.02497420310974121 - -key: score_time -value: [0.00948644 0.01130271 0.0117383 0.01180124 0.01184773 0.01192331 - 0.01176596 0.01187563 0.01197004 0.01187992] - -mean value: 0.011559128761291504 - -key: test_mcc -value: [0.9591663 0.91986621 0.95825929 0.91804649 0.95825929 0.95825929 - 0.95833333 0.91833182 0.40290954 0.91804649] - -mean value: 0.8869478067076157 - -key: train_mcc -value: [0.98594778 0.97668677 0.98135106 0.97674215 0.985981 0.99063227 - 0.98598008 0.98598008 0.51345631 0.98134942] - -mean value: 0.9364106910161993 - -key: test_fscore -value: [0.97959184 0.96 0.97959184 0.96 0.97959184 0.97959184 - 0.9787234 0.95833333 0.57142857 0.95454545] - -mean value: 0.9301398110501454 - -key: train_fscore -value: [0.99297424 0.98834499 0.99065421 0.98834499 0.99297424 0.99530516 - 0.99300699 0.99300699 0.58940397 0.99069767] - -mean value: 0.9514713458310984 - -key: test_precision -value: [0.96 0.92307692 0.96 0.92307692 0.96 0.96 - 0.95833333 0.92 0.83333333 1. ] - -mean value: 0.9397820512820513 - -key: train_precision -value: [0.98604651 0.97695853 0.98148148 0.97695853 0.98604651 0.99065421 - 0.98611111 0.98611111 1. 0.98156682] - -mean value: 0.9851934803534738 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.43478261 0.91304348] - -mean value: 0.9347826086956521 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.41784038 1. ] - -mean value: 0.9417840375586854 - -key: test_accuracy -value: [0.97916667 0.95833333 0.9787234 0.95744681 0.9787234 0.9787234 - 0.9787234 0.95744681 0.68085106 0.95744681] - -mean value: 0.9405585106382979 - -key: train_accuracy -value: [0.99292453 0.98820755 0.99058824 0.98823529 0.99294118 0.99529412 - 0.99294118 0.99294118 0.70823529 0.99058824] - -mean value: 0.9632896781354052 - -key: test_roc_auc -value: [0.97916667 0.95833333 0.97826087 0.95652174 0.97826087 0.97826087 - 0.97916667 0.95833333 0.67572464 0.95652174] - -mean value: 0.9398550724637682 - -key: train_roc_auc -value: [0.99292453 0.98820755 0.99061033 0.98826291 0.99295775 0.99530516 - 0.99292453 0.99292453 0.70892019 0.99056604] - -mean value: 0.963360350783949 - -key: test_jcc -value: [0.96 0.92307692 0.96 0.92307692 0.96 0.96 - 0.95833333 0.92 0.4 0.91304348] - -mean value: 0.887753065774805 - -key: train_jcc -value: [0.98604651 0.97695853 0.98148148 0.97695853 0.98604651 0.99065421 - 0.98611111 0.98611111 0.41784038 0.98156682] - -mean value: 0.9269775179121591 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 221 - -mean value: 221.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01678896 0.01649952 0.01591492 0.01822972 0.01876831 0.01699924 - 0.01787925 0.01754594 0.01638031 0.0158534 ] - -mean value: 0.017085957527160644 - -key: score_time -value: [0.01152825 0.01157141 0.01164985 0.01152349 0.01157451 0.0115478 - 0.01151896 0.01157546 0.01188564 0.01181984] - -mean value: 0.01161952018737793 - -key: test_mcc -value: [0.91666667 0.91986621 0.91485507 0.91804649 0.95825929 0.91485507 - 0.91485507 0.64404991 0.83303222 0.5646597 ] - -mean value: 0.8499145717462007 - -key: train_mcc -value: [0.95389198 0.95389198 0.94849477 0.98135106 0.98135106 0.95790971 - 0.96706971 0.81557001 0.92474809 0.47960727] - -mean value: 0.896388565223219 - -key: test_fscore -value: [0.95833333 0.96 0.95833333 0.96 0.97959184 0.95833333 - 0.95652174 0.76923077 0.91666667 0.64705882] - -mean value: 0.9064069835291976 - -key: train_fscore -value: [0.97695853 0.97695853 0.97374702 0.99065421 0.99065421 0.97852029 - 0.98352941 0.89175258 0.96226415 0.55405405] - -mean value: 0.9279092959090567 - -key: test_precision -value: [0.95833333 0.92307692 0.95833333 0.92307692 0.96 0.95833333 - 0.95652174 0.9375 0.88 1. ] - -mean value: 0.9455175585284282 - -key: train_precision -value: [0.95495495 0.95495495 0.98550725 0.98148148 0.98148148 0.99033816 - 0.98584906 0.98857143 0.96682464 0.98795181] - -mean value: 0.9777915220454773 - -key: test_recall -value: [0.95833333 1. 0.95833333 1. 1. 0.95833333 - 0.95652174 0.65217391 0.95652174 0.47826087] - -mean value: 0.8918478260869567 - -key: train_recall -value: [1. 1. 0.96226415 1. 1. 0.96698113 - 0.98122066 0.81220657 0.95774648 0.38497653] - -mean value: 0.9065395517760653 - -key: test_accuracy -value: [0.95833333 0.95833333 0.95744681 0.95744681 0.9787234 0.95744681 - 0.95744681 0.80851064 0.91489362 0.74468085] - -mean value: 0.9193262411347517 - -key: train_accuracy -value: [0.97641509 0.97641509 0.97411765 0.99058824 0.99058824 0.97882353 - 0.98352941 0.90117647 0.96235294 0.68941176] - -mean value: 0.9423418423973363 - -key: test_roc_auc -value: [0.95833333 0.95833333 0.95742754 0.95652174 0.97826087 0.95742754 - 0.95742754 0.80525362 0.91576087 0.73913043] - -mean value: 0.9183876811594203 - -key: train_roc_auc -value: [0.97641509 0.97641509 0.97408982 0.99061033 0.99061033 0.97879573 - 0.98353486 0.90138631 0.96236381 0.69012977] - -mean value: 0.9424351138276197 - -key: test_jcc -value: [0.92 0.92307692 0.92 0.92307692 0.96 0.92 - 0.91666667 0.625 0.84615385 0.47826087] - -mean value: 0.8432235228539577 - -key: train_jcc -value: [0.95495495 0.95495495 0.94883721 0.98148148 0.98148148 0.95794393 - 0.96759259 0.80465116 0.92727273 0.38317757] - -mean value: 0.886234806015832 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 211 - -mean value: 211.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.6 - -Accuracy on Blind test: 0.88 - -Running classifier: 18 -Model_name: AdaBoost Classifier -Model func: AdaBoostClassifier(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.22470832 0.20434523 0.20656443 0.20643067 0.20798802 0.20693946 - 0.21186805 0.20994353 0.21088386 0.21096039] - -mean value: 0.21006319522857667 - -key: score_time -value: [0.01509857 0.01499677 0.01507831 0.01502872 0.01508951 0.01544309 - 0.01509452 0.01587653 0.01597238 0.01538754] - -mean value: 0.015306591987609863 - -key: test_mcc -value: [1. 0.9591663 0.95833333 0.87318841 1. 0.91804649 - 0.95833333 1. 0.91833182 0.95825929] - -mean value: 0.954365898313708 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.9787234 0.9787234 0.93617021 1. 0.96 - 0.9787234 1. 0.95833333 0.97777778] - -mean value: 0.9768451536643026 - -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. 0.95652174 1. 0.92307692 - 0.95833333 1. 0.92 1. ] - -mean value: 0.9757931995540691 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95833333 0.95833333 0.91666667 1. 1. - 1. 1. 1. 0.95652174] - -mean value: 0.9789855072463769 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97916667 0.9787234 0.93617021 1. 0.95744681 - 0.9787234 1. 0.95744681 0.9787234 ] - -mean value: 0.9766400709219859 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97916667 0.97916667 0.9365942 1. 0.95652174 - 0.97916667 1. 0.95833333 0.97826087] - -mean value: 0.9767210144927535 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.95833333 0.95833333 0.88 1. 0.92307692 - 0.95833333 1. 0.92 0.95652174] - -mean value: 0.955459866220736 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.04095602 0.05502033 0.05681586 0.06088853 0.05758262 0.05486798 - 0.07888913 0.07030559 0.06846881 0.05174327] - -mean value: 0.05955381393432617 - -key: score_time -value: [0.01834273 0.01944971 0.02726221 0.01841426 0.01991796 0.02375793 - 0.03805304 0.02091193 0.02872539 0.02132058] - -mean value: 0.023615574836730956 - -key: test_mcc -value: [1. 1. 0.87979456 0.91485507 0.95833333 0.91804649 - 0.95833333 0.95825929 0.87318841 0.91804649] - -mean value: 0.9378856980120954 - -key: train_mcc -value: [0.99529409 0.99061012 0.99530506 1. 1. 1. - 0.99530506 0.99530516 0.99530506 0.99530506] - -mean value: 0.9962429618909043 - -key: test_fscore -value: [1. 1. 0.93333333 0.95833333 0.9787234 0.96 - 0.9787234 0.97777778 0.93617021 0.95454545] - -mean value: 0.9677606920266495 - -key: train_fscore -value: [0.99764706 0.99530516 0.99763593 1. 1. 1. - 0.99765808 0.99764706 0.99765808 0.99765808] - -mean value: 0.9981209454648333 - -key: test_precision -value: [1. 1. 1. 0.95833333 1. 0.92307692 - 0.95833333 1. 0.91666667 1. ] - -mean value: 0.9756410256410255 - -key: train_precision -value: [0.99530516 0.99065421 1. 1. 1. 1. - 0.9953271 1. 0.9953271 0.9953271 ] - -mean value: 0.997194067833794 - -key: test_recall -value: [1. 1. 0.875 0.95833333 0.95833333 1. - 1. 0.95652174 0.95652174 0.91304348] - -mean value: 0.9617753623188406 - -key: train_recall -value: [1. 1. 0.99528302 1. 1. 1. - 1. 0.99530516 1. 1. ] - -mean value: 0.9990588183187175 - -key: test_accuracy -value: [1. 1. 0.93617021 0.95744681 0.9787234 0.95744681 - 0.9787234 0.9787234 0.93617021 0.95744681] - -mean value: 0.9680851063829788 - -key: train_accuracy -value: [0.99764151 0.99528302 0.99764706 1. 1. 1. - 0.99764706 0.99764706 0.99764706 0.99764706] - -mean value: 0.9981159822419533 - -key: test_roc_auc -value: [1. 1. 0.9375 0.95742754 0.97916667 0.95652174 - 0.97916667 0.97826087 0.9365942 0.95652174] - -mean value: 0.9681159420289855 - -key: train_roc_auc -value: [0.99764151 0.99528302 0.99764151 1. 1. 1. - 0.99764151 0.99765258 0.99764151 0.99764151] - -mean value: 0.998114314819736 - -key: test_jcc -value: [1. 1. 0.875 0.92 0.95833333 0.92307692 - 0.95833333 0.95652174 0.88 0.91304348] - -mean value: 0.9384308807134895 - -key: train_jcc -value: [0.99530516 0.99065421 0.99528302 1. 1. 1. - 0.9953271 0.99530516 0.9953271 0.9953271 ] - -mean value: 0.9962528861525113 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 227 - -mean value: 227.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.12498903 0.13653755 0.14616394 0.13924384 0.14144564 0.10727668 - 0.14344168 0.10445166 0.16795921 0.14799881] - -mean value: 0.13595080375671387 - -key: score_time -value: [0.02295804 0.02838063 0.02303791 0.02607822 0.02326965 0.01507163 - 0.02788782 0.01454759 0.02842116 0.01466465] - -mean value: 0.02243173122406006 - -key: test_mcc -value: [0.54594868 0.71393289 0.66801039 0.75474102 0.57427536 0.65942029 - 0.36612568 0.59613578 0.74682354 0.61706091] - -mean value: 0.6242474541008988 - -key: train_mcc -value: [0.9435642 0.94377428 0.92942088 0.94356964 0.95294092 0.93883291 - 0.93907982 0.9435291 0.95310772 0.92950167] - -mean value: 0.9417321140302078 - -key: test_fscore -value: [0.78431373 0.8627451 0.84615385 0.88461538 0.79166667 0.83333333 - 0.69387755 0.80769231 0.86363636 0.8 ] - -mean value: 0.8168034276647722 - -key: train_fscore -value: [0.97196262 0.97209302 0.96453901 0.97156398 0.97641509 0.96926714 - 0.96983759 0.97183099 0.97674419 0.96503497] - -mean value: 0.9709288586036555 - -key: test_precision -value: [0.74074074 0.81481481 0.78571429 0.82142857 0.79166667 0.83333333 - 0.65384615 0.72413793 0.9047619 0.81818182] - -mean value: 0.7888626220522772 - -key: train_precision -value: [0.96296296 0.9587156 0.96682464 0.97619048 0.97641509 0.97156398 - 0.9587156 0.97183099 0.96774194 0.95833333] - -mean value: 0.9669294606478728 - -key: test_recall -value: [0.83333333 0.91666667 0.91666667 0.95833333 0.79166667 0.83333333 - 0.73913043 0.91304348 0.82608696 0.7826087 ] - -mean value: 0.8510869565217393 - -key: train_recall -value: [0.98113208 0.98584906 0.96226415 0.96698113 0.97641509 0.96698113 - 0.98122066 0.97183099 0.98591549 0.97183099] - -mean value: 0.975042076357516 - -key: test_accuracy -value: [0.77083333 0.85416667 0.82978723 0.87234043 0.78723404 0.82978723 - 0.68085106 0.78723404 0.87234043 0.80851064] - -mean value: 0.8093085106382978 - -key: train_accuracy -value: [0.97169811 0.97169811 0.96470588 0.97176471 0.97647059 0.96941176 - 0.96941176 0.97176471 0.97647059 0.96470588] - -mean value: 0.9708102108768035 - -key: test_roc_auc -value: [0.77083333 0.85416667 0.82789855 0.87047101 0.78713768 0.82971014 - 0.68206522 0.78985507 0.87137681 0.80797101] - -mean value: 0.8091485507246376 - -key: train_roc_auc -value: [0.97169811 0.97169811 0.96470015 0.97175348 0.97647046 0.96940606 - 0.96938391 0.97176455 0.97644831 0.96468908] - -mean value: 0.970801222428913 - -key: test_jcc -value: [0.64516129 0.75862069 0.73333333 0.79310345 0.65517241 0.71428571 - 0.53125 0.67741935 0.76 0.66666667] - -mean value: 0.6935012911171142 - -key: train_jcc -value: [0.94545455 0.94570136 0.93150685 0.94470046 0.95391705 0.94036697 - 0.94144144 0.94520548 0.95454545 0.93243243] - -mean value: 0.9435272044104863 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 35 - -mean value: 35.0 - -key: FN -value: 55 - -mean value: 55.0 - -key: TP -value: 201 - -mean value: 201.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.11 - -Accuracy on Blind test: 0.68 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.84788013 0.82336068 0.82631397 0.82613254 0.83214068 0.82518101 - 0.84120417 0.82660365 0.82076645 0.8304646 ] - -mean value: 0.8300047874450683 - -key: score_time -value: [0.00928783 0.00998926 0.00946808 0.00917816 0.00920033 0.00939775 - 0.00932908 0.00917006 0.00909996 0.00968266] - -mean value: 0.009380316734313965 - -key: test_mcc -value: [1. 1. 0.95825929 0.91485507 0.95833333 0.91804649 - 0.91833182 0.95825929 0.91833182 0.95825929] - -mean value: 0.9502676415549521 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.97959184 0.95833333 0.9787234 0.96 - 0.95833333 0.97777778 0.95833333 0.97777778] - -mean value: 0.9748870796545569 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.96 0.95833333 1. 0.92307692 - 0.92 1. 0.92 1. ] - -mean value: 0.9681410256410257 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95833333 0.95833333 1. - 1. 0.95652174 1. 0.95652174] - -mean value: 0.9829710144927537 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.9787234 0.95744681 0.9787234 0.95744681 - 0.95744681 0.9787234 0.95744681 0.9787234 ] - -mean value: 0.9744680851063829 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.97826087 0.95742754 0.97916667 0.95652174 - 0.95833333 0.97826087 0.95833333 0.97826087] - -mean value: 0.9744565217391304 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.96 0.92 0.95833333 0.92307692 - 0.92 0.95652174 0.92 0.95652174] - -mean value: 0.9514453734671127 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 232 - -mean value: 232.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis() - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02677035 0.02802253 0.02849007 0.02773046 0.02764273 0.02758217 - 0.02782226 0.04608202 0.03823519 0.04036951] - -mean value: 0.03187472820281982 - -key: score_time -value: [0.01233816 0.01240921 0.01265359 0.01354814 0.01499772 0.01506662 - 0.01908278 0.01705933 0.01990175 0.0129528 ] - -mean value: 0.015001010894775391 - -key: test_mcc -value: [0.9591663 1. 0.91804649 0.95825929 0.95825929 1. - 0.91833182 0.91833182 1. 0.95825929] - -mean value: 0.9588654314414964 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 1. 0.96 0.97959184 0.97959184 1. - 0.95833333 0.95833333 1. 0.97777778] - -mean value: 0.9793219954648527 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 1. 0.92307692 0.96 0.96 1. - 0.92 0.92 1. 1. ] - -mean value: 0.9643076923076922 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.95652174] - -mean value: 0.9956521739130435 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 1. 0.95744681 0.9787234 0.9787234 1. - 0.95744681 0.95744681 1. 0.9787234 ] - -mean value: 0.9787677304964539 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.95652174 0.97826087 0.97826087 1. - 0.95833333 0.95833333 1. 0.97826087] - -mean value: 0.978713768115942 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.92307692 0.96 0.96 1. - 0.92 0.92 1. 0.95652174] - -mean value: 0.9599598662207358 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 227 - -mean value: 227.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.1 - -Accuracy on Blind test: 0.76 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0153389 0.01543188 0.0312953 0.03483987 0.03201866 0.03430152 - 0.03340054 0.03347754 0.03270888 0.03707957] - -mean value: 0.02998926639556885 - -key: score_time -value: [0.01203394 0.01204205 0.02133679 0.02125907 0.02122617 0.02147245 - 0.01996827 0.02125692 0.01610708 0.02232218] - -mean value: 0.01890249252319336 - -key: test_mcc -value: [0.9591663 0.9591663 0.91804649 0.91804649 0.95825929 0.91804649 - 1. 0.91833182 0.91833182 1. ] - -mean value: 0.9467395023615742 - -key: train_mcc -value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 - 0.9767396 0.98134942 0.98598008 0.9767396 ] - -mean value: 0.979036867016519 - -key: test_fscore -value: [0.97959184 0.97959184 0.96 0.96 0.97959184 0.96 - 1. 0.95833333 0.95833333 1. ] - -mean value: 0.973544217687075 - -key: train_fscore -value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 - 0.98839907 0.99069767 0.99300699 0.98839907] - -mean value: 0.9895191175872012 - -key: test_precision -value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 - 1. 0.92 0.92 1. ] - -mean value: 0.9489230769230769 - -key: train_precision -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97706422 0.98156682 0.98611111 0.97706422] - -mean value: 0.9792603436100032 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 - 1. 0.95744681 0.95744681 1. ] - -mean value: 0.9724290780141844 - -key: train_accuracy -value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 - 0.98823529 0.99058824 0.99294118 0.98823529] - -mean value: 0.9894062153163151 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.95652174 0.95652174 0.97826087 0.95652174 - 1. 0.95833333 0.95833333 1. ] - -mean value: 0.9722826086956522 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 - 0.98820755 0.99056604 0.99292453 0.98820755] - -mean value: 0.9894067233590219 - -key: test_jcc -value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 - 1. 0.92 0.92 1. ] - -mean value: 0.9489230769230769 - -key: train_jcc -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97706422 0.98156682 0.98611111 0.97706422] - -mean value: 0.9792603436100032 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 0 - -mean value: 0.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 236 - -mean value: 236.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.93 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: 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 - smnc_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: 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 - smnc_CV['Resampling'] = rs_smnc -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: 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 - smnc_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: 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 - smnc_BT['Resampling'] = rs_smnc -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.26434445 0.32251453 0.15206838 0.24239111 0.28772497 0.20101905 - 0.18936872 0.27143574 0.1601553 0.28233242] - -mean value: 0.237335467338562 - -key: score_time -value: [0.02213812 0.02163053 0.01248097 0.02126622 0.02346134 0.01217699 - 0.02457643 0.02345705 0.01250052 0.02009153] - -mean value: 0.019377970695495607 - -key: test_mcc -value: [0.9591663 0.9591663 0.91804649 0.91804649 0.95825929 0.91804649 - 1. 0.91833182 0.91833182 1. ] - -mean value: 0.9467395023615742 - -key: train_mcc -value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 - 0.9767396 0.98134942 0.98598008 0.9767396 ] - -mean value: 0.979036867016519 - -key: test_fscore -value: [0.97959184 0.97959184 0.96 0.96 0.97959184 0.96 - 1. 0.95833333 0.95833333 1. ] - -mean value: 0.973544217687075 - -key: train_fscore -value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 - 0.98839907 0.99069767 0.99300699 0.98839907] - -mean value: 0.9895191175872012 - -key: test_precision -value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 - 1. 0.92 0.92 1. ] - -mean value: 0.9489230769230769 - -key: train_precision -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97706422 0.98156682 0.98611111 0.97706422] - -mean value: 0.9792603436100032 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 - 1. 0.95744681 0.95744681 1. ] - -mean value: 0.9724290780141844 - -key: train_accuracy -value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 - 0.98823529 0.99058824 0.99294118 0.98823529] - -mean value: 0.9894062153163151 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.95652174 0.95652174 0.97826087 0.95652174 - 1. 0.95833333 0.95833333 1. ] - -mean value: 0.9722826086956522 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 - 0.98820755 0.99056604 0.99292453 0.98820755] - -mean value: 0.9894067233590219 - -key: test_jcc -value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 - 1. 0.92 0.92 1. ] - -mean value: 0.9489230769230769 - -key: train_jcc -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97706422 0.98156682 0.98611111 0.97706422] - -mean value: 0.9792603436100032 - -key: TN -value: 223 - -mean value: 223.0 - -key: FP -value: 0 - -mean value: 0.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 236 - -mean value: 236.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.93 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.04575014 0.03561878 0.03952789 0.03515625 0.03371787 0.03832698 - 0.03580189 0.03812385 0.03297853 0.03531456] - -mean value: 0.0370316743850708 - -key: score_time -value: [0.01466537 0.0143652 0.01469421 0.01486397 0.01183844 0.01454234 - 0.01436806 0.01438093 0.01181459 0.01306009] - -mean value: 0.013859319686889648 - -key: test_mcc -value: [0.87576054 0.87576054 0.91833182 0.87917396 0.91833182 0.78804348 - 0.82971014 0.91833182 0.87318841 0.82971014] - -mean value: 0.8706342679311371 - -key: train_mcc -value: [0.93877324 0.93877324 0.95311186 0.9576579 0.94824493 0.92942088 - 0.94824493 0.95298209 0.96235273 0.94356964] - -mean value: 0.9473131457063468 - -key: test_fscore -value: [0.93877551 0.93617021 0.95652174 0.94117647 0.95652174 0.89361702 - 0.91304348 0.95833333 0.93617021 0.91304348] - -mean value: 0.9343373195716769 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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_fscore -value: [0.96955504 0.96955504 0.97663551 0.97882353 0.97399527 0.96453901 - 0.97423888 0.97663551 0.98122066 0.97196262] - -mean value: 0.9737161056644213 - -key: test_precision -value: [0.92 0.95652174 1. 0.88888889 1. 0.91304348 - 0.91304348 0.92 0.91666667 0.91304348] - -mean value: 0.9341207729468598 - -key: train_precision -value: [0.9627907 0.9627907 0.96759259 0.97652582 0.97630332 0.96682464 - 0.97196262 0.97209302 0.98122066 0.96744186] - -mean value: 0.9705545929443339 - -key: test_recall -value: [0.95833333 0.91666667 0.91666667 1. 0.91666667 0.875 - 0.91304348 1. 0.95652174 0.91304348] - -mean value: 0.9365942028985508 - -key: train_recall -value: [0.97641509 0.97641509 0.98584906 0.98113208 0.97169811 0.96226415 - 0.97652582 0.98122066 0.98122066 0.97652582] - -mean value: 0.976926654265214 - -key: test_accuracy -value: [0.9375 0.9375 0.95744681 0.93617021 0.95744681 0.89361702 - 0.91489362 0.95744681 0.93617021 0.91489362] - -mean value: 0.9343085106382978 - -key: train_accuracy -value: [0.96933962 0.96933962 0.97647059 0.97882353 0.97411765 0.96470588 - 0.97411765 0.97647059 0.98117647 0.97176471] - -mean value: 0.973632630410655 - -key: test_roc_auc -value: [0.9375 0.9375 0.95833333 0.93478261 0.95833333 0.89402174 - 0.91485507 0.95833333 0.9365942 0.91485507] - -mean value: 0.9345108695652172 - -key: train_roc_auc -value: [0.96933962 0.96933962 0.9764926 0.97882895 0.97411197 0.96470015 - 0.97411197 0.97645939 0.98117637 0.97175348] - -mean value: 0.9736314111081583 - -key: test_jcc -value: [0.88461538 0.88 0.91666667 0.88888889 0.91666667 0.80769231 - 0.84 0.92 0.88 0.84 ] - -mean value: 0.8774529914529914 - -key: train_jcc -value: [0.94090909 0.94090909 0.9543379 0.95852535 0.94930876 0.93150685 - 0.94977169 0.9543379 0.96313364 0.94545455] - -mean value: 0.9488194807107753 - -key: TN -value: 220 - -mean value: 220.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 221 - -mean value: 221.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.89 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.8614018 0.77416611 0.771734 0.95600319 0.76238513 0.77007675 - 0.90413976 0.76445174 0.77944541 0.86435747] - -mean value: 0.8208161354064941 - -key: score_time -value: [0.01345611 0.01349068 0.01326203 0.01331139 0.01330566 0.01315808 - 0.01339507 0.0134182 0.01340961 0.01469398] - -mean value: 0.013490080833435059 - -key: test_mcc -value: [1. 0.9591663 0.95833333 0.91804649 0.95825929 0.95825929 - 0.95833333 0.95833333 0.91833182 0.95833333] - -mean value: 0.9545396535412275 - -key: train_mcc -value: [0.98594778 0.98594778 0.985981 1. 0.985981 1. - 1. 0.98598008 0.99063185 1. ] - -mean value: 0.992046948762083 - -key: test_fscore -value: [1. 0.97959184 0.9787234 0.96 0.97959184 0.97959184 - 0.9787234 0.9787234 0.95833333 0.9787234 ] - -mean value: 0.9772002460558692 - -key: train_fscore -value: [0.99297424 0.99297424 0.99297424 1. 0.99297424 1. - 1. 0.99300699 0.9953271 1. ] - -mean value: 0.9960231051314243 - -key: test_precision -value: [1. 0.96 1. 0.92307692 0.96 0.96 - 0.95833333 0.95833333 0.92 0.95833333] - -mean value: 0.9598076923076924 - -key: train_precision -value: [0.98604651 0.98604651 0.98604651 1. 0.98604651 1. - 1. 0.98611111 0.99069767 1. ] - -mean value: 0.9920994832041344 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97916667 0.9787234 0.95744681 0.9787234 0.9787234 - 0.9787234 0.9787234 0.95744681 0.9787234 ] - -mean value: 0.9766400709219859 - -key: train_accuracy -value: [0.99292453 0.99292453 0.99294118 1. 0.99294118 1. - 1. 0.99294118 0.99529412 1. ] - -mean value: 0.9959966703662598 - -key: test_roc_auc -value: [1. 0.97916667 0.97916667 0.95652174 0.97826087 0.97826087 - 0.97916667 0.97916667 0.95833333 0.97916667] - -mean value: 0.9767210144927537 - -key: train_roc_auc -value: [0.99292453 0.99292453 0.99295775 1. 0.99295775 1. - 1. 0.99292453 0.99528302 1. ] - -mean value: 0.9959972096731331 - -key: test_jcc -value: [1. 0.96 0.95833333 0.92307692 0.96 0.96 - 0.95833333 0.95833333 0.92 0.95833333] - -mean value: 0.9556410256410258 - -key: train_jcc -value: [0.98604651 0.98604651 0.98604651 1. 0.98604651 1. - 1. 0.98611111 0.99069767 1. ] - -mean value: 0.9920994832041344 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.93 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01357484 0.01358032 0.01013279 0.00968552 0.00968051 0.00999379 - 0.0106163 0.0103898 0.01098871 0.01038814] - -mean value: 0.010903072357177735 - -key: score_time -value: [0.01210165 0.01052999 0.00906539 0.0088408 0.00886726 0.00886559 - 0.00925517 0.00962162 0.0091269 0.00949335] - -mean value: 0.009576773643493653 - -key: test_mcc -value: [0.62554324 0.51639778 0.66801039 0.62296012 0.79308818 0.54211097 - 0.4121128 0.45173716 0.48913043 0.58127976] - -mean value: 0.5702370834691168 - -key: train_mcc -value: [0.66037736 0.67007974 0.68970563 0.62608853 0.6525241 0.66709242 - 0.69884615 0.68471521 0.64738091 0.66604642] - -mean value: 0.6662856464972534 - -key: test_fscore -value: [0.80851064 0.71428571 0.84615385 0.8 0.90196078 0.79245283 - 0.72 0.73469388 0.73913043 0.76190476] - -mean value: 0.781909288747823 - -key: train_fscore -value: [0.83018868 0.8372093 0.84651163 0.77866667 0.82949309 0.84070796 - 0.85046729 0.84235294 0.82678984 0.83526682] - -mean value: 0.8317654218882868 - -key: test_precision -value: [0.82608696 0.83333333 0.78571429 0.85714286 0.85185185 0.72413793 - 0.66666667 0.69230769 0.73913043 0.84210526] - -mean value: 0.7818477272513412 - -key: train_precision -value: [0.83018868 0.82568807 0.83486239 0.89570552 0.81081081 0.79166667 - 0.84651163 0.84433962 0.81363636 0.82568807] - -mean value: 0.8319097824490095 - -key: test_recall -value: [0.79166667 0.625 0.91666667 0.75 0.95833333 0.875 - 0.7826087 0.7826087 0.73913043 0.69565217] - -mean value: 0.7916666666666666 - -key: train_recall -value: [0.83018868 0.8490566 0.85849057 0.68867925 0.8490566 0.89622642 - 0.85446009 0.84037559 0.84037559 0.84507042] - -mean value: 0.8351979803348393 - -key: test_accuracy -value: [0.8125 0.75 0.82978723 0.80851064 0.89361702 0.76595745 - 0.70212766 0.72340426 0.74468085 0.78723404] - -mean value: 0.7817819148936171 - -key: train_accuracy -value: [0.83018868 0.83490566 0.84470588 0.80470588 0.82588235 0.83058824 - 0.84941176 0.84235294 0.82352941 0.83294118] - -mean value: 0.8319211986681465 - -key: test_roc_auc -value: [0.8125 0.75 0.82789855 0.80978261 0.89221014 0.76358696 - 0.70380435 0.72463768 0.74456522 0.78532609] - -mean value: 0.7814311594202898 - -key: train_roc_auc -value: [0.83018868 0.83490566 0.84473824 0.80443352 0.82593675 0.83074232 - 0.84939986 0.8423576 0.82348968 0.83291257] - -mean value: 0.8319104880857473 - -key: test_jcc -value: [0.67857143 0.55555556 0.73333333 0.66666667 0.82142857 0.65625 - 0.5625 0.58064516 0.5862069 0.61538462] - -mean value: 0.6456542228782217 - -key: train_jcc -value: [0.70967742 0.72 0.73387097 0.63755459 0.70866142 0.72519084 - 0.7398374 0.72764228 0.70472441 0.71713147] - -mean value: 0.7124290787616256 - -key: TN -value: 182 - -mean value: 182.0 - -key: FP -value: 49 - -mean value: 49.0 - -key: FN -value: 54 - -mean value: 54.0 - -key: TP -value: 187 - -mean value: 187.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.24 - -Accuracy on Blind test: 0.68 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01090574 0.01078701 0.01001048 0.00974464 0.00974822 0.00984216 - 0.0097878 0.01360297 0.00971985 0.00994635] - -mean value: 0.01040952205657959 - -key: score_time -value: [0.00956082 0.00963616 0.00891781 0.00883818 0.00886345 0.00890994 - 0.01088643 0.01318765 0.00889277 0.00891685] - -mean value: 0.009661006927490234 - -key: test_mcc -value: [0.58536941 0.34426519 0.45455353 0.36612568 0.28602655 0.53734864 - 0.53734864 0.44646172 0.44874504 0.44874504] - -mean value: 0.44549894448919625 - -key: train_mcc -value: [0.51000165 0.5311697 0.49206576 0.50190856 0.5353736 0.49927415 - 0.465305 0.4867519 0.50899412 0.51535889] - -mean value: 0.50462033294049 - -key: test_fscore -value: [0.8 0.61904762 0.75471698 0.66666667 0.60465116 0.75555556 - 0.7755102 0.71111111 0.69767442 0.69767442] - -mean value: 0.7082608137594661 - -key: train_fscore -value: [0.74879227 0.75124378 0.74038462 0.74271845 0.75794621 0.73316708 - 0.72058824 0.72361809 0.73945409 0.75650118] - -mean value: 0.7414414008247979 - -key: test_precision -value: [0.76923077 0.72222222 0.68965517 0.71428571 0.68421053 0.80952381 - 0.73076923 0.72727273 0.75 0.75 ] - -mean value: 0.7347170172034057 - -key: train_precision -value: [0.76732673 0.79473684 0.75490196 0.765 0.78680203 0.77777778 - 0.75384615 0.77837838 0.78421053 0.76190476] - -mean value: 0.7724885164242559 - -key: test_recall -value: [0.83333333 0.54166667 0.83333333 0.625 0.54166667 0.70833333 - 0.82608696 0.69565217 0.65217391 0.65217391] - -mean value: 0.6909420289855073 - -key: train_recall -value: [0.73113208 0.71226415 0.72641509 0.72169811 0.73113208 0.69339623 - 0.69014085 0.67605634 0.69953052 0.75117371] - -mean value: 0.7132939144299761 - -key: test_accuracy -value: [0.79166667 0.66666667 0.72340426 0.68085106 0.63829787 0.76595745 - 0.76595745 0.72340426 0.72340426 0.72340426] - -mean value: 0.7203014184397164 - -key: train_accuracy -value: [0.75471698 0.76415094 0.74588235 0.75058824 0.76705882 0.74823529 - 0.73176471 0.74117647 0.75294118 0.75764706] - -mean value: 0.7514162042175362 - -key: test_roc_auc -value: [0.79166667 0.66666667 0.72101449 0.68206522 0.64039855 0.76721014 - 0.76721014 0.72282609 0.72192029 0.72192029] - -mean value: 0.7202898550724637 - -key: train_roc_auc -value: [0.75471698 0.76415094 0.74583666 0.75052042 0.76697449 0.74810656 - 0.73186288 0.74133006 0.75306715 0.75766233] - -mean value: 0.7514228452475862 - -key: test_jcc -value: [0.66666667 0.44827586 0.60606061 0.5 0.43333333 0.60714286 - 0.63333333 0.55172414 0.53571429 0.53571429] - -mean value: 0.5517965367965367 - -key: train_jcc -value: [0.5984556 0.60159363 0.58778626 0.59073359 0.61023622 0.57874016 - 0.56321839 0.56692913 0.58661417 0.60836502] - -mean value: 0.5892672169084556 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 73 - -mean value: 73.0 - -key: FN -value: 59 - -mean value: 59.0 - -key: TP -value: 163 - -mean value: 163.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.03 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.02165532 0.00901961 0.01004386 0.00999832 0.01018262 0.01008296 - 0.00927544 0.01035762 0.0091846 0.01037288] - -mean value: 0.011017322540283203 - -key: score_time -value: [0.01685476 0.01256466 0.01446056 0.01262641 0.01310086 0.01363015 - 0.01195931 0.01255512 0.01195836 0.01244521] - -mean value: 0.01321554183959961 - -key: test_mcc -value: [0.37532595 0.54213748 0.55422693 0.59180008 0.49183384 0.49454913 - 0.47117841 0.53734864 0.33346345 0.64834149] - -mean value: 0.5040205390753851 - -key: train_mcc -value: [0.69567994 0.68472112 0.68044488 0.66992632 0.69556724 0.68709327 - 0.69628526 0.70058158 0.69538058 0.71556485] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - -mean value: 0.6921245003133125 - -key: test_fscore -value: [0.69387755 0.7755102 0.8 0.81481481 0.73913043 0.76923077 - 0.75471698 0.7755102 0.69230769 0.83018868] - -mean value: 0.7645287330696917 - -key: train_fscore -value: [0.85462555 0.85042735 0.84810127 0.84279476 0.8539823 0.85097192 - 0.85526316 0.85775862 0.85462555 0.86451613] - -mean value: 0.8533066608144875 - -key: test_precision -value: [0.68 0.76 0.70967742 0.73333333 0.77272727 0.71428571 - 0.66666667 0.73076923 0.62068966 0.73333333] - -mean value: 0.7121482625642803 - -key: train_precision -value: [0.80165289 0.77734375 0.76717557 0.78455285 0.80416667 0.78486056 - 0.80246914 0.79282869 0.80497925 0.79761905] - -mean value: 0.7917648406837627 - -key: test_recall -value: [0.70833333 0.79166667 0.91666667 0.91666667 0.70833333 0.83333333 - 0.86956522 0.82608696 0.7826087 0.95652174] - -mean value: 0.8309782608695653 - -key: train_recall -value: [0.91509434 0.93867925 0.94811321 0.91037736 0.91037736 0.92924528 - 0.91549296 0.9342723 0.91079812 0.94366197] - -mean value: 0.9256112144565506 - -key: test_accuracy -value: [0.6875 0.77083333 0.76595745 0.78723404 0.74468085 0.74468085 - 0.72340426 0.76595745 0.65957447 0.80851064] - -mean value: 0.7458333333333333 - -key: train_accuracy -value: [0.84433962 0.83490566 0.83058824 0.83058824 0.84470588 0.83764706 - 0.84470588 0.84470588 0.84470588 0.85176471] - -mean value: 0.840865704772475 - -key: test_roc_auc -value: [0.6875 0.77083333 0.76268116 0.78442029 0.74547101 0.74275362 - 0.72644928 0.76721014 0.66213768 0.8115942 ] - -mean value: 0.7461050724637681 - -key: train_roc_auc -value: [0.84433962 0.83490566 0.83086412 0.83077553 0.84486004 0.83786208 - 0.84453893 0.84449464 0.84455 0.85154797] - -mean value: 0.8408738595092569 - -key: test_jcc -value: [0.53125 0.63333333 0.66666667 0.6875 0.5862069 0.625 - 0.60606061 0.63333333 0.52941176 0.70967742] - -mean value: 0.6208440020006385 - -key: train_jcc -value: [0.74615385 0.73977695 0.73626374 0.72830189 0.74517375 0.7406015 - 0.74712644 0.7509434 0.74615385 0.76136364] - -mean value: 0.7441858985341548 - -key: TN -value: 156 - -mean value: 156.0 - -key: FP -value: 40 - -mean value: 40.0 - -key: FN -value: 80 - -mean value: 80.0 - -key: TP -value: 196 - -mean value: 196.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.04 - -Accuracy on Blind test: 0.57 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02334023 0.01960754 0.01953459 0.01931095 0.0190649 0.0195787 - 0.01955843 0.01917648 0.01953411 0.01945996] - -mean value: 0.01981658935546875 - -key: score_time -value: [0.01181769 0.0121665 0.01234674 0.01127791 0.01118779 0.01132178 - 0.01210308 0.01147628 0.01121712 0.01117063] - -mean value: 0.011608552932739259 - -key: test_mcc -value: [0.79235477 0.63902148 0.66121206 0.57713344 0.7196797 0.74773263 - 0.65942029 0.70289855 0.7876601 0.62966842] - -mean value: 0.6916781436411534 - -key: train_mcc -value: [0.81626479 0.83052137 0.81668554 0.77495013 0.82595793 0.81668554 - 0.82596948 0.8406091 0.84941093 0.78867849] - -mean value: 0.8185733306374121 - -key: test_fscore -value: [0.89795918 0.79069767 0.84 0.7826087 0.8372093 0.86956522 - 0.82608696 0.85106383 0.88888889 0.7804878 ] - -mean value: 0.8364567553537045 - -key: train_fscore -value: [0.90692124 0.9138756 0.90692124 0.88405797 0.91211401 0.90692124 - 0.91252955 0.91866029 0.92488263 0.89260143] - -mean value: 0.9079485205500408 - -key: test_precision -value: [0.88 0.89473684 0.80769231 0.81818182 0.94736842 0.90909091 - 0.82608696 0.83333333 0.90909091 0.88888889] - -mean value: 0.87144703859578 - -key: train_precision -value: [0.9178744 0.92718447 0.9178744 0.90594059 0.91866029 0.9178744 - 0.91904762 0.93658537 0.92488263 0.90776699] - -mean value: 0.9193691139866482 - -key: test_recall -value: [0.91666667 0.70833333 0.875 0.75 0.75 0.83333333 - 0.82608696 0.86956522 0.86956522 0.69565217] - -mean value: 0.8094202898550724 - -key: train_recall -value: [0.89622642 0.9009434 0.89622642 0.86320755 0.90566038 0.89622642 - 0.90610329 0.90140845 0.92488263 0.87793427] - -mean value: 0.896881920453539 - -key: test_accuracy -value: [0.89583333 0.8125 0.82978723 0.78723404 0.85106383 0.87234043 - 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-key: FN -value: 29 - -mean value: 29.0 - -key: TP -value: 191 - -mean value: 191.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.38 - -Accuracy on Blind test: 0.79 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.38979816 1.88678241 1.85302711 1.83790088 1.86066747 1.58438802 - 1.89584589 1.81168294 1.87610316 1.85480547] - -mean value: 1.7851001501083374 - -key: score_time -value: [0.0121727 0.01449251 0.01456285 0.01456189 0.01456237 0.01217008 - 0.0145936 0.01232529 0.01464391 0.01461911] - -mean value: 0.013870429992675782 - -key: test_mcc -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -[0.9591663 0.9591663 0.91485507 0.87917396 1. 0.87917396 - 0.87979456 0.91833182 0.91833182 0.95833333] - -mean value: 0.9266327142476708 - -key: train_mcc -value: [0.98594778 0.99061012 1. 0.99530516 0.99530516 0.99530516 - 0.99530506 0.99063185 0.99063185 0.99063185] - 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-mean value: 0.9617907801418442 - -key: train_accuracy -value: [0.99292453 0.99528302 1. 0.99764706 0.99764706 0.99764706 - 0.99764706 0.99529412 0.99529412 0.99529412] - -mean value: 0.9964678135405107 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.95742754 0.93478261 1. 0.93478261 - 0.9375 0.95833333 0.95833333 0.97916667] - -mean value: 0.9618659420289856 - -key: train_roc_auc -value: [0.99292453 0.99528302 1. 0.99765258 0.99765258 0.99765258 - 0.99764151 0.99528302 0.99528302 0.99528302] - -mean value: 0.996465585968642 - -key: test_jcc -value: [0.96 0.96 0.92 0.88888889 1. 0.88888889 - 0.88461538 0.92 0.92 0.95833333] - -mean value: 0.9300726495726496 - -key: train_jcc -value: [0.98604651 0.99065421 1. 0.99530516 0.99530516 0.99530516 - 0.9953271 0.99069767 0.99069767 0.99069767] - -mean value: 0.9930036336252683 - -key: TN -value: 219 - -mean value: 219.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.69 - -Accuracy on Blind test: 0.89 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02435589 0.01885676 0.01731181 0.01705623 0.01679707 0.01767826 - 0.01765943 0.01808286 0.01462436 0.01631927] - -mean value: 0.01787419319152832 - -key: score_time -value: [0.0118289 0.00927591 0.00880337 0.00892401 0.00854754 0.00902939 - 0.00864697 0.00859952 0.00880909 0.00884032] - -mean value: 0.009130501747131347 - -key: test_mcc -value: [0.9591663 0.9591663 0.95833333 0.91804649 1. 0.91804649 - 0.95833333 0.95833333 0.91833182 1. ] - -mean value: 0.9547757417604974 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 0.97959184 0.9787234 0.96 1. 0.96 - 0.9787234 0.9787234 0.95833333 1. ] - -mean value: 0.9773687219568679 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 0.96 1. 0.92307692 1. 0.92307692 - 0.95833333 0.95833333 0.92 1. ] - -mean value: 0.9602820512820512 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.97916667 0.9787234 0.95744681 1. 0.95744681 - 0.9787234 0.9787234 0.95744681 1. ] - -mean value: 0.9766843971631205 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.97916667 0.95652174 1. 0.95652174 - 0.97916667 0.97916667 0.95833333 1. ] - -mean value: 0.9767210144927537 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 0.96 0.95833333 0.92307692 1. 0.92307692 - 0.95833333 0.95833333 0.92 1. ] - -mean value: 0.9561153846153847 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.92 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.11192679 0.11509895 0.11650324 0.11855555 0.11755848 0.11214924 - 0.11129642 0.11113 0.11224771 0.11210442] - -mean value: 0.1138570785522461 - -key: score_time -value: [0.01835728 0.0193646 0.0183847 0.01931548 0.01799941 0.01849747 - 0.01754355 0.01751018 0.01742411 0.01748538] - -mean value: 0.018188214302062987 - -key: test_mcc -value: [1. 0.87576054 0.87318841 1. 0.95833333 0.95825929 - 0.91833182 0.87318841 0.91833182 0.95833333] - -mean value: 0.9333726950567002 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.93617021 0.93617021 1. 0.9787234 0.97959184 - 0.95833333 0.93617021 0.95833333 0.9787234 ] - -mean value: 0.9662215950209871 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95652174 0.95652174 1. 1. 0.96 - 0.92 0.91666667 0.92 0.95833333] - -mean value: 0.958804347826087 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.91666667 0.91666667 1. 0.95833333 1. - 1. 0.95652174 1. 1. ] - -mean value: 0.9748188405797101 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9375 0.93617021 1. 0.9787234 0.9787234 - 0.95744681 0.93617021 0.95744681 0.9787234 ] - -mean value: 0.9660904255319149 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9375 0.9365942 1. 0.97916667 0.97826087 - 0.95833333 0.9365942 0.95833333 0.97916667] - -mean value: 0.9663949275362318 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.88 0.88 1. 0.95833333 0.96 - 0.92 0.88 0.92 0.95833333] - -mean value: 0.9356666666666668 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 230 - -mean value: 230.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.4 - -Accuracy on Blind test: 0.83 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00963283 0.00949097 0.00959659 0.01043701 0.01073122 0.00953889 - 0.00993323 0.00965047 0.00965047 0.01083255] - -mean value: 0.009949421882629395 - -key: score_time -value: [0.00854564 0.0085845 0.00857139 0.00928402 0.00901961 0.00862765 - 0.00914121 0.00857615 0.00881815 0.00919294] - -mean value: 0.008836126327514649 - -key: test_mcc -value: [0.84515425 0.70710678 0.7876601 0.91804649 0.73387289 0.91804649 - 0.87979456 0.84254172 0.80641033 0.87979456] - -mean value: 0.8318428175010515 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.92307692 0.85714286 0.89795918 0.96 0.87272727 0.96 - 0.93877551 0.92 0.90196078 0.93877551] - -mean value: 0.917041804134241 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.75 0.88 0.92307692 0.77419355 0.92307692 - 0.88461538 0.85185185 0.82142857 0.88461538] - -mean value: 0.8550001444194993 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.91666667 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9916666666666666 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.91666667 0.83333333 0.89361702 0.95744681 0.85106383 0.95744681 - 0.93617021 0.91489362 0.89361702 0.93617021] - -mean value: 0.9090425531914892 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.91666667 0.83333333 0.89311594 0.95652174 0.84782609 0.95652174 - 0.9375 0.91666667 0.89583333 0.9375 ] - -mean value: 0.9091485507246377 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85714286 0.75 0.81481481 0.92307692 0.77419355 0.92307692 - 0.88461538 0.85185185 0.82142857 0.88461538] - -mean value: 0.8484816259009808 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 41 - -mean value: 41.0 - -key: TP -value: 234 - -mean value: 234.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.19 - -Accuracy on Blind test: 0.74 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.57338715 1.54828095 1.5590179 1.55561304 1.52688169 1.5289619 - 1.55676508 1.60693359 1.57568049 1.57357478] - -mean value: 1.5605096578598023 - -key: score_time -value: [0.09393835 0.09686708 0.09653354 0.09693933 0.09467936 0.09551907 - 0.09138155 0.09113574 0.0913012 0.09131646] - -mean value: 0.09396116733551026 - -key: test_mcc -value: [1. 0.91986621 0.95833333 0.95825929 1. 0.95825929 - 0.95833333 1. 0.91833182 1. ] - -mean value: 0.9671383281416119 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.95652174 0.9787234 0.97959184 1. 0.97959184 - 0.9787234 1. 0.95833333 1. ] - -mean value: 0.9831485554443795 - -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. 0.96 1. 0.96 - 0.95833333 1. 0.92 1. ] - -mean value: 0.9798333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.91666667 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9875 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.95833333 0.9787234 0.9787234 1. 0.9787234 - 0.9787234 1. 0.95744681 1. ] - -mean value: 0.9830673758865249 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95833333 0.97916667 0.97826087 1. 0.97826087 - 0.97916667 1. 0.95833333 1. ] - -mean value: 0.9831521739130435 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.91666667 0.95833333 0.96 1. 0.96 - 0.95833333 1. 0.92 1. ] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -mean value: 0.9673333333333334 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 231 - -mean value: 231.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 233 - -mean value: 233.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.9 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.92463493 0.91933489 0.90347314 0.90513873 0.96518564 0.93658257 - 0.92193604 0.89846778 0.90099645 0.93485951] - -mean value: 0.9210609674453736 - -key: score_time -value: [0.22926402 0.18491554 0.19461608 0.21672845 0.18792701 0.20230293 - 0.20627904 0.17378759 0.19786906 0.21795678] - -mean value: 0.2011646509170532 - -key: test_mcc -value: [0.9591663 0.91986621 0.95833333 0.95825929 0.91833182 0.95825929 - 0.95833333 0.91833182 0.91833182 1. ] - -mean value: 0.9467213227939449 - -key: train_mcc -value: [0.99061012 0.98586002 0.99063227 0.99063227 0.98589335 0.99058818 - 0.98598008 0.98134942 0.99063185 0.99063185] - -mean value: 0.9882809416190621 - -key: test_fscore -value: [0.97959184 0.95652174 0.9787234 0.97959184 0.95652174 0.97959184 - 0.9787234 0.95833333 0.95833333 1. ] - -mean value: 0.9725932463642257 - -key: train_fscore -value: [0.99530516 0.99294118 0.99530516 0.99530516 0.99294118 0.99528302 - 0.99300699 0.99069767 0.9953271 0.9953271 ] - -mean value: 0.9941439737799922 - -key: test_precision -value: [0.96 1. 1. 0.96 1. 0.96 - 0.95833333 0.92 0.92 1. ] - -mean value: 0.9678333333333333 - -key: train_precision -value: [0.99065421 0.99061033 0.99065421 0.99065421 0.99061033 0.99528302 - 0.98611111 0.98156682 0.99069767 0.99069767] - -mean value: 0.9897539573192169 - -key: test_recall -value: [1. 0.91666667 0.95833333 1. 0.91666667 1. - 1. 1. 1. 1. ] - -mean value: 0.9791666666666666 - -key: train_recall -value: [1. 0.99528302 1. 1. 0.99528302 0.99528302 - 1. 1. 1. 1. ] - -mean value: 0.9985849056603774 - -key: test_accuracy -value: [0.97916667 0.95833333 0.9787234 0.9787234 0.95744681 0.9787234 - 0.9787234 0.95744681 0.95744681 1. ] - -mean value: 0.9724734042553191 - -key: train_accuracy -value: [0.99528302 0.99292453 0.99529412 0.99529412 0.99294118 0.99529412 - 0.99294118 0.99058824 0.99529412 0.99529412] - -mean value: 0.9941148723640401 - -key: test_roc_auc -value: [0.97916667 0.95833333 0.97916667 0.97826087 0.95833333 0.97826087 - 0.97916667 0.95833333 0.95833333 1. ] - -mean value: 0.9727355072463769 - -key: train_roc_auc -value: [0.99528302 0.99292453 0.99530516 0.99530516 0.99294667 0.99529409 - 0.99292453 0.99056604 0.99528302 0.99528302] - -mean value: 0.9941115244928691 - -key: test_jcc -value: [0.96 0.91666667 0.95833333 0.96 0.91666667 0.96 - 0.95833333 0.92 0.92 1. ] - -mean value: 0.9470000000000001 - -key: train_jcc -value: [0.99065421 0.98598131 0.99065421 0.99065421 0.98598131 0.99061033 - 0.98611111 0.98156682 0.99069767 0.99069767] - -mean value: 0.9883608842508176 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.91 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.06745553 0.05746651 0.05764294 0.05900121 0.05914187 0.05862188 - 0.05908942 0.0608089 0.05977464 0.06338382] - -mean value: 0.06023867130279541 - -key: score_time -value: [0.0108037 0.01069331 0.01060319 0.01084948 0.01076293 0.01081204 - 0.01059222 0.01054955 0.01077199 0.01091146] - -mean value: 0.010734987258911134 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.95825929 1. 0.91804649 - 1. 1. 0.91833182 1. ] - -mean value: 0.9712137244006647 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.97959184 1. 0.96 - 1. 1. 0.95833333 1. ] - -mean value: 0.985624041105804 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 1. 1. 0.96 1. 0.92307692 - 1. 1. 0.92 1. ] - -mean value: 0.9763076923076923 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.9787234 1. 0.95744681 - 1. 1. 0.95744681 1. ] - -mean value: 0.9851507092198583 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.97826087 1. 0.95652174 - 1. 1. 0.95833333 1. ] - -mean value: 0.9851449275362318 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.95833333 0.96 1. 0.92307692 - 1. 1. 0.92 1. ] - -mean value: 0.9721410256410257 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03682876 0.07974267 0.05892038 0.04035926 0.04105043 0.06086922 - 0.0796988 0.049788 0.03683519 0.03657722] - -mean value: 0.052066993713378903 - -key: score_time -value: [0.02253962 0.0241096 0.01197815 0.01230359 0.01230454 0.01259184 - 0.02162075 0.0121882 0.01248527 0.01208448] - -mean value: 0.01542060375213623 - -key: test_mcc -value: [0.9591663 0.8819171 0.83243502 0.84147165 0.95825929 0.91804649 - 0.91833182 0.95833333 0.87979456 0.84254172] - -mean value: 0.899029728310231 - -key: train_mcc -value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 - 0.97215032 0.9767396 0.98134942 0.97215032] - -mean value: 0.9771949634162567 - -key: test_fscore -value: [0.97959184 0.94117647 0.92 0.92307692 0.97959184 0.96 - 0.95833333 0.9787234 0.93877551 0.92 ] - -mean value: 0.9499269314927281 - -key: train_fscore -value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 - 0.98611111 0.98839907 0.99069767 0.98611111] - -mean value: 0.9886007333161488 - -key: test_precision -value: [0.96 0.88888889 0.88461538 0.85714286 0.96 0.92307692 - 0.92 0.95833333 0.88461538 0.85185185] - -mean value: 0.9088524623524623 - -key: train_precision -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97260274 0.97706422 0.98156682 0.97260274] - -mean value: 0.9774633584257492 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.9375 0.91489362 0.91489362 0.9787234 0.95744681 - 0.95744681 0.9787234 0.93617021 0.91489362] - -mean value: 0.9469858156028368 - -key: train_accuracy -value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 - 0.98588235 0.98823529 0.99058824 0.98588235] - -mean value: 0.988465038845727 - -key: test_roc_auc -value: [0.97916667 0.9375 0.91394928 0.91304348 0.97826087 0.95652174 - 0.95833333 0.97916667 0.9375 0.91666667] - -mean value: 0.9470108695652174 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 - 0.98584906 0.98820755 0.99056604 0.98584906] - -mean value: 0.988463327132607 - -key: test_jcc -value: [0.96 0.88888889 0.85185185 0.85714286 0.96 0.92307692 - 0.92 0.95833333 0.88461538 0.85185185] - -mean value: 0.905576109076109 - -key: train_jcc -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 - 0.97260274 0.97706422 0.98156682 0.97260274] - -mean value: 0.9774633584257492 - -key: TN -value: 212 - -mean value: 212.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.9 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01340699 0.01024008 0.00958824 0.0094533 0.01009774 0.00923991 - 0.00937963 0.00952458 0.00938821 0.00920439] - -mean value: 0.009952306747436523 - -key: score_time -value: [0.01165438 0.00972748 0.00859261 0.00851846 0.00869226 0.00849319 - 0.00853133 0.00925541 0.00852942 0.0085206 ] - -mean value: 0.009051513671875 - -key: test_mcc -value: [0.50709255 0.44194174 0.53176131 0.36231884 0.23369565 0.36116212 - 0.5326087 0.58428436 0.4899891 0.36265926] - -mean value: 0.44075136351965527 - -key: train_mcc -value: [0.49490154 0.49625189 0.48033439 0.48845269 0.54396959 0.48764745 - 0.5548004 0.53004837 0.53464113 0.52575614] - -mean value: 0.5136803598430164 - -key: test_fscore -value: [0.72727273 0.65 0.7755102 0.68085106 0.625 0.69387755 - 0.76595745 0.8 0.72727273 0.65116279] - -mean value: 0.7096904510983468 - -key: train_fscore -value: [0.72727273 0.7244898 0.72319202 0.71794872 0.76626506 0.73607748 - 0.76772616 0.75961538 0.76258993 0.75544794] - -mean value: 0.7440625219101846 - -key: test_precision -value: [0.8 0.8125 0.76 0.69565217 0.625 0.68 - 0.75 0.74074074 0.76190476 0.7 ] - -mean value: 0.7325797676558546 - -key: train_precision -value: [0.7826087 0.78888889 0.76719577 0.78651685 0.78325123 0.75621891 - 0.80102041 0.77832512 0.77941176 0.78 ] - -mean value: 0.7803437638691002 - -key: test_recall -value: [0.66666667 0.54166667 0.79166667 0.66666667 0.625 0.70833333 - 0.7826087 0.86956522 0.69565217 0.60869565] - -mean value: 0.6956521739130435 - -key: train_recall -value: [0.67924528 0.66981132 0.68396226 0.66037736 0.75 0.71698113 - 0.7370892 0.74178404 0.74647887 0.73239437] - -mean value: 0.7118123837363806 - -key: test_accuracy -value: [0.75 0.70833333 0.76595745 0.68085106 0.61702128 0.68085106 - 0.76595745 0.78723404 0.74468085 0.68085106] - -mean value: 0.7181737588652483 - -key: train_accuracy -value: [0.74528302 0.74528302 0.73882353 0.74117647 0.77176471 0.74352941 - 0.77647059 0.76470588 0.76705882 0.76235294] - -mean value: 0.7556448390677025 - -key: test_roc_auc -value: [0.75 0.70833333 0.76539855 0.68115942 0.61684783 0.68025362 - 0.76630435 0.78894928 0.74365942 0.67934783] - -mean value: 0.7180253623188406 - -key: train_roc_auc -value: [0.74528302 0.74528302 0.73869475 0.7409868 0.77171362 0.74346709 - 0.77656347 0.76475994 0.76710736 0.7624236 ] - -mean value: 0.7556282664540703 - -key: test_jcc -value: [0.57142857 0.48148148 0.63333333 0.51612903 0.45454545 0.53125 - 0.62068966 0.66666667 0.57142857 0.48275862] - -mean value: 0.5529711387004211 - -key: train_jcc -value: [0.57142857 0.568 0.56640625 0.56 0.62109375 0.58237548 - 0.62301587 0.6124031 0.61627907 0.60700389] - -mean value: 0.5928005984964867 - -key: TN -value: 175 - -mean value: 175.0 - -key: FP -value: 72 - -mean value: 72.0 - -key: FN -value: 61 - -mean value: 61.0 - -key: TP -value: 164 - -mean value: 164.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.29 - -Accuracy on Blind test: 0.72 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01910973 0.02240539 0.02354193 0.02688932 0.02919555 0.02573752 - 0.0232811 0.02313137 0.02699065 0.0267837 ] - -mean value: 0.02470662593841553 - -key: score_time -value: [0.00947046 0.01099229 0.01149726 0.01185322 0.01161885 0.0115099 - 0.01150322 0.01151037 0.01153064 0.0115459 ] - -mean value: 0.011303210258483886 - -key: test_mcc -value: [0.9591663 0.91986621 0.95833333 0.91804649 0.91833182 0.91804649 - 0.95833333 0.95833333 0.38895926 0.91804649] - -mean value: 0.8815463075360693 - -key: train_mcc -value: [0.97208751 0.98130676 0.985981 0.99063227 0.98589335 0.97674215 - 0.97648101 0.98598008 0.45056456 0.95311186] - -mean value: 0.9258780536304961 - -key: test_fscore -value: [0.97959184 0.96 0.9787234 0.96 0.95652174 0.96 - 0.9787234 0.9787234 0.5 0.95454545] - -mean value: 0.9206829243176541 - -key: train_fscore -value: [0.98604651 0.99065421 0.99297424 0.99530516 0.99294118 0.98834499 - 0.9882904 0.99300699 0.50526316 0.97630332] - -mean value: 0.9409130151809825 - -key: test_precision -value: [0.96 0.92307692 1. 0.92307692 1. 0.92307692 - 0.95833333 0.95833333 0.88888889 1. ] - -mean value: 0.9534786324786324 - -key: train_precision -value: [0.97247706 0.98148148 0.98604651 0.99065421 0.99061033 0.97695853 - 0.98598131 0.98611111 1. 0.98564593] - -mean value: 0.9855966469457847 - -key: test_recall -value: [1. 1. 0.95833333 1. 0.91666667 1. - 1. 1. 0.34782609 0.91304348] - -mean value: 0.913586956521739 - -key: train_recall -value: [1. 1. 1. 1. 0.99528302 1. - 0.99061033 1. 0.33802817 0.96713615] - -mean value: 0.9291057666755249 - -key: test_accuracy -value: [0.97916667 0.95833333 0.9787234 0.95744681 0.95744681 0.95744681 - 0.9787234 0.9787234 0.65957447 0.95744681] - -mean value: 0.9363031914893618 - -key: train_accuracy -value: [0.98584906 0.99056604 0.99294118 0.99529412 0.99294118 0.98823529 - 0.98823529 0.99294118 0.66823529 0.97647059] - -mean value: 0.9571709211986681 - -key: test_roc_auc -value: [0.97916667 0.95833333 0.97916667 0.95652174 0.95833333 0.95652174 - 0.97916667 0.97916667 0.65307971 0.95652174] - -mean value: 0.9355978260869566 - -key: train_roc_auc -value: [0.98584906 0.99056604 0.99295775 0.99530516 0.99294667 0.98826291 - 0.98822969 0.99292453 0.66901408 0.9764926 ] - -mean value: 0.9572548498538399 - -key: test_jcc -value: [0.96 0.92307692 0.95833333 0.92307692 0.91666667 0.92307692 - 0.95833333 0.95833333 0.33333333 0.91304348] - -mean value: 0.8767274247491639 - -key: train_jcc -value: [0.97247706 0.98148148 0.98604651 0.99065421 0.98598131 0.97695853 - 0.97685185 0.98611111 0.33802817 0.9537037 ] - -mean value: 0.9148293932374637 - -key: TN -value: 226 - -mean value: 226.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 216 - -mean value: 216.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.82 - -Accuracy on Blind test: 0.94 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01817179 0.01651382 0.02004027 0.0179913 0.01704335 0.01677728 - 0.01724768 0.01694465 0.01827598 0.01648617] - -mean value: 0.01754922866821289 - -key: score_time -value: [0.01151705 0.01156211 0.0117805 0.01157784 0.01157165 0.01157284 - 0.01156044 0.01203346 0.01163268 0.01156354] - -mean value: 0.011637210845947266 - -key: test_mcc -value: [0.9591663 0.84515425 0.91485507 0.87917396 0.87979456 0.91804649 - 0.73692303 0.5732115 0.91833182 0.26673253] - -mean value: 0.7891389538646197 - -key: train_mcc -value: [0.98130676 0.95389198 0.97674215 0.98135106 0.95765696 0.94504426 - 0.85916204 0.71378158 0.99063185 0.55691515] - -mean value: 0.8916483794877926 - -key: test_fscore -value: [0.97959184 0.92307692 0.95833333 0.94117647 0.93333333 0.96 - 0.86792453 0.79310345 0.95833333 0.23076923] - -mean value: 0.8545642437746832 - -key: train_fscore -value: [0.99065421 0.97695853 0.98834499 0.99065421 0.9787234 0.97247706 - 0.930131 0.86060606 0.9953271 0.64984227] - -mean value: 0.9333718832451053 - -key: test_precision -value: [0.96 0.85714286 0.95833333 0.88888889 1. 0.92307692 - 0.76666667 0.65714286 0.92 1. ] - -mean value: 0.8931251526251526 - -key: train_precision -value: [0.98148148 0.95495495 0.97695853 0.98148148 0.98104265 0.94642857 - 0.86938776 0.75531915 0.99069767 0.99038462] - -mean value: 0.942813686256198 - -key: test_recall -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -[1. 1. 0.95833333 1. 0.875 1. - 1. 1. 1. 0.13043478] - -mean value: 0.8963768115942029 - -key: train_recall -value: [1. 1. 1. 1. 0.97641509 1. - 1. 1. 1. 0.48356808] - -mean value: 0.9459983169456991 - -key: test_accuracy -value: [0.97916667 0.91666667 0.95744681 0.93617021 0.93617021 0.95744681 - 0.85106383 0.74468085 0.95744681 0.57446809] - -mean value: 0.8810726950354612 - -key: train_accuracy -value: [0.99056604 0.97641509 0.98823529 0.99058824 0.97882353 0.97176471 - 0.92470588 0.83764706 0.99529412 0.73882353] - -mean value: 0.9392863485016647 - -key: test_roc_auc -value: [0.97916667 0.91666667 0.95742754 0.93478261 0.9375 0.95652174 - 0.85416667 0.75 0.95833333 0.56521739] - -mean value: 0.8809782608695652 - -key: train_roc_auc -value: [0.99056604 0.97641509 0.98826291 0.99061033 0.97881788 0.97183099 - 0.9245283 0.83726415 0.99528302 0.73942555] - -mean value: 0.9393004251926653 - -key: test_jcc -value: [0.96 0.85714286 0.92 0.88888889 0.875 0.92307692 - 0.76666667 0.65714286 0.92 0.13043478] - -mean value: 0.7898352975526889 - -key: train_jcc -value: [0.98148148 0.95495495 0.97695853 0.98148148 0.95833333 0.94642857 - 0.86938776 0.75531915 0.99069767 0.48130841] - -mean value: 0.8896351337697215 - -key: TN -value: 204 - -mean value: 204.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 32 - -mean value: 32.0 - -key: TP -value: 212 - -mean value: 212.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.91 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.16770864 0.14934564 0.14924645 0.1498208 0.14950657 0.14951468 - 0.15319133 0.1516273 0.15055633 0.15102768] - -mean value: 0.152154541015625 - -key: score_time -value: [0.01524901 0.01509094 0.0154016 0.01516843 0.01500225 0.01513433 - 0.01561236 0.01504779 0.01508641 0.0153389 ] - -mean value: 0.015213203430175782 - -key: test_mcc -value: [0.9591663 0.9591663 0.91485507 0.95825929 1. 0.91804649 - 1. 1. 0.91833182 1. ] - -mean value: 0.9627825287799624 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 0.97959184 0.95833333 0.97959184 1. 0.96 - 1. 1. 0.95833333 1. ] - -mean value: 0.9815442176870748 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 0.96 0.95833333 0.96 1. 0.92307692 - 1. 1. 0.92 1. ] - -mean value: 0.9681410256410257 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 0.97916667 0.95744681 0.9787234 1. 0.95744681 - 1. 1. 0.95744681 1. ] - -mean value: 0.9809397163120568 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.95742754 0.97826087 1. 0.95652174 - 1. 1. 0.95833333 1. ] - -mean value: 0.9808876811594203 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 0.96 0.92 0.96 1. 0.92307692 - 1. 1. 0.92 1. ] - -mean value: 0.9643076923076924 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.93 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.04388475 0.07044077 0.06779981 0.05081892 0.0576489 0.04585266 - 0.04862523 0.05665183 0.06696415 0.0718863 ] - -mean value: 0.058057332038879396 - -key: score_time -value: [0.02634716 0.03491426 0.01997948 0.02338076 0.02006984 0.02316165 - 0.0306685 0.02496171 0.02564192 0.03851509] - -mean value: 0.02676403522491455 - -key: test_mcc -value: [0.9591663 0.9591663 0.91833182 0.95825929 1. 0.91804649 - 0.95833333 0.95833333 0.91833182 1. ] - -mean value: 0.9547968702932919 - -key: train_mcc -value: [0.98117574 0.99061012 1. 0.99530506 0.99530506 0.99530506 - 0.99530516 0.99530516 0.99058818 0.99058818] - -mean value: 0.9929487736067821 - -key: test_fscore -value: [0.97959184 0.97959184 0.95652174 0.97959184 1. 0.96 - 0.9787234 0.9787234 0.95833333 1. ] - -mean value: 0.9771077391178489 - -key: train_fscore -value: [0.99061033 0.99530516 1. 0.99763593 0.99763593 0.99763593 - 0.99764706 0.99764706 0.99530516 0.99530516] - -mean value: 0.9964727740661742 - -key: test_precision -value: [0.96 0.96 1. 0.96 1. 0.92307692 - 0.95833333 0.95833333 0.92 1. ] - -mean value: 0.963974358974359 - -key: train_precision -value: [0.98598131 0.99065421 1. 1. 1. 1. - 1. 1. 0.99530516 0.99530516] - -mean value: 0.9967245842657189 - -key: test_recall -value: [1. 1. 0.91666667 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9916666666666666 - -key: train_recall -value: [0.99528302 1. 1. 0.99528302 0.99528302 0.99528302 - 0.99530516 0.99530516 0.99530516 0.99530516] - -mean value: 0.9962352732748692 - -key: test_accuracy -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -[0.97916667 0.97916667 0.95744681 0.9787234 1. 0.95744681 - 0.9787234 0.9787234 0.95744681 1. ] - -mean value: 0.9766843971631205 - -key: train_accuracy -value: [0.99056604 0.99528302 1. 0.99764706 0.99764706 0.99764706 - 0.99764706 0.99764706 0.99529412 0.99529412] - -mean value: 0.996467258601554 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.95833333 0.97826087 1. 0.95652174 - 0.97916667 0.97916667 0.95833333 1. ] - -mean value: 0.9768115942028986 - -key: train_roc_auc -value: [0.99056604 0.99528302 1. 0.99764151 0.99764151 0.99764151 - 0.99765258 0.99765258 0.99529409 0.99529409] - -mean value: 0.9964666932412085 - -key: test_jcc -value: [0.96 0.96 0.91666667 0.96 1. 0.92307692 - 0.95833333 0.95833333 0.92 1. ] - -mean value: 0.9556410256410256 - -key: train_jcc -value: [0.98139535 0.99065421 1. 0.99528302 0.99528302 0.99528302 - 0.99530516 0.99530516 0.99065421 0.99065421] - -mean value: 0.992981735090191 - -key: TN -value: 227 - -mean value: 227.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 234 - -mean value: 234.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.92 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.12236857 0.10208821 0.11127877 0.08595467 0.113271 0.17878008 - 0.09545183 0.11677861 0.08445358 0.15772724] - -mean value: 0.11681525707244873 - -key: score_time -value: [0.02326179 0.01438665 0.02932024 0.01423693 0.02347517 0.03062963 - 0.01435137 0.02352238 0.01427579 0.02511668] - -mean value: 0.021257662773132326 - -key: test_mcc -value: [0.797925 0.83333333 0.74682354 0.8047833 0.57713344 0.87917396 - 0.68369322 0.67037015 0.61775362 0.87979456] - -mean value: 0.7490784119796873 - -key: train_mcc -value: [0.95806958 0.94491118 0.953621 0.92992145 0.92507925 0.92992145 - 0.94908163 0.94874597 0.92991066 0.9253549 ] - -mean value: 0.9394617058916153 - -key: test_fscore -value: [0.90196078 0.91666667 0.88 0.90566038 0.7826087 0.94117647 - 0.84615385 0.84 0.80851064 0.93877551] - -mean value: 0.8761512989235092 - -key: train_fscore -value: [0.97911833 0.97247706 0.97685185 0.96519722 0.9627907 0.96519722 - 0.97471264 0.97459584 0.96535797 0.96313364] - -mean value: 0.9699432469622048 - -key: test_precision -value: [0.85185185 0.91666667 0.84615385 0.82758621 0.81818182 0.88888889 - 0.75862069 0.77777778 0.79166667 0.88461538] - -mean value: 0.8362009797354626 - -key: train_precision -value: [0.96347032 0.94642857 0.95909091 0.94977169 0.94954128 0.94977169 - 0.95495495 0.95909091 0.95 0.94570136] - -mean value: 0.9527821685065214 - -key: test_recall -value: [0.95833333 0.91666667 0.91666667 1. 0.75 1. - 0.95652174 0.91304348 0.82608696 1. ] - -mean value: 0.9237318840579709 - -key: train_recall -value: [0.99528302 1. 0.99528302 0.98113208 0.97641509 0.98113208 - 0.99530516 0.99061033 0.98122066 0.98122066] - -mean value: 0.9877602090530606 - -key: test_accuracy -value: [0.89583333 0.91666667 0.87234043 0.89361702 0.78723404 0.93617021 - 0.82978723 0.82978723 0.80851064 0.93617021] - -mean value: 0.8706117021276596 - -key: train_accuracy -value: [0.97877358 0.97169811 0.97647059 0.96470588 0.96235294 0.96470588 - 0.97411765 0.97411765 0.96470588 0.96235294] - -mean value: 0.9694001109877914 - -key: test_roc_auc -value: [0.89583333 0.91666667 0.87137681 0.89130435 0.78804348 0.93478261 - 0.83242754 0.83152174 0.80887681 0.9375 ] - -mean value: 0.8708333333333333 - -key: train_roc_auc -value: [0.97877358 0.97169811 0.97651475 0.96474444 0.96238595 0.96474444 - 0.97406768 0.97407875 0.96466693 0.96230844] - -mean value: 0.9693983080875188 - -key: test_jcc -value: [0.82142857 0.84615385 0.78571429 0.82758621 0.64285714 0.88888889 - 0.73333333 0.72413793 0.67857143 0.88461538] - -mean value: 0.7833287019493916 - -key: train_jcc -value: [0.95909091 0.94642857 0.95475113 0.93273543 0.92825112 0.93273543 - 0.95067265 0.95045045 0.93303571 0.92888889] - -mean value: 0.9417040284200333 - -key: TN -value: 193 - -mean value: 193.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 218 - -mean value: 218.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.07 - -Accuracy on Blind test: 0.68 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.57085848 0.55797744 0.54987121 0.55645442 0.55771732 0.56728172 - 0.56024218 0.55635619 0.56131268 0.5593884 ] - -mean value: 0.5597460031509399 - -key: score_time -value: [0.00907898 0.00924873 0.0092032 0.00910139 0.00924087 0.01006508 - 0.01004052 0.00920558 0.00913525 0.00903201] - -mean value: 0.009335160255432129 - -key: test_mcc -value: [1. 0.9591663 0.95833333 0.95825929 1. 0.91804649 - 0.95833333 1. 0.91833182 1. ] - -mean value: 0.967047057733998 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97959184 0.9787234 0.97959184 1. 0.96 - 0.9787234 1. 0.95833333 1. ] - -mean value: 0.983496381531336 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.96 1. 0.96 1. 0.92307692 - 0.95833333 1. 0.92 1. ] - -mean value: 0.9721410256410257 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95833333 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9958333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97916667 0.9787234 0.9787234 1. 0.95744681 - 0.9787234 1. 0.95744681 1. ] - -mean 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/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -0.98302304964539 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97916667 0.97916667 0.97826087 1. 0.95652174 - 0.97916667 1. 0.95833333 1. ] - -mean value: 0.9830615942028986 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.96 0.95833333 0.96 1. 0.92307692 - 0.95833333 1. 0.92 1. ] - -mean value: 0.9679743589743589 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 229 - -mean value: 229.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02258968 0.02946877 0.02697659 0.02740622 0.04164696 0.03037763 - 0.04019165 0.04432678 0.02746391 0.02753806] - -mean value: 0.03179862499237061 - -key: score_time -value: [0.01176834 0.01242232 0.01303983 0.01382589 0.01480007 0.01367903 - 0.01615787 0.02514005 0.014853 0.01391315] - -mean value: 0.014959955215454101 - -key: test_mcc -value: [1. 0.91986621 0.82971014 1. 0.95833333 1. - 0.91833182 0.95825929 1. 1. ] - -mean value: 0.9584500801604692 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.95652174 0.91666667 1. 0.9787234 1. - 0.95833333 0.97777778 1. 1. ] - -mean value: 0.9788022921163531 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.91666667 1. 1. 1. - 0.92 1. 1. 1. ] - -mean value: 0.9836666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.91666667 0.91666667 1. 0.95833333 1. - 1. 0.95652174 1. 1. ] - -mean value: 0.9748188405797101 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.95833333 0.91489362 1. 0.9787234 1. - 0.95744681 0.9787234 1. 1. ] - -mean value: 0.9788120567375886 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95833333 0.91485507 1. 0.97916667 1. - 0.95833333 0.97826087 1. 1. ] - -mean value: 0.978894927536232 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.91666667 0.84615385 1. 0.95833333 1. - 0.92 0.95652174 1. 1. ] - -mean value: 0.9597675585284282 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 233 - -mean value: 233.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 230 - -mean value: 230.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.04 - -Accuracy on Blind test: 0.78 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0237093 0.03062272 0.03110003 0.03046179 0.03772449 0.03272963 - 0.03654647 0.03618479 0.05121851 0.03163123] - -mean value: 0.034192895889282225 - -key: score_time -value: [0.02175546 0.02137756 0.02146101 0.02175856 0.0223124 0.02355242 - 0.02141953 0.02142358 0.02148175 0.02138925] - -mean value: 0.021793150901794435 - -key: test_mcc -value: [1. 0.9591663 0.91485507 0.87917396 0.87318841 0.78804348 - 1. 0.95833333 0.91833182 0.95833333] - -mean value: 0.9249425713309061 - -key: train_mcc -value: [0.97668677 0.97668677 0.97674215 0.98135106 0.96715612 0.9576579 - 0.9767396 0.98134942 0.98134942 0.9767396 ] - -mean value: 0.9752458821450679 - -key: test_fscore -value: [1. 0.97959184 0.95833333 0.94117647 0.93617021 0.89361702 - 1. 0.9787234 0.95833333 0.9787234 ] - -mean value: 0.9624669016542787 - -key: train_fscore -value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98360656 0.97882353 - 0.98839907 0.99069767 0.99069767 0.98839907] - -mean value: 0.9876312750119973 - -key: test_precision -value: [1. 0.96 0.95833333 0.88888889 0.95652174 0.91304348 - 1. 0.95833333 0.92 0.95833333] - -mean value: 0.9513454106280195 - -key: train_precision -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97674419 0.97652582 - 0.97706422 0.98156682 0.98156682 0.97706422] - -mean value: 0.9782889146081072 - -key: test_recall -value: [1. 1. 0.95833333 1. 0.91666667 0.875 - 1. 1. 1. 1. ] - -mean value: 0.975 - -key: train_recall -value: [1. 1. 1. 1. 0.99056604 0.98113208 - 1. 1. 1. 1. ] - -mean value: 0.9971698113207548 - -key: test_accuracy -value: [1. 0.97916667 0.95744681 0.93617021 0.93617021 0.89361702 - 1. 0.9787234 0.95744681 0.9787234 ] - -mean value: 0.9617464539007093 - -key: train_accuracy -value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98352941 0.97882353 - 0.98823529 0.99058824 0.99058824 0.98823529] - -mean value: 0.9875238623751388 - -key: test_roc_auc -value: [1. 0.97916667 0.95742754 0.93478261 0.9365942 0.89402174 - 1. 0.97916667 0.95833333 0.97916667] - -mean value: 0.9618659420289856 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98354593 0.97882895 - 0.98820755 0.99056604 0.99056604 0.98820755] - -mean value: 0.987521038178758 - -key: test_jcc -value: [1. 0.96 0.92 0.88888889 0.88 0.80769231 - 1. 0.95833333 0.92 0.95833333] - -mean value: 0.9293247863247863 - -key: train_jcc -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: 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 - ros_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: 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 - ros_CV['Resampling'] = rs_ros -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: 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 - ros_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: 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 - ros_BT['Resampling'] = rs_ros -[0.97695853 0.97695853 0.97695853 0.98148148 0.96774194 0.95852535 - 0.97706422 0.98156682 0.98156682 0.97706422] - -mean value: 0.9755886419544307 - -key: TN -value: 224 - -mean value: 224.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 230 - -mean value: 230.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.92 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.14134121 0.1367569 0.25494242 0.31138849 0.2787056 0.21976233 - 0.18788242 0.32086945 0.32341385 0.30260015] - -mean value: 0.24776628017425537 - -key: score_time -value: [0.01222801 0.0124011 0.01748705 0.02304387 0.02139544 0.01204228 - 0.0219698 0.02384806 0.02312064 0.02127814] - -mean value: 0.01888144016265869 - -key: test_mcc -value: [1. 0.9591663 0.91485507 0.87917396 0.87318841 0.78804348 - 1. 0.95833333 0.91833182 0.95833333] - -mean value: 0.9249425713309061 - -key: train_mcc -value: [0.97668677 0.97668677 0.97674215 0.98135106 0.96715612 0.9576579 - 0.9767396 0.98134942 0.98134942 0.9767396 ] - -mean value: 0.9752458821450679 - -key: test_fscore -value: [1. 0.97959184 0.95833333 0.94117647 0.93617021 0.89361702 - 1. 0.9787234 0.95833333 0.9787234 ] - -mean value: 0.9624669016542787 - -key: train_fscore -value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98360656 0.97882353 - 0.98839907 0.99069767 0.99069767 0.98839907] - -mean value: 0.9876312750119973 - -key: test_precision -value: [1. 0.96 0.95833333 0.88888889 0.95652174 0.91304348 - 1. 0.95833333 0.92 0.95833333] - -mean value: 0.9513454106280195 - -key: train_precision -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97674419 0.97652582 - 0.97706422 0.98156682 0.98156682 0.97706422] - -mean value: 0.9782889146081072 - -key: test_recall -value: [1. 1. 0.95833333 1. 0.91666667 0.875 - 1. 1. 1. 1. ] - -mean value: 0.975 - -key: train_recall -value: [1. 1. 1. 1. 0.99056604 0.98113208 - 1. 1. 1. 1. ] - -mean value: 0.9971698113207548 - -key: test_accuracy -value: [1. 0.97916667 0.95744681 0.93617021 0.93617021 0.89361702 - 1. 0.9787234 0.95744681 0.9787234 ] - -mean value: 0.9617464539007093 - -key: train_accuracy -value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98352941 0.97882353 - 0.98823529 0.99058824 0.99058824 0.98823529] - -mean value: 0.9875238623751388 - -key: test_roc_auc -value: [1. 0.97916667 0.95742754 0.93478261 0.9365942 0.89402174 - 1. 0.97916667 0.95833333 0.97916667] - -mean value: 0.9618659420289856 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98354593 0.97882895 - 0.98820755 0.99056604 0.99056604 0.98820755] - -mean value: 0.987521038178758 - -key: test_jcc -value: [1. 0.96 0.92 0.88888889 0.88 0.80769231 - 1. 0.95833333 0.92 0.95833333] - -mean value: 0.9293247863247863 - -key: train_jcc -value: [0.97695853 0.97695853 0.97695853 0.98148148 0.96774194 0.95852535 - 0.97706422 0.98156682 0.98156682 0.97706422] - -mean value: 0.9755886419544307 - -key: TN -value: 224 - -mean value: 224.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 230 - -mean value: 230.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.92 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -fit_time -value: [0.02548289 0.02497411 0.02377176 0.02586055 0.02631164 0.02622843 - 0.02287912 0.02469206 0.02876091 0.02566028] - -mean value: 0.02546217441558838 - -key: score_time -value: [0.01147556 0.01142359 0.01143599 0.01148558 0.01146889 0.01144385 - 0.01146317 0.01166201 0.01145411 0.01142836] - -mean value: 0.011474108695983887 - -key: test_mcc -value: [0.54761905 0.41475753 0.21957752 1. 0.7200823 0.73192505 - 0.50709255 0.54761905 0.50709255 1. ] - -mean value: 0.6195765602863357 - -key: train_mcc -value: [0.87950384 0.93042952 0.89564428 0.91318814 0.89615538 0.89775899 - 0.91316192 0.89615538 0.87944107 0.87944107] - -mean value: 0.8980879593254129 - -key: test_fscore -value: [0.76923077 0.71428571 0.54545455 1. 0.875 0.83333333 - 0.6 0.76923077 0.72727273 1. ] - -mean value: 0.783380785880786 - -key: train_fscore -value: [0.9380531 0.96551724 0.94827586 0.95652174 0.94642857 0.94545455 - 0.95575221 0.94642857 0.93913043 0.93913043] - -mean value: 0.9480692710190131 - -key: test_precision -value: [0.71428571 0.625 0.6 1. 0.77777778 1. - 1. 0.83333333 0.8 1. ] - -mean value: 0.8350396825396824 - -key: train_precision -value: [0.96363636 0.96551724 0.94827586 0.96491228 0.96363636 0.98113208 - 0.96428571 0.96363636 0.94736842 0.94736842] - -mean value: 0.9609769106921797 - -key: test_recall -value: [0.83333333 0.83333333 0.5 1. 1. 0.71428571 - 0.42857143 0.71428571 0.66666667 1. ] - -mean value: 0.7690476190476191 - -key: train_recall -value: [0.9137931 0.96551724 0.94827586 0.94827586 0.92982456 0.9122807 - 0.94736842 0.92982456 0.93103448 0.93103448] - -mean value: 0.9357229280096794 - -key: test_accuracy -value: [0.76923077 0.69230769 0.61538462 1. 0.84615385 0.84615385 - 0.69230769 0.76923077 0.75 1. ] - -mean value: 0.7980769230769231 - -key: train_accuracy -value: [0.93913043 0.96521739 0.94782609 0.95652174 0.94782609 0.94782609 - 0.95652174 0.94782609 0.93965517 0.93965517] - -mean value: 0.94880059970015 - -key: test_roc_auc -value: [0.77380952 0.70238095 0.60714286 1. 0.83333333 0.85714286 - 0.71428571 0.77380952 0.75 1. ] - -mean value: 0.8011904761904762 - -key: train_roc_auc -value: [0.93935269 0.96521476 0.94782214 0.95659407 0.9476709 0.94751966 - 0.95644283 0.9476709 0.93965517 0.93965517] - -mean value: 0.9487598306110103 - -key: test_jcc -value: [0.625 0.55555556 0.375 1. 0.77777778 0.71428571 - 0.42857143 0.625 0.57142857 1. ] - -mean value: 0.6672619047619047 - -key: train_jcc -value: [0.88333333 0.93333333 0.90163934 0.91666667 0.89830508 0.89655172 - 0.91525424 0.89830508 0.8852459 0.8852459 ] - -mean value: 0.9013880611791908 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.8 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.55759573 0.70166278 0.56455135 0.56107998 0.57032299 0.6551044 - 0.56000018 0.56036115 0.56738853 0.66849685] - -mean value: 0.5966563940048217 - -key: score_time -value: [0.0117178 0.01296282 0.01171088 0.01284122 0.01298141 0.01283264 - 0.012954 0.01287794 0.012851 0.01291609] - -mean value: 0.012664580345153808 - -key: test_mcc -value: [0.73192505 0.85714286 0.73192505 1. 0.53674504 0.41475753 - 0.73192505 0.54761905 0.84515425 0.84515425] - -mean value: 0.7242348149477957 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.85714286 0.92307692 0.85714286 1. 0.8 0.66666667 - 0.83333333 0.76923077 0.92307692 0.92307692] - -mean value: 0.8552747252747253 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.75 0.85714286 0.75 1. 0.75 0.8 - 1. 0.83333333 0.85714286 0.85714286] - -mean value: 0.8454761904761904 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.85714286 0.57142857 - 0.71428571 0.71428571 1. 1. ] - -mean value: 0.8857142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.84615385 0.92307692 0.84615385 1. 0.76923077 0.69230769 - 0.84615385 0.76923077 0.91666667 0.91666667] - -mean value: 0.8525641025641025 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.85714286 0.92857143 0.85714286 1. 0.76190476 0.70238095 - 0.85714286 0.77380952 0.91666667 0.91666667] - -mean value: 0.8571428571428573 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.75 0.85714286 0.75 1. 0.66666667 0.5 - 0.71428571 0.625 0.85714286 0.85714286] - -mean value: 0.7577380952380952 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.84 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01163244 0.01148558 0.00906825 0.00943017 0.00903463 0.00944757 - 0.00886846 0.00889182 0.00922656 0.00919008] - -mean value: 0.009627556800842286 - -key: score_time -value: [0.01144195 0.01121354 0.00962234 0.00908494 0.00913692 0.00922203 - 0.00858068 0.00846982 0.0084188 0.00912619] - -mean value: 0.009431719779968262 - -key: test_mcc -value: [ 0.22537447 0.53674504 0.21957752 0.23809524 0.53674504 0.09759001 - 0.50709255 -0.23809524 0.84515425 0.50709255] - -mean value: 0.34753714322820006 - -key: train_mcc -value: [0.65827364 0.69801188 0.51354191 0.59642005 0.60560491 0.62065383 - 0.67489964 0.52793171 0.6457464 0.6040687 ] - -mean value: 0.6145152675573603 - -key: test_fscore -value: [0.44444444 0.72727273 0.54545455 0.61538462 0.8 0.5 - 0.6 0.42857143 0.90909091 0.72727273] - -mean value: 0.6297491397491397 - -key: train_fscore -value: [0.79207921 0.83018868 0.65934066 0.76923077 0.75510204 0.76767677 - 0.84297521 0.72 0.78 0.76470588] - -mean value: 0.7681299213195109 - -key: test_precision -value: [0.66666667 0.8 0.6 0.57142857 0.75 0.6 - 1. 0.42857143 1. 0.8 ] - -mean value: 0.7216666666666666 - -key: train_precision -value: [0.93023256 0.91666667 0.90909091 0.86956522 0.90243902 0.9047619 - 0.796875 0.8372093 0.92857143 0.88636364] - -mean value: 0.8881775647701209 - -key: test_recall -value: [0.33333333 0.66666667 0.5 0.66666667 0.85714286 0.42857143 - 0.42857143 0.42857143 0.83333333 0.66666667] - -mean value: 0.5809523809523809 - -key: train_recall -value: [0.68965517 0.75862069 0.51724138 0.68965517 0.64912281 0.66666667 - 0.89473684 0.63157895 0.67241379 0.67241379] - -mean value: 0.6842105263157895 - -key: test_accuracy -value: [0.61538462 0.76923077 0.61538462 0.61538462 0.76923077 0.53846154 - 0.69230769 0.38461538 0.91666667 0.75 ] - -mean value: 0.6666666666666667 - -key: train_accuracy -value: [0.8173913 0.84347826 0.73043478 0.79130435 0.79130435 0.8 - 0.83478261 0.75652174 0.81034483 0.79310345] - -mean value: 0.7968665667166417 - -key: test_roc_auc -value: [0.5952381 0.76190476 0.60714286 0.61904762 0.76190476 0.54761905 - 0.71428571 0.38095238 0.91666667 0.75 ] - -mean value: 0.6654761904761906 - -key: train_roc_auc -value: [0.8185118 0.84422263 0.7323049 0.79219601 0.79007864 0.79885057 - 0.83529946 0.75544465 0.81034483 0.79310345] - -mean value: 0.7970356926799758 - -key: test_jcc -value: [0.28571429 0.57142857 0.375 0.44444444 0.66666667 0.33333333 - 0.42857143 0.27272727 0.83333333 0.57142857] - -mean value: 0.47826479076479067 - -key: train_jcc -value: [0.6557377 0.70967742 0.49180328 0.625 0.60655738 0.62295082 - 0.72857143 0.5625 0.63934426 0.61904762] - -mean value: 0.6261189909596837 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.27 - -Accuracy on Blind test: 0.72 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00853348 0.00885534 0.00906277 0.0089674 0.00840831 0.00923443 - 0.00851059 0.00884318 0.00853682 0.00857449] - -mean value: 0.008752679824829102 - -key: score_time -value: [0.00847602 0.00842738 0.00847268 0.00882149 0.00835872 0.00905609 - 0.00877452 0.00874686 0.00880098 0.0091145 ] - -mean value: 0.008704924583435058 - -key: test_mcc -value: [ 0.38575837 0.09759001 0.21957752 -0.09759001 -0.07142857 -0.07142857 - 0.09759001 0.50709255 -0.19245009 0.35355339] - -mean value: 0.12282646094568686 - -key: train_mcc -value: [0.52166881 0.43213628 0.42809663 0.53276944 0.49667181 0.52793171 - 0.49561273 0.5202221 0.53647994 0.54103611] - -mean value: 0.5032625556468886 - -key: test_fscore -value: [0.6 0.57142857 0.54545455 0.36363636 0.46153846 0.46153846 - 0.5 0.6 0.22222222 0.6 ] - -mean value: 0.4925818625818626 - -key: train_fscore -value: [0.73584906 0.69158879 0.7027027 0.75675676 0.69387755 0.72 - 0.74336283 0.73076923 0.75675676 0.74766355] - -mean value: 0.7279327222916634 - -key: test_precision -value: [0.75 0.5 0.6 0.4 0.5 0.5 - 0.6 1. 0.33333333 0.75 ] - -mean value: 0.5933333333333333 - -key: train_precision -value: [0.8125 0.75510204 0.73584906 0.79245283 0.82926829 0.8372093 - 0.75 0.80851064 0.79245283 0.81632653] - -mean value: 0.7929671521716084 - -key: test_recall -value: [0.5 0.66666667 0.5 0.33333333 0.42857143 0.42857143 - 0.42857143 0.42857143 0.16666667 0.5 ] - -mean value: 0.43809523809523804 - -key: train_recall -value: [0.67241379 0.63793103 0.67241379 0.72413793 0.59649123 0.63157895 - 0.73684211 0.66666667 0.72413793 0.68965517] - -mean value: 0.6752268602540834 - -key: test_accuracy -value: [0.69230769 0.53846154 0.61538462 0.46153846 0.46153846 0.46153846 - 0.53846154 0.69230769 0.41666667 0.66666667] - -mean value: 0.5544871794871795 - -key: train_accuracy -value: [0.75652174 0.71304348 0.71304348 0.76521739 0.73913043 0.75652174 - 0.74782609 0.75652174 0.76724138 0.76724138] - -mean value: 0.7482308845577211 - -key: test_roc_auc -value: [0.67857143 0.54761905 0.60714286 0.45238095 0.46428571 0.46428571 - 0.54761905 0.71428571 0.41666667 0.66666667] - -mean value: 0.555952380952381 - -key: train_roc_auc -value: [0.75725953 0.71370236 0.71339988 0.76557774 0.73790079 0.75544465 - 0.7477314 0.75574713 0.76724138 0.76724138] - -mean value: 0.7481246218995766 - -key: test_jcc -value: [0.42857143 0.4 0.375 0.22222222 0.3 0.3 - 0.33333333 0.42857143 0.125 0.42857143] - -mean value: 0.3341269841269841 - -key: train_jcc -value: [0.58208955 0.52857143 0.54166667 0.60869565 0.53125 0.5625 - 0.5915493 0.57575758 0.60869565 0.59701493] - -mean value: 0.5727790748730086 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.05 - -Accuracy on Blind test: 0.54 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00907969 0.00883484 0.00880814 0.00789714 0.00841284 0.00867152 - 0.00813246 0.00892329 0.00887847 0.00889778] - -mean value: 0.008653616905212403 - -key: score_time -value: [0.00998592 0.00991344 0.0096209 0.00929761 0.00940967 0.00916767 - 0.01009822 0.01010013 0.01001501 0.00996399] - -mean value: 0.009757256507873536 - -key: test_mcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - -value: [-0.23809524 0.54761905 0.05143445 0.53674504 0.38095238 0.50709255 - 0.28288947 0.14085904 0.16903085 0.50709255] - -mean value: 0.2885620154590426 - -key: train_mcc -value: [0.48354418 0.46484968 0.52541766 0.43847995 0.47616964 0.41195157 - 0.48484331 0.44946451 0.40690442 0.38769906] - -mean value: 0.45293239771525967 - -key: test_fscore -value: [0.33333333 0.76923077 0.4 0.72727273 0.71428571 0.6 - 0.54545455 0.4 0.54545455 0.72727273] - -mean value: 0.5762304362304362 - -key: train_fscore -value: [0.72222222 0.71559633 0.73076923 0.67961165 0.68686869 0.67924528 - 0.71153846 0.69230769 0.66019417 0.65384615] - -mean value: 0.6932199886089263 - -key: test_precision -value: [0.33333333 0.71428571 0.5 0.8 0.71428571 1. - 0.75 0.66666667 0.6 0.8 ] - -mean value: 0.6878571428571428 - -key: train_precision -value: [0.78 0.76470588 0.82608696 0.77777778 0.80952381 0.73469388 - 0.78723404 0.76595745 0.75555556 0.73913043] - -mean value: 0.7740665783427154 - -key: test_recall -value: [0.33333333 0.83333333 0.33333333 0.66666667 0.71428571 0.42857143 - 0.42857143 0.28571429 0.5 0.66666667] - -mean value: 0.5190476190476191 - -key: train_recall -value: [0.67241379 0.67241379 0.65517241 0.60344828 0.59649123 0.63157895 - 0.64912281 0.63157895 0.5862069 0.5862069 ] - -mean value: 0.6284633998790079 - -key: test_accuracy -value: [0.38461538 0.76923077 0.53846154 0.76923077 0.69230769 0.69230769 - 0.61538462 0.53846154 0.58333333 0.75 ] - -mean value: 0.6333333333333333 - -key: train_accuracy -value: [0.73913043 0.73043478 0.75652174 0.71304348 0.73043478 0.70434783 - 0.73913043 0.72173913 0.69827586 0.68965517] - -mean value: 0.7222713643178411 - -key: test_roc_auc -value: [0.38095238 0.77380952 0.52380952 0.76190476 0.69047619 0.71428571 - 0.63095238 0.55952381 0.58333333 0.75 ] - -mean value: 0.636904761904762 - -key: train_roc_auc -value: [0.73971567 0.73094374 0.75741077 0.71400484 0.7292801 0.70372051 - 0.73835451 0.72096189 0.69827586 0.68965517] - -mean value: 0.7222323049001815 - -key: test_jcc -value: [0.2 0.625 0.25 0.57142857 0.55555556 0.42857143 - 0.375 0.25 0.375 0.57142857] - -mean value: 0.4201984126984127 - -key: train_jcc -value: [0.56521739 0.55714286 0.57575758 0.51470588 0.52307692 0.51428571 - 0.55223881 0.52941176 0.49275362 0.48571429] - -mean value: 0.5310304823499082 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 33 - -mean value: 33.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.03 - -Accuracy on Blind test: 0.56 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00929236 0.0091424 0.00922894 0.00920606 0.00914025 0.00913072 - 0.00918317 0.00913858 0.00915313 0.00918818] - -mean value: 0.009180378913879395 - -key: score_time -value: [0.00859475 0.00858831 0.00861931 0.00862646 0.00869465 0.0085423 - 0.00871539 0.00858855 0.00859594 0.00857806] - -mean value: 0.008614373207092286 - -key: test_mcc -value: [ 0.07142857 0.23809524 0.22537447 0.21957752 0.38575837 0.41475753 - 0.28288947 -0.07142857 0.50709255 0.66666667] - -mean value: 0.2940211822778897 - -key: train_mcc -value: [0.63744225 0.65603842 0.63744225 0.56987466 0.60692685 0.58540009 - 0.60404888 0.63913253 0.60789179 0.57594167] - -mean value: 0.6120139388631578 - -key: test_fscore -value: [0.5 0.61538462 0.44444444 0.54545455 0.75 0.66666667 - 0.54545455 0.46153846 0.72727273 0.83333333] - -mean value: 0.6089549339549339 - -key: train_fscore -value: [0.81081081 0.81818182 0.81081081 0.7706422 0.78095238 0.77777778 - 0.78504673 0.80373832 0.78899083 0.76635514] - -mean value: 0.7913306812972423 - -key: test_precision -value: [0.5 0.57142857 0.66666667 0.6 0.66666667 0.8 - 0.75 0.5 0.8 0.83333333] - -mean value: 0.6688095238095237 - -key: train_precision -value: [0.8490566 0.86538462 0.8490566 0.82352941 0.85416667 0.82352941 - 0.84 0.86 0.84313725 0.83673469] - -mean value: 0.8444595261907375 - -key: test_recall -value: [0.5 0.66666667 0.33333333 0.5 0.85714286 0.57142857 - 0.42857143 0.42857143 0.66666667 0.83333333] - -mean value: 0.5785714285714285 - -key: train_recall -value: [0.77586207 0.77586207 0.77586207 0.72413793 0.71929825 0.73684211 - 0.73684211 0.75438596 0.74137931 0.70689655] - -mean value: 0.7447368421052631 - -key: test_accuracy -value: [0.53846154 0.61538462 0.61538462 0.61538462 0.69230769 0.69230769 - 0.61538462 0.46153846 0.75 0.83333333] - -mean value: 0.642948717948718 - -key: train_accuracy -value: [0.8173913 0.82608696 0.8173913 0.7826087 0.8 0.79130435 - 0.8 0.8173913 0.80172414 0.78448276] - -mean value: 0.8038380809595204 - -key: test_roc_auc -value: [0.53571429 0.61904762 0.5952381 0.60714286 0.67857143 0.70238095 - 0.63095238 0.46428571 0.75 0.83333333] - -mean value: 0.6416666666666666 - -key: train_roc_auc -value: [0.8177556 0.82652753 0.8177556 0.7831216 0.7993043 0.79083485 - 0.79945554 0.81684815 0.80172414 0.78448276] - -mean value: 0.8037810042347248 - -key: test_jcc -value: [0.33333333 0.44444444 0.28571429 0.375 0.6 0.5 - 0.375 0.3 0.57142857 0.71428571] - -mean value: 0.44992063492063494 - -key: train_jcc -value: [0.68181818 0.69230769 0.68181818 0.62686567 0.640625 0.63636364 - 0.64615385 0.671875 0.65151515 0.62121212] - -mean value: 0.6550554482830602 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.23 - -Accuracy on Blind test: 0.65 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.53628516 0.62847281 0.5407598 0.60236335 0.53843927 0.60092044 - 0.54009676 0.54076219 0.54493165 0.71171832] - -mean value: 0.5784749746322632/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - - -key: score_time -value: [0.01352882 0.01294923 0.01417613 0.01308346 0.01450157 0.01442575 - 0.0144608 0.0145483 0.01314163 0.01451683] - -mean value: 0.013933253288269044 - -key: test_mcc -value: [0.07142857 0.23809524 0.21957752 1. 0.22537447 0.09759001 - 0.28288947 0.41475753 0.50709255 0.50709255] - -mean value: 0.3563897912767536 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.61538462 0.54545455 1. 0.70588235 0.5 - 0.54545455 0.66666667 0.76923077 0.76923077] - -mean value: 0.6617304264363087 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.5 0.57142857 0.6 1. 0.6 0.6 - 0.75 0.8 0.71428571 0.71428571] - -mean value: 0.685 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.5 0.66666667 0.5 1. 0.85714286 0.42857143 - 0.42857143 0.57142857 0.83333333 0.83333333] - -mean value: 0.6619047619047619 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.53846154 0.61538462 0.61538462 1. 0.61538462 0.53846154 - 0.61538462 0.69230769 0.75 0.75 ] - -mean value: 0.6730769230769231 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.53571429 0.61904762 0.60714286 1. 0.5952381 0.54761905 - 0.63095238 0.70238095 0.75 0.75 ] - -mean value: 0.6738095238095239 - -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 0.44444444 0.375 1. 0.54545455 0.33333333 - 0.375 0.5 0.625 0.625 ] - -mean value: 0.5156565656565657 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.4 - -Accuracy on Blind test: 0.71 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', DecisionTreeClassifier(random_state=42))]) - -key: fit_time -value: [0.01383662 0.01364303 0.01029849 0.01006365 0.00987554 0.00922799 - 0.00956511 0.00956154 0.0096581 0.00960445] - -mean value: 0.010533452033996582 - -key: score_time -value: [0.01158452 0.00933385 0.00867271 0.00883508 0.00839686 0.00836468 - 0.00835061 0.00836229 0.00824571 0.00836349] - -mean value: 0.008850979804992675 - -key: test_mcc -value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 - 1. 0.69047619 1. 1. ] - -mean value: 0.9119047619047619 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 - 1. 0.85714286 1. 1. ] - -mean value: 0.9549450549450549 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 1. 0.85714286 0.85714286 1. 1. - 1. 0.85714286 1. 1. ] - -mean value: 0.9428571428571428 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9714285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 - 1. 0.84615385 1. 1. ] - -mean value: 0.9538461538461538 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 - 1. 0.8452381 1. 1. ] - -mean value: 0.955952380952381 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 - 1. 0.75 1. 1. ] - -mean value: 0.9178571428571429 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 60 - -mean value: 60.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.08474231 0.08478355 0.08375072 0.08426261 0.08383918 0.08362508 - 0.08540273 0.08369732 0.08442283 0.08440924] - -mean value: 0.0842935562133789 - -key: score_time -value: [0.01687574 0.01674438 0.01730251 0.01674294 0.01672387 0.0167408 - 0.016819 0.01675367 0.01675248 0.01690674] - -mean value: 0.016836214065551757 - -key: test_mcc -value: [0.07142857 0.6172134 0.59160798 0.54761905 0.22537447 0.41475753 - 0.14085904 0.41475753 0.84515425 0.66666667] - -mean value: 0.45354384909897416 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.8 0.66666667 0.76923077 0.70588235 0.66666667 - 0.4 0.66666667 0.92307692 0.83333333] - -mean value: 0.6931523378582203 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.5 0.66666667 1. 0.71428571 0.6 0.8 - 0.66666667 0.8 0.85714286 0.83333333] - -mean value: 0.7438095238095237 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.5 1. 0.5 0.83333333 0.85714286 0.57142857 - 0.28571429 0.57142857 1. 0.83333333] - -mean value: 0.6952380952380952 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.53846154 0.76923077 0.76923077 0.76923077 0.61538462 0.69230769 - 0.53846154 0.69230769 0.91666667 0.83333333] - -mean value: 0.7134615384615385 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.53571429 0.78571429 0.75 0.77380952 0.5952381 0.70238095 - 0.55952381 0.70238095 0.91666667 0.83333333] - -mean value: 0.7154761904761905 - -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 0.66666667 0.5 0.625 0.54545455 0.5 - 0.25 0.5 0.85714286 0.71428571] - -mean value: 0.5491883116883117 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 47 - -mean value: 47.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.46 - -Accuracy on Blind test: 0.74 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00844145 0.00862551 0.00836611 0.00838566 0.00852084 0.0095427 - 0.00830698 0.0083344 0.00922632 0.00840116] - -mean value: 0.0086151123046875 - -key: score_time -value: [0.00828457 0.00885868 0.00828385 0.0083971 0.00831819 0.00866413 - 0.00849199 0.00838947 0.00894332 0.00846314] - -mean value: 0.008509445190429687 - -key: test_mcc -value: [ 0.6172134 1. 0.46056619 0.07142857 0.05143445 -0.28288947 - -0.22537447 0.38095238 0.33333333 0. ] - -mean value: 0.24066643791637493 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 1. 0.5 0.5 0.625 0.5 - 0.2 0.71428571 0.66666667 0.57142857] - -mean value: 0.6077380952380953 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 1. 1. 0.5 0.55555556 0.44444444 - 0.33333333 0.71428571 0.66666667 0.5 ] - -mean value: 0.638095238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.33333333 0.5 0.71428571 0.57142857 - 0.14285714 0.71428571 0.66666667 0.66666667] - -mean value: 0.630952380952381 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.76923077 1. 0.69230769 0.53846154 0.53846154 0.38461538 - 0.38461538 0.69230769 0.66666667 0.5 ] - -mean value: 0.6166666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.78571429 1. 0.66666667 0.53571429 0.52380952 0.36904762 - 0.4047619 0.69047619 0.66666667 0.5 ] - -mean value: 0.6142857142857143 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 1. 0.33333333 0.33333333 0.45454545 0.33333333 - 0.11111111 0.55555556 0.5 0.4 ] - -mean value: 0.46878787878787875 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.23 - -Accuracy on Blind test: 0.66 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.06711006 1.06243563 1.05919814 1.0622704 1.0625546 1.06830359 - 1.06534982 1.0697732 1.07362056 1.06692076] - -mean value: 1.0657536745071412 - -key: score_time -value: [0.08754396 0.08738852 0.08666062 0.08688879 0.08696675 0.08656383 - 0.08840013 0.08690977 0.08692837 0.08670497] - -mean value: 0.08709557056427002 - -key: test_mcc -value: [0.54761905 0.73192505 0.53674504 1. 0.7200823 0.73192505 - 0.69047619 0.6172134 0.84515425 0.84515425] - -mean value: 0.7266294596768228 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.76923077 0.85714286 0.72727273 1. 0.875 0.83333333 - 0.85714286 0.72727273 0.92307692 0.92307692] - -mean value: 0.8492549117549117 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.75 0.8 1. 0.77777778 1. - 0.85714286 1. 0.85714286 0.85714286] - -mean value: 0.8613492063492062 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.83333333 1. 0.66666667 1. 1. 0.71428571 - 0.85714286 0.57142857 1. 1. ] - -mean value: 0.8642857142857142 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.76923077 0.84615385 0.76923077 1. 0.84615385 0.84615385 - 0.84615385 0.76923077 0.91666667 0.91666667] - -mean value: 0.8525641025641025 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.77380952 0.85714286 0.76190476 1. 0.83333333 0.85714286 - 0.8452381 0.78571429 0.91666667 0.91666667] - -mean value: 0.854761904761905 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.625 0.75 0.57142857 1. 0.77777778 0.71428571 - 0.75 0.57142857 0.85714286 0.85714286] - -mean value: 0.7474206349206348 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 54 - -mean value: 54.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 55 - -mean value: 55.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.86 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.78958631 0.82710862 0.84518576 0.87769175 0.84533954 0.9362042 - 0.90117383 0.90355873 0.81010485 0.82837105] - -mean value: 0.8564324617385864 - -key: score_time -value: [0.18188357 0.16762471 0.19198155 0.14597726 0.17890453 0.15681887 - 0.18019104 0.18178201 0.18352628 0.20134711] - -mean value: 0.17700369358062745 - -key: test_mcc -value: [0.38095238 0.54761905 0.59160798 1. 0.7200823 0.73192505 - 0.73192505 0.6172134 0.84515425 0.84515425] - -mean value: 0.7011633725432687 - -key: train_mcc -value: [0.96580942 0.96521476 0.96521476 0.98275345 0.98275862 0.96521476 - 0.98275862 0.98275862 0.98290472 0.96551724] - -mean value: 0.974090498263962 - -key: test_fscore -value: [0.66666667 0.76923077 0.66666667 1. 0.875 0.83333333 - 0.83333333 0.72727273 0.92307692 0.92307692] - -mean value: 0.8217657342657343 - -key: train_fscore -value: [0.98245614 0.98275862 0.98275862 0.99145299 0.99130435 0.98245614 - 0.99130435 0.99130435 0.99145299 0.98275862] - -mean value: 0.9870007169154963 - -key: test_precision -value: [0.66666667 0.71428571 1. 1. 0.77777778 1. - 1. 1. 0.85714286 0.85714286] - -mean value: 0.8873015873015874 - -key: train_precision -value: [1. 0.98275862 0.98275862 0.98305085 0.98275862 0.98245614 - 0.98275862 0.98275862 0.98305085 0.98275862] - -mean value: 0.9845109559404062 - -key: test_recall -value: [0.66666667 0.83333333 0.5 1. 1. 0.71428571 - 0.71428571 0.57142857 1. 1. ] - -mean value: 0.8 - -key: train_recall -value: [0.96551724 0.98275862 0.98275862 1. 1. 0.98245614 - 1. 1. 1. 0.98275862] - -mean value: 0.9896249243799152 - -key: test_accuracy -value: [0.69230769 0.76923077 0.76923077 1. 0.84615385 0.84615385 - 0.84615385 0.76923077 0.91666667 0.91666667] - -mean value: 0.8371794871794872 - -key: train_accuracy -value: [0.9826087 0.9826087 0.9826087 0.99130435 0.99130435 0.9826087 - 0.99130435 0.99130435 0.99137931 0.98275862] - -mean value: 0.9869790104947527 - -key: test_roc_auc -value: [0.69047619 0.77380952 0.75 1. 0.83333333 0.85714286 - 0.85714286 0.78571429 0.91666667 0.91666667] - -mean value: 0.8380952380952381 - -key: train_roc_auc -value: [0.98275862 0.98260738 0.98260738 0.99122807 0.99137931 0.98260738 - 0.99137931 0.99137931 0.99137931 0.98275862] - -mean value: 0.9870084694494856 - -key: test_jcc -value: [0.5 0.625 0.5 1. 0.77777778 0.71428571 - 0.71428571 0.57142857 0.85714286 0.85714286] - -mean value: 0.7117063492063491 - -key: train_jcc -value: [0.96551724 0.96610169 0.96610169 0.98305085 0.98275862 0.96551724 - 0.98275862 0.98275862 0.98305085 0.96610169] - -mean value: 0.9743717124488602 - -key: TN -value: 56 - -mean value: 56.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 51 - -mean value: 51.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.89 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.0548141 0.03600359 0.03932428 0.03700233 0.03631186 0.03644156 - 0.03789878 0.03571916 0.03629661 0.03441095] - -mean value: 0.03842232227325439 - -key: score_time -value: [0.01012206 0.00998211 0.01084924 0.01008987 0.01075172 0.01114345 - 0.01033568 0.01090884 0.01054478 0.01018047] - -mean value: 0.010490822792053222 - -key: test_mcc -value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9428571428571428 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9692307692307691 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.85714286 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9857142857142858 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9571428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9692307692307693 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.92857143 1. 0.92857143 0.92857143 - 1. 0.92857143 1. 1. ] - -mean value: 0.9714285714285715 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9428571428571428 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 1 - -mean value: 1.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.94 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02421451 0.04412055 0.0320704 0.04514098 0.041291 0.04488754 - 0.03924036 0.05337286 0.05183053 0.04660392] - -mean value: 0.04227726459503174 - -key: score_time -value: [0.0211246 0.02603841 0.02130747 0.01807022 0.02199841 0.02134991 - 0.02378583 0.02136731 0.02127481 0.02119875] - -mean value: 0.02175157070159912 - -key: test_mcc -value: [-0.54761905 -0.09759001 0.59160798 0.69047619 0.38095238 0.50709255 - 0.14085904 0.41475753 0.35355339 -0.19245009] - -mean value: 0.22416399219830202 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.16666667 0.36363636 0.66666667 0.83333333 0.71428571 0.6 - 0.4 0.66666667 0.6 0.22222222] - -mean value: 0.5233477633477632 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.16666667 0.4 1. 0.83333333 0.71428571 1. - 0.66666667 0.8 0.75 0.33333333] - -mean value: 0.6664285714285715 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.16666667 0.33333333 0.5 0.83333333 0.71428571 0.42857143 - 0.28571429 0.57142857 0.5 0.16666667] - -mean value: 0.45000000000000007 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.23076923 0.46153846 0.76923077 0.84615385 0.69230769 0.69230769 - 0.53846154 0.69230769 0.66666667 0.41666667] - -mean value: 0.6006410256410257 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.22619048 0.45238095 0.75 0.8452381 0.69047619 0.71428571 - 0.55952381 0.70238095 0.66666667 0.41666667] - -mean value: 0.6023809523809525 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.09090909 0.22222222 0.5 0.71428571 0.55555556 0.42857143 - 0.25 0.5 0.42857143 0.125 ] - -mean value: 0.381511544011544 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 35 - -mean value: 35.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 29 - -mean value: 29.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.43 - -Accuracy on Blind test: 0.76 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01183629 0.01080275 0.00891304 0.00854683 0.00870848 0.00964117 - 0.0084188 0.00841784 0.00847912 0.00880694] - -mean value: 0.009257125854492187 - -key: score_time -value: [0.01165819 0.00944376 0.0087254 0.00845647 0.00867558 0.00870895 - 0.00839353 0.00828433 0.00842071 0.00850129] - -mean value: 0.008926820755004884 - -key: test_mcc -value: [0.23809524 0.28288947 0.05143445 0.54761905 0.53674504 0.73192505 - 0.50709255 0.09759001 0.66666667 0.35355339] - -mean value: 0.4013610922857148 - -key: train_mcc -value: [0.52166881 0.48131767 0.48354418 0.39140956 0.5236969 0.50112101 - 0.4971185 0.48037511 0.49343516 0.41778637] - -mean value: 0.4791473276191696 - -key: test_fscore -value: [0.61538462 0.66666667 0.4 0.76923077 0.8 0.83333333 - 0.6 0.5 0.83333333 0.71428571] - -mean value: 0.6732234432234432 - -key: train_fscore -value: [0.73584906 0.72727273 0.72222222 0.69565217 0.7254902 0.72380952 - 0.73394495 0.72222222 0.71153846 0.68518519] - -mean value: 0.7183186722974032 - -key: test_precision -value: [0.57142857 0.55555556 0.5 0.71428571 0.75 1. - 1. 0.6 0.83333333 0.625 ] - -mean value: 0.7149603174603174 - -key: train_precision -value: [0.8125 0.76923077 0.78 0.70175439 0.82222222 0.79166667 - 0.76923077 0.76470588 0.80434783 0.74 ] - -mean value: 0.7755658521755237 - -key: test_recall -value: [0.66666667 0.83333333 0.33333333 0.83333333 0.85714286 0.71428571 - 0.42857143 0.42857143 0.83333333 0.83333333] - -mean value: 0.6761904761904761 - -key: train_recall -value: [0.67241379 0.68965517 0.67241379 0.68965517 0.64912281 0.66666667 - 0.70175439 0.68421053 0.63793103 0.63793103] - -mean value: 0.6701754385964913 - -key: test_accuracy -value: [0.61538462 0.61538462 0.53846154 0.76923077 0.76923077 0.84615385 - 0.69230769 0.53846154 0.83333333 0.66666667] - -mean value: 0.6884615384615385 - -key: train_accuracy -value: [0.75652174 0.73913043 0.73913043 0.69565217 0.75652174 0.74782609 - 0.74782609 0.73913043 0.74137931 0.70689655] - -mean value: 0.7370014992503747 - -key: test_roc_auc -value: [0.61904762 0.63095238 0.52380952 0.77380952 0.76190476 0.85714286 - 0.71428571 0.54761905 0.83333333 0.66666667] - -mean value: 0.6928571428571428 - -key: train_roc_auc -value: [0.75725953 0.73956443 0.73971567 0.69570478 0.75559589 0.74712644 - 0.74742892 0.73865699 0.74137931 0.70689655] - -mean value: 0.7369328493647912 - -key: test_jcc -value: [0.44444444 0.5 0.25 0.625 0.66666667 0.71428571 - 0.42857143 0.33333333 0.71428571 0.55555556] - -mean value: 0.5232142857142856 - -key: train_jcc -value: [0.58208955 0.57142857 0.56521739 0.53333333 0.56923077 0.56716418 - 0.57971014 0.56521739 0.55223881 0.52112676] - -mean value: 0.5606756899405718 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.66 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01212406 0.01453185 0.01396585 0.01321959 0.01503253 0.01537943 - 0.01324081 0.01318336 0.01479745 0.01383924] - -mean value: 0.01393141746520996 - -key: score_time -value: [0.00918651 0.01131415 0.01131535 0.01138783 0.01142979 0.01165056 - 0.01144242 0.01141739 0.01159096 0.01164055] - -mean value: 0.011237549781799316 - -key: test_mcc -value: [0.41475753 0.6172134 0.38095238 0.7200823 0.7200823 0.41475753 - 0.6172134 0.54761905 0.70710678 1. ] - -mean value: 0.6139784671107156 - -key: train_mcc -value: [0.98275345 1. 0.98275862 0.80942721 1. 1. - 0.82362769 0.93272881 1. 0.93325653] - -mean value: 0.9464552313523782 - -key: test_fscore -value: [0.71428571 0.8 0.66666667 0.8 0.875 0.66666667 - 0.72727273 0.76923077 0.85714286 1. ] - -mean value: 0.7876265401265401 - -key: train_fscore -value: [0.99145299 1. 0.99130435 0.88461538 1. 1. - 0.89320388 0.96610169 1. 0.96428571] - -mean value: 0.9690964016590577 - -key: test_precision -value: [0.625 0.66666667 0.66666667 1. 0.77777778 0.8 - 1. 0.83333333 0.75 1. ] - -mean value: 0.8119444444444444 - -key: train_precision -value: [0.98305085 1. 1. 1. 1. 1. - 1. 0.93442623 1. 1. ] - -mean value: 0.9917477076965824 - -key: test_recall -value: [0.83333333 1. 0.66666667 0.66666667 1. 0.57142857 - 0.57142857 0.71428571 1. 1. ] - -mean value: 0.8023809523809524 - -key: train_recall -value: [1. 1. 0.98275862 0.79310345 1. 1. - 0.80701754 1. 1. 0.93103448] - -mean value: 0.9513914095583788 - -key: test_accuracy -value: [0.69230769 0.76923077 0.69230769 0.84615385 0.84615385 0.69230769 - 0.76923077 0.76923077 0.83333333 1. ] - -mean value: 0.791025641025641 - -key: train_accuracy -value: [0.99130435 1. 0.99130435 0.89565217 1. 1. - 0.90434783 0.96521739 1. 0.96551724] - -mean value: 0.9713343328335832 - -key: test_roc_auc -value: [0.70238095 0.78571429 0.69047619 0.83333333 0.83333333 0.70238095 - 0.78571429 0.77380952 0.83333333 1. ] - -mean value: 0.7940476190476191 - -key: train_roc_auc -value: [0.99122807 1. 0.99137931 0.89655172 1. 1. - 0.90350877 0.96551724 1. 0.96551724] - -mean value: 0.9713702359346641 - -key: test_jcc -value: [0.55555556 0.66666667 0.5 0.66666667 0.77777778 0.5 - 0.57142857 0.625 0.75 1. ] - -mean value: 0.6613095238095237 - -key: train_jcc -value: [0.98305085 1. 0.98275862 0.79310345 1. 1. - 0.80701754 0.93442623 1. 0.93103448] - -mean value: 0.9431391172549611 - -key: TN -value: 50 - -mean value: 50.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 51 - -mean value: 51.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.69 - -Accuracy on Blind test: 0.86 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01287651 0.01258087 0.0124259 0.01267362 0.01336336 0.01277065 - 0.01268458 0.01311874 0.0131762 0.01308203] - -mean value: 0.012875247001647949 - -key: score_time -value: [0.01142859 0.01157522 0.01139855 0.01146388 0.01148963 0.01156664 - 0.01148224 0.01146364 0.01155543 0.0116024 ] - -mean value: 0.011502623558044434 - -key: test_mcc -value: [0.22537447 0.73192505 0.23809524 0.73192505 0.53674504 0.73192505 - 0.69047619 0.54761905 0.84515425 0.70710678] - -mean value: 0.5986346184289039 - -key: train_mcc -value: [0.69641781 0.96521476 0.87836615 0.83863125 1. 0.93264992 - 0.64227406 1. 0.93325653 0.98290472] - -mean value: 0.8869715198554682 - -key: test_fscore -value: [0.44444444 0.85714286 0.61538462 0.85714286 0.8 0.83333333 - 0.85714286 0.76923077 0.90909091 0.85714286] - -mean value: 0.7800055500055499 - -key: train_fscore -value: [0.79166667 0.98275862 0.94017094 0.92063492 1. 0.96363636 - 0.82608696 1. 0.96428571 0.99145299] - -mean value: 0.938069317405899 - -key: test_precision -value: [0.66666667 0.75 0.57142857 0.75 0.75 1. - 0.85714286 0.83333333 1. 0.75 ] - -mean value: 0.7928571428571429 - -key: train_precision -value: [1. 0.98275862 0.93220339 0.85294118 1. 1. - 0.7037037 1. 1. 0.98305085] - -mean value: 0.9454657738152082 - -key: test_recall -value: [0.33333333 1. 0.66666667 1. 0.85714286 0.71428571 - 0.85714286 0.71428571 0.83333333 1. ] - -mean value: 0.7976190476190476 - -key: train_recall -value: [0.65517241 0.98275862 0.94827586 1. 1. 0.92982456 - 1. 1. 0.93103448 1. ] - -mean value: 0.9447065940713854 - -key: test_accuracy -value: [0.61538462 0.84615385 0.61538462 0.84615385 0.76923077 0.84615385 - 0.84615385 0.76923077 0.91666667 0.83333333] - -mean value: 0.7903846153846155 - -key: train_accuracy -value: [0.82608696 0.9826087 0.93913043 0.91304348 1. 0.96521739 - 0.79130435 1. 0.96551724 0.99137931] - -mean value: 0.9374287856071962 - -key: test_roc_auc -value: [0.5952381 0.85714286 0.61904762 0.85714286 0.76190476 0.85714286 - 0.8452381 0.77380952 0.91666667 0.83333333] - -mean value: 0.7916666666666667 - -key: train_roc_auc -value: [0.82758621 0.98260738 0.93905021 0.9122807 1. 0.96491228 - 0.79310345 1. 0.96551724 0.99137931] - -mean value: 0.9376436781609195 - -key: test_jcc -value: [0.28571429 0.75 0.44444444 0.75 0.66666667 0.71428571 - 0.75 0.625 0.83333333 0.75 ] - -mean value: 0.6569444444444443 - -key: train_jcc -value: [0.65517241 0.96610169 0.88709677 0.85294118 1. 0.92982456 - 0.7037037 1. 0.93103448 0.98305085] - -mean value: 0.8908925654695956 - -key: TN -value: 50 - -mean value: 50.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 51 - -mean value: 51.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.64 - -Accuracy on Blind test: 0.84 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.1059854 0.08800244 0.08774233 0.08785915 0.08800817 0.08856511 - 0.08835125 0.09002113 0.09012151 0.08924937] - -mean value: 0.09039058685302734 - -key: score_time -value: [0.01466513 0.01457238 0.01447558 0.01458764 0.01465821 0.01449251 - 0.01508141 0.01487041 0.01488805 0.01517725] - -mean value: 0.014746856689453126 - -key: test_mcc -value: [1. 1. 0.85714286 1. 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9571428571428571 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.92307692 1. 1. 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9769230769230768 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.85714286 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9857142857142858 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9714285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.92307692 1. 1. 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.976923076923077 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.92857143 1. 1. 0.92857143 - 1. 0.92857143 1. 1. ] - -mean value: 0.9785714285714286 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.85714286 1. 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9571428571428571 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 1 - -mean value: 1.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03008389 0.04762316 0.02873278 0.0335021 0.02972507 0.02821159 - 0.03297353 0.0320456 0.03593206 0.05288649] - -mean value: 0.03517162799835205 - -key: score_time -value: [0.01709604 0.02524185 0.02258182 0.0180335 0.01741457 0.01950884 - 0.02742362 0.02516723 0.02462244 0.03974295] - -mean value: 0.0236832857131958 - -key: test_mcc -value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9428571428571428 - -key: train_mcc -value: [1. 1. 0.98275862 0.98275862 0.98275345 0.98275345 - 0.98275345 1. 1. 1. ] - -mean value: 0.9913777588135837 - -key: test_fscore -value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9692307692307691 - -key: train_fscore -value: [1. 1. 0.99130435 0.99130435 0.99115044 0.99115044 - 0.99115044 1. 1. 1. ] - -mean value: 0.9956060023085802 - -key: test_precision -value: [1. 1. 0.85714286 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9857142857142858 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9571428571428571 - -key: train_recall -value: [1. 1. 0.98275862 0.98275862 0.98245614 0.98245614 - 0.98245614 1. 1. 1. ] - -mean value: 0.9912885662431942 - -key: test_accuracy -value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9692307692307693 - -key: train_accuracy -value: [1. 1. 0.99130435 0.99130435 0.99130435 0.99130435 - 0.99130435 1. 1. 1. ] - -mean value: 0.9956521739130435 - -key: test_roc_auc -value: [1. 1. 0.92857143 1. 0.92857143 0.92857143 - 1. 0.92857143 1. 1. ] - -mean value: 0.9714285714285715 - -key: train_roc_auc -value: [1. 1. 0.99137931 0.99137931 0.99122807 0.99122807 - 0.99122807 1. 1. 1. ] - -mean value: 0.9956442831215971 - -key: test_jcc -value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9428571428571428 - -key: train_jcc -value: [1. 1. 0.98275862 0.98275862 0.98245614 0.98245614 - 0.98245614 1. 1. 1. ] - -mean value: 0.9912885662431942 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 1 - -mean value: 1.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03084588 0.03903985 0.04139996 0.03601909 0.04611945 0.0508759 - 0.03565288 0.0392096 0.03545499 0.01767039] - -mean value: 0.03722879886627197 - -key: score_time -value: [0.02145314 0.02262902 0.02137327 0.02144718 0.02134752 0.02161336 - 0.02137446 0.01924396 0.0216949 0.01203442] - -mean value: 0.02042112350463867 - -key: test_mcc -value: [-0.23809524 0.23809524 -0.14085904 0.21957752 0.05143445 0.28288947 - -0.03289758 0.39477102 0.35355339 0.50709255] - -mean value: 0.16355617745347897 - -key: train_mcc -value: [0.96580942 0.98275862 0.98275862 0.98275862 0.98275345 0.96578908 - 0.98275345 0.98275345 0.98290472 0.98290472] - -mean value: 0.9793944154512012 - -key: test_fscore -value: [0.33333333 0.61538462 0.22222222 0.54545455 0.625 0.54545455 - 0.22222222 0.44444444 0.71428571 0.72727273] - -mean value: 0.499507437007437 - -key: train_fscore -value: [0.98245614 0.99130435 0.99130435 0.99130435 0.99115044 0.98214286 - 0.99115044 0.99115044 0.99130435 0.99130435] - -mean value: 0.9894572064057797 - -key: test_precision -value: [0.33333333 0.57142857 0.33333333 0.6 0.55555556 0.75 - 0.5 1. 0.625 0.8 ] - -mean value: 0.6068650793650793 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.33333333 0.66666667 0.16666667 0.5 0.71428571 0.42857143 - 0.14285714 0.28571429 0.83333333 0.66666667] - -mean value: 0.4738095238095238 - -key: train_recall -value: [0.96551724 0.98275862 0.98275862 0.98275862 0.98245614 0.96491228 - 0.98245614 0.98245614 0.98275862 0.98275862] - -mean value: 0.9791591046581971 - -key: test_accuracy -value: [0.38461538 0.61538462 0.46153846 0.61538462 0.53846154 0.61538462 - 0.46153846 0.61538462 0.66666667 0.75 ] - -mean value: 0.5724358974358975 - -key: train_accuracy -value: [0.9826087 0.99130435 0.99130435 0.99130435 0.99130435 0.9826087 - 0.99130435 0.99130435 0.99137931 0.99137931] - -mean value: 0.9895802098950526 - -key: test_roc_auc -value: [0.38095238 0.61904762 0.44047619 0.60714286 0.52380952 0.63095238 - 0.48809524 0.64285714 0.66666667 0.75 ] - -mean value: 0.575 - -key: train_roc_auc -value: [0.98275862 0.99137931 0.99137931 0.99137931 0.99122807 0.98245614 - 0.99122807 0.99122807 0.99137931 0.99137931] - -mean value: 0.9895795523290986 - -key: test_jcc -value: [0.2 0.44444444 0.125 0.375 0.45454545 0.375 - 0.125 0.28571429 0.55555556 0.57142857] - -mean value: 0.35116883116883113 - -key: train_jcc -value: [0.96551724 0.98275862 0.98275862 0.98275862 0.98245614 0.96491228 - 0.98245614 0.98245614 0.98275862 0.98275862] - -mean value: 0.9791591046581971 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 34 - -mean value: 34.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 30 - -mean value: 30.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.62 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.18425417 0.16746235 0.16713881 0.16807127 0.16799498 0.13374901 - 0.16947222 0.16639709 0.17026401 0.16803885] - -mean value: 0.16628427505493165 - -key: score_time -value: [0.00870323 0.00875211 0.00923729 0.00900006 0.0088172 0.00897241 - 0.00904417 0.00871325 0.00923657 0.00879717] - -mean value: 0.008927345275878906 - -key: test_mcc -value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9285714285714285 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9615384615384615 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 1. 0.85714286 0.85714286 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9571428571428571 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9714285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 - 1. 0.92307692 1. 1. ] - -mean value: 0.9615384615384615 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 - 1. 0.92857143 1. 1. ] - -mean value: 0.9642857142857142 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 - 1. 0.85714286 1. 1. ] - -mean value: 0.9285714285714285 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 61 - -mean value: 61.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.95 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis() - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01616454 0.01594663 0.01507449 0.01565361 0.01556253 0.01537538 - 0.01601553 0.01538396 0.01568604 0.01597762] - -mean value: 0.015684032440185548 - -key: score_time -value: [0.01205802 0.01172352 0.01197767 0.01179051 0.01297235 0.01291943 - 0.01175404 0.01311636 0.01326632 0.01292157] - -mean value: 0.012449979782104492 - -key: test_mcc -value: [-0.69047619 0.38095238 0.05143445 -0.05143445 0.22537447 0.38575837 - -0.38095238 -0.05143445 -0.16903085 0.19245009] - -mean value: -0.010735855884654828 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.15384615 0.66666667 0.4 0.53333333 0.70588235 0.75 - 0.30769231 0.36363636 0.46153846 0.66666667] - -mean value: 0.500926230632113 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.14285714 0.66666667 0.5 0.44444444 0.6 0.66666667 - 0.33333333 0.5 0.42857143 0.55555556] - -mean value: 0.4838095238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.16666667 0.66666667 0.33333333 0.66666667 0.85714286 0.85714286 - 0.28571429 0.28571429 0.5 0.83333333] - -mean value: 0.5452380952380952 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.15384615 0.69230769 0.53846154 0.46153846 0.61538462 0.69230769 - 0.30769231 0.46153846 0.41666667 0.58333333] - -mean value: 0.49230769230769234 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.1547619 0.69047619 0.52380952 0.47619048 0.5952381 0.67857143 - 0.30952381 0.47619048 0.41666667 0.58333333] - -mean value: 0.4904761904761905 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.08333333 0.5 0.25 0.36363636 0.54545455 0.6 - 0.18181818 0.22222222 0.3 0.5 ] - -mean value: 0.35464646464646465 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 28 - -mean value: 28.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 36 - -mean value: 36.0 - -key: TP -value: 36 - -mean value: 36.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.11 - -Accuracy on Blind test: 0.5 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.04481435 0.05445457 0.0491457 0.05211663 0.01283717 0.01266241 - 0.01273108 0.01271272 0.04638553 0.05051947] - -mean value: 0.034837961196899414 - -key: score_time -value: [0.02102113 0.02327156 0.02284217 0.01170707 0.01157761 0.01172662 - 0.01173377 0.01661181 0.02016473 0.02096701] - -mean value: 0.017162346839904787 - -key: test_mcc -value: [0.41475753 0.38095238 0.38095238 1. 0.7200823 0.6172134 - 0.6172134 0.41475753 0.84515425 0.84515425] - -mean value: 0.6236237432887879 - -key: train_mcc -value: [1. 1. 0.96521476 1. 1. 1. - 0.98275345 1. 1. 1. ] - -mean value: 0.9947968209959374 - -key: test_fscore -value: [0.71428571 0.66666667 0.66666667 1. 0.875 0.72727273 - 0.72727273 0.66666667 0.90909091 0.92307692] - -mean value: 0.7875999000999001 - -key: train_fscore -value: [1. 1. 0.98275862 1. 1. 1. - 0.99115044 1. 1. 1. ] - -mean value: 0.9973909063167531 - -key: test_precision -value: [0.625 0.66666667 0.66666667 1. 0.77777778 1. - 1. 0.8 1. 0.85714286] - -mean value: 0.8393253968253968 - -key: train_precision -value: [1. 1. 0.98275862 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9982758620689655 - -key: test_recall -value: [0.83333333 0.66666667 0.66666667 1. 1. 0.57142857 - 0.57142857 0.57142857 0.83333333 1. ] - -mean value: 0.7714285714285715 - -key: train_recall -value: [1. 1. 0.98275862 1. 1. 1. - 0.98245614 1. 1. 1. ] - -mean value: 0.9965214761040532 - -key: test_accuracy -value: [0.69230769 0.69230769 0.69230769 1. 0.84615385 0.76923077 - 0.76923077 0.69230769 0.91666667 0.91666667] - -mean value: 0.7987179487179488 - -key: train_accuracy -value: [1. 1. 0.9826087 1. 1. 1. - 0.99130435 1. 1. 1. ] - -mean value: 0.9973913043478261 - -key: test_roc_auc -value: [0.70238095 0.69047619 0.69047619 1. 0.83333333 0.78571429 - 0.78571429 0.70238095 0.91666667 0.91666667] - -mean value: 0.8023809523809524 - -key: train_roc_auc -value: [1. 1. 0.98260738 1. 1. 1. - 0.99122807 1. 1. 1. ] - -mean value: 0.9973835450695704 - -key: test_jcc -value: [0.55555556 0.5 0.5 1. 0.77777778 0.57142857 - 0.57142857 0.5 0.83333333 0.85714286] - -mean value: 0.6666666666666666 - -key: train_jcc -value: [1. 1. 0.96610169 1. 1. 1. - 0.98245614 1. 1. 1. ] - -mean value: 0.9948557835266131 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.64 - -Accuracy on Blind test: 0.84 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: 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 - rus_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: 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 - rus_CV['Resampling'] = rs_rus -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: 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 - rus_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: 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 - rus_BT['Resampling'] = rs_rus -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.10236979 0.19759798 0.20445657 0.26835752 0.19444537 0.18951321 - 0.18658042 0.20327044 0.2164557 0.1654892 ] - -mean value: 0.19285361766815184 - -key: score_time -value: [0.0115881 0.02972293 0.02169919 0.02145386 0.02297568 0.02107 - 0.02039909 0.02235222 0.02386165 0.01167202] - -mean value: 0.020679473876953125 - -key: test_mcc -value: [0.41475753 0.38095238 0.38095238 1. 0.7200823 0.41475753 - 0.6172134 0.41475753 0.33333333 0.70710678] - -mean value: 0.5383913169105485 - -key: train_mcc -value: [1. 1. 0.96521476 1. 1. 1. - 0.98275345 1. 1. 1. ] - -mean value: 0.9947968209959374 - -key: test_fscore -value: [0.71428571 0.66666667 0.66666667 1. 0.875 0.66666667 - 0.72727273 0.66666667 0.66666667 0.85714286] - -mean value: 0.7507034632034632 - -key: train_fscore -value: [1. 1. 0.98275862 1. 1. 1. - 0.99115044 1. 1. 1. ] - -mean value: 0.9973909063167531 - -key: test_precision -value: [0.625 0.66666667 0.66666667 1. 0.77777778 0.8 - 1. 0.8 0.66666667 0.75 ] - -mean value: 0.7752777777777778 - -key: train_precision -value: [1. 1. 0.98275862 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9982758620689655 - -key: test_recall -value: [0.83333333 0.66666667 0.66666667 1. 1. 0.57142857 - 0.57142857 0.57142857 0.66666667 1. ] - -mean value: 0.7547619047619049 - -key: train_recall -value: [1. 1. 0.98275862 1. 1. 1. - 0.98245614 1. 1. 1. ] - -mean value: 0.9965214761040532 - -key: test_accuracy -value: [0.69230769 0.69230769 0.69230769 1. 0.84615385 0.69230769 - 0.76923077 0.69230769 0.66666667 0.83333333] - -mean value: 0.7576923076923077 - -key: train_accuracy -value: [1. 1. 0.9826087 1. 1. 1. - 0.99130435 1. 1. 1. ] - -mean value: 0.9973913043478261 - -key: test_roc_auc -value: [0.70238095 0.69047619 0.69047619 1. 0.83333333 0.70238095 - 0.78571429 0.70238095 0.66666667 0.83333333] - -mean value: 0.7607142857142857 - -key: train_roc_auc -value: [1. 1. 0.98260738 1. 1. 1. - 0.99122807 1. 1. 1. ] - -mean value: 0.9973835450695704 - -key: test_jcc -value: [0.55555556 0.5 0.5 1. 0.77777778 0.5 - 0.57142857 0.5 0.5 0.75 ] - -mean value: 0.6154761904761904 - -key: train_jcc -value: [1. 1. 0.96610169 1. 1. 1. - 0.98245614 1. 1. 1. ] - -mean value: 0.9948557835266131 - -key: TN -value: 49 - -mean value: 49.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 64 - -mean value: 64.0 - -key: trainingY_pos -value: 64 - -mean value: 64.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.64 - -Accuracy on Blind test: 0.84 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03761697 0.03439021 0.03464413 0.03514004 0.03435802 0.0339179 - 0.03460574 0.03575659 0.03499985 0.03665447] - -mean value: 0.035208392143249514 - -key: score_time -value: [0.01418996 0.01417136 0.01432729 0.0129149 0.01295686 0.01291966 - 0.01281643 0.01288414 0.01295233 0.01192045] - -mean value: 0.013205337524414062 - -key: test_mcc -value: [0.83624201 1. 0.91485507 0.75474102 0.91485507 0.95833333 - 0.7085716 0.91485507 0.87318841 0.71722586] - -mean value: 0.8592867442031116 - -key: train_mcc -value: [0.95283019 0.96725018 0.97180697 0.95298209 0.93883291 0.9576579 - 0.9435291 0.95294092 0.93883426 0.93883426] - -mean value: 0.9515498776477427 - -key: test_fscore -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.92 1. 0.95652174 0.85714286 0.95652174 0.9787234 - 0.84444444 0.95833333 0.93617021 0.86792453] - -mean value: 0.9275782258504666 - -key: train_fscore -value: [0.97641509 0.98368298 0.98598131 0.97663551 0.96955504 0.97882353 - 0.97169811 0.97641509 0.96941176 0.96941176] - -mean value: 0.9758030201952016 - -key: test_precision -value: [0.88461538 1. 0.95652174 0.94736842 0.95652174 0.95833333 - 0.9047619 0.95833333 0.95652174 0.79310345] - -mean value: 0.9316081042763755 - -key: train_precision -value: [0.97641509 0.97235023 0.98139535 0.97209302 0.96728972 0.98113208 - 0.97169811 0.97641509 0.96713615 0.96713615] - -mean value: 0.9733060999961912 - -key: test_recall -value: [0.95833333 1. 0.95652174 0.7826087 0.95652174 1. - 0.79166667 0.95833333 0.91666667 0.95833333] - -mean value: 0.9278985507246377 - -key: train_recall -value: [0.97641509 0.99528302 0.99061033 0.98122066 0.97183099 0.97652582 - 0.97169811 0.97641509 0.97169811 0.97169811] - -mean value: 0.9783395340597041 - -key: test_accuracy -value: [0.91666667 1. 0.95744681 0.87234043 0.95744681 0.9787234 - 0.85106383 0.95744681 0.93617021 0.85106383] - -mean value: 0.9278368794326243 - -key: train_accuracy -value: [0.97641509 0.98349057 0.98588235 0.97647059 0.96941176 0.97882353 - 0.97176471 0.97647059 0.96941176 0.96941176] - -mean value: 0.9757552719200888 - -key: test_roc_auc -value: [0.91666667 1. 0.95742754 0.87047101 0.95742754 0.97916667 - 0.85235507 0.95742754 0.9365942 0.84873188] - -mean value: 0.9276268115942029 - -key: train_roc_auc -value: [0.97641509 0.98349057 0.9858712 0.97645939 0.96940606 0.97882895 - 0.97176455 0.97647046 0.96941713 0.96941713] - -mean value: 0.9757540526175923 - -key: test_jcc -value: [0.85185185 1. 0.91666667 0.75 0.91666667 0.95833333 - 0.73076923 0.92 0.88 0.76666667] - -mean value: 0.8690954415954415 - -key: train_jcc -value: [0.95391705 0.96788991 0.97235023 0.9543379 0.94090909 0.95852535 - 0.94495413 0.95391705 0.94063927 0.94063927] - -mean value: 0.9528079243381858 - -key: TN -value: 219 - -mean value: 219.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 219 - -mean value: 219.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.89 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.78390193 0.94591141 0.78064728 0.92265105 0.77569675 0.75856495 - 0.89818215 0.78333569 0.78202963 1.12071753] - -mean value: 0.855163836479187 - -key: score_time -value: [0.01343822 0.01443529 0.01470709 0.01484108 0.01459599 0.01428747 - 0.0131669 0.01431489 0.02354884 0.01364064] - -mean value: 0.015097641944885254 - -key: test_mcc -value: [0.91986621 0.9591663 0.95833333 0.91485507 0.95833333 0.95833333 - 0.95825929 0.91804649 1. 0.84147165] - -mean value: 0.9386665021079992 - -key: train_mcc -value: [0.98594778 1. 1. 0.98598008 1. 0.99063185 - 1. 0.985981 1. 1. ] - -mean value: 0.9948540708331428 - -key: test_fscore -value: [0.96 0.97959184 0.9787234 0.95652174 0.9787234 0.9787234 - 0.97959184 0.96 1. 0.92307692] - -mean value: 0.9694952548442703 - -key: train_fscore -value: [0.99297424 1. 1. 0.99300699 1. 0.9953271 - 1. 0.99297424 1. 1. ] - -mean value: 0.9974282573562487 - -key: test_precision -value: [0.92307692 0.96 0.95833333 0.95652174 0.95833333 0.95833333 - 0.96 0.92307692 1. 0.85714286] - -mean value: 0.9454818442427138 - -key: train_precision -value: [0.98604651 1. 1. 0.98611111 1. 0.99069767 - 1. 0.98604651 1. 1. ] - -mean value: 0.9948901808785531 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9956521739130434 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95833333 0.97916667 0.9787234 0.95744681 0.9787234 0.9787234 - 0.9787234 0.95744681 1. 0.91489362] - -mean value: 0.9682180851063829 - -key: train_accuracy -value: [0.99292453 1. 1. 0.99294118 1. 0.99529412 - 1. 0.99294118 1. 1. ] - -mean value: 0.9974100998890123 - -key: test_roc_auc -value: [0.95833333 0.97916667 0.97916667 0.95742754 0.97916667 0.97916667 - 0.97826087 0.95652174 1. 0.91304348] - -mean value: 0.9680253623188406 - -key: train_roc_auc -value: [0.99292453 1. 1. 0.99292453 1. 0.99528302 - 1. 0.99295775 1. 1. ] - -mean value: 0.9974089821950571 - -key: test_jcc -value: [0.92307692 0.96 0.95833333 0.91666667 0.95833333 0.95833333 - 0.96 0.92307692 1. 0.85714286] - -mean value: 0.9414963369963371 - -key: train_jcc -value: [0.98604651 1. 1. 0.98611111 1. 0.99069767 - 1. 0.98604651 1. 1. ] - -mean value: 0.9948901808785531 - -key: TN -value: 221 - -mean value: 221.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 234 - -mean value: 234.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.93 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01341796 0.01314044 0.0100596 0.00966549 0.0095849 0.01072311 - 0.01043272 0.00973892 0.00969958 0.01060581] - -mean value: 0.010706853866577149 - -key: score_time -value: [0.01171517 0.00988674 0.00951385 0.00905657 0.00912094 0.00920796 - 0.00893307 0.00947523 0.00947046 0.00933242] - -mean value: 0.009571242332458495 - -key: test_mcc -value: [0.54213748 0.35355339 0.74773263 0.27943865 0.45948781 0.74773263 - 0.62091661 0.58127976 0.49819858 0.42102089] - -mean value: 0.5251498421702169 - -key: train_mcc -value: [0.66993058 0.63309049 0.62030596 0.66608339 0.66593578 0.68966441 - 0.69320894 0.65652271 0.68112737 0.60553441] - -mean value: 0.658140403139998 - -key: test_fscore -value: [0.7755102 0.6 0.875 0.58536585 0.74509804 0.875 - 0.82352941 0.80769231 0.72727273 0.66666667] - -mean value: 0.7481135210352263 - -key: train_fscore -value: [0.8364486 0.82110092 0.81632653 0.83135392 0.83449883 0.84722222 - 0.85201794 0.82660333 0.8440367 0.76216216] - -mean value: 0.8271771144180518 - -key: test_precision -value: [0.76 0.75 0.84 0.66666667 0.67857143 0.84 - 0.77777778 0.75 0.8 0.77777778] - -mean value: 0.764079365079365 - -key: train_precision -value: [0.8287037 0.79910714 0.78947368 0.84134615 0.8287037 0.83561644 - 0.81196581 0.83253589 0.82142857 0.89240506] - -mean value: 0.8281286158530381 - -key: test_recall -value: [0.79166667 0.5 0.91304348 0.52173913 0.82608696 0.91304348 - 0.875 0.875 0.66666667 0.58333333] - -mean value: 0.7465579710144927 - -key: train_recall -value: [0.84433962 0.84433962 0.84507042 0.82159624 0.84037559 0.85915493 - 0.89622642 0.82075472 0.86792453 0.66509434] - -mean value: 0.8304876428381611 - -key: test_accuracy -value: [0.77083333 0.66666667 0.87234043 0.63829787 0.72340426 0.87234043 - 0.80851064 0.78723404 0.74468085 0.70212766] - -mean value: 0.7586436170212766 - -key: train_accuracy -value: [0.83490566 0.81603774 0.80941176 0.83294118 0.83294118 0.84470588 - 0.84470588 0.82823529 0.84 0.79294118] - -mean value: 0.8276825749167592 - -key: test_roc_auc -value: [0.77083333 0.66666667 0.87318841 0.63586957 0.72554348 0.87318841 - 0.80706522 0.78532609 0.74637681 0.70471014] - -mean value: 0.7588768115942028 - -key: train_roc_auc -value: [0.83490566 0.81603774 0.80932766 0.83296793 0.83292364 0.8446718 - 0.84482682 0.82821773 0.84006555 0.79264107] - -mean value: 0.827658561431482 - -key: test_jcc -value: [0.63333333 0.42857143 0.77777778 0.4137931 0.59375 0.77777778 - 0.7 0.67741935 0.57142857 0.5 ] - -mean value: 0.6073851347175874 - -key: train_jcc -value: [0.7188755 0.69649805 0.68965517 0.71138211 0.716 0.73493976 - 0.7421875 0.70445344 0.73015873 0.61572052] - -mean value: 0.7059870797225559 - -key: TN -value: 182 - -mean value: 182.0 - -key: FP -value: 60 - -mean value: 60.0 - -key: FN -value: 54 - -mean value: 54.0 - -key: TP -value: 176 - -mean value: 176.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.24 - -Accuracy on Blind test: 0.68 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01091361 0.01103592 0.01082873 0.01045895 0.00958872 0.00965691 - 0.00966382 0.00966477 0.009624 0.00963116] - -mean value: 0.010106658935546875 - -key: score_time -value: [0.00961423 0.00953054 0.00960398 0.00916052 0.00865173 0.00873494 - 0.00867224 0.00870585 0.00867987 0.00867605] - -mean value: 0.009002995491027833 - -key: test_mcc -value: [0.25354628 0.51639778 0.48913043 0.42545532 0.31876614 0.7085716 - 0.44646172 0.40653424 0.31884058 0.32605546] - -mean value: 0.4209759551550246 - -key: train_mcc -value: [0.54318451 0.53172012 0.50722652 0.52031192 0.49399615 0.47474887 - 0.48764745 0.47693583 0.51529177 0.51618878] - -mean value: 0.5067251933766291 - -key: test_fscore -value: [0.59090909 0.71428571 0.73913043 0.63157895 0.63636364 0.85714286 - 0.73469388 0.69565217 0.66666667 0.7037037 ] - -mean value: 0.6970127102686764 - -key: train_fscore -value: [0.76513317 0.75 0.74452555 0.74055416 0.72122762 0.7254902 - 0.73607748 0.71717172 0.75650118 0.74939173] - -mean value: 0.7406072801630106 - -key: test_precision -value: [0.65 0.83333333 0.73913043 0.8 0.66666667 0.80769231 - 0.72 0.72727273 0.66666667 0.63333333] - -mean value: 0.7244095469747645 - -key: train_precision -value: [0.78606965 0.79787234 0.77272727 0.79891304 0.79213483 0.75897436 - 0.75621891 0.77173913 0.75829384 0.77386935] - -mean value: 0.7766812720311039 - -key: test_recall -value: [0.54166667 0.625 0.73913043 0.52173913 0.60869565 0.91304348 - 0.75 0.66666667 0.66666667 0.79166667] - -mean value: 0.6824275362318841 - -key: train_recall -value: [0.74528302 0.70754717 0.71830986 0.69014085 0.66197183 0.69483568 - 0.71698113 0.66981132 0.75471698 0.72641509] - -mean value: 0.7086012932943573 - -key: test_accuracy -value: [0.625 0.75 0.74468085 0.70212766 0.65957447 0.85106383 - 0.72340426 0.70212766 0.65957447 0.65957447] - -mean value: 0.7077127659574467 - -key: train_accuracy -value: [0.77122642 0.76415094 0.75294118 0.75764706 0.74352941 0.73647059 - 0.74352941 0.73647059 0.75764706 0.75764706] - -mean value: 0.7521259711431743 - -key: test_roc_auc -value: [0.625 0.75 0.74456522 0.69836957 0.65851449 0.85235507 - 0.72282609 0.70289855 0.65942029 0.6567029 ] - -mean value: 0.7070652173913043 - -key: train_roc_auc -value: [0.77122642 0.76415094 0.75302285 0.75780627 0.74372176 0.73656878 - 0.74346709 0.73631411 0.75764018 0.75757374] - -mean value: 0.7521492160510231 - -key: test_jcc -value: [0.41935484 0.55555556 0.5862069 0.46153846 0.46666667 0.75 - 0.58064516 0.53333333 0.5 0.54285714] - -mean value: 0.5396158056502884 - -key: train_jcc -value: [0.61960784 0.6 0.59302326 0.588 0.564 0.56923077 - 0.58237548 0.55905512 0.60836502 0.59922179] - -mean value: 0.5882879274114092 - -key: TN -value: 173 - -mean value: 173.0 - -key: FP -value: 75 - -mean value: 75.0 - -key: FN -value: 63 - -mean value: 63.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.03 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00887275 0.009969 0.00995159 0.01022196 0.00914335 0.01016235 - 0.00951147 0.01033115 0.01050925 0.01074362] - -mean value: 0.009941649436950684 - -key: score_time -value: [0.01565647 0.01562095 0.01235104 0.01209283 0.01130271 0.01212144 - 0.01227593 0.01490164 0.01249647 0.0126102 ] - -mean value: 0.013142967224121093 - -key: test_mcc -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -[0.38341289 0.54594868 0.79308818 0.31884058 0.67023783 0.65942029 - 0.54211097 0.46440394 0.4899891 0.40290954] - -mean value: 0.5270362005800171 - -key: train_mcc -value: [0.72426952 0.68335602 0.66440957 0.71015175 0.68941367 0.64633879 - 0.69126867 0.70274704 0.65723256 0.69851923] - -mean value: 0.6867706820106939 - -key: test_fscore -value: [0.71698113 0.78431373 0.88372093 0.65217391 0.83636364 0.82608696 - 0.79245283 0.76363636 0.76 0.74576271] - -mean value: 0.7761492199416529 - -key: train_fscore -value: [0.86784141 0.84978541 0.84095861 0.86206897 0.85287846 0.8329718 - 0.85209713 0.85776805 0.8373102 0.8558952 ] - -mean value: 0.8509575228344838 - -key: test_precision -value: [0.65517241 0.74074074 0.95 0.65217391 0.71875 0.82608696 - 0.72413793 0.67741935 0.73076923 0.62857143] - -mean value: 0.7303821969312914 - -key: train_precision -value: [0.81404959 0.77952756 0.78455285 0.79681275 0.78125 0.77419355 - 0.80082988 0.8 0.7751004 0.79674797] - -mean value: 0.7903064533356285 - -key: test_recall -value: [0.79166667 0.83333333 0.82608696 0.65217391 1. 0.82608696 - 0.875 0.875 0.79166667 0.91666667] - -mean value: 0.838768115942029 - -key: train_recall -value: [0.92924528 0.93396226 0.90610329 0.93896714 0.93896714 0.90140845 - 0.91037736 0.9245283 0.91037736 0.9245283 ] - -mean value: 0.9218464877314201 - -key: test_accuracy -value: [0.6875 0.77083333 0.89361702 0.65957447 0.80851064 0.82978723 - 0.76595745 0.72340426 0.74468085 0.68085106] - -mean value: 0.7564716312056737 - -key: train_accuracy -value: [0.85849057 0.83490566 0.82823529 0.84941176 0.83764706 0.81882353 - 0.84235294 0.84705882 0.82352941 0.84470588] - -mean value: 0.8385160932297447 - -key: test_roc_auc -value: [0.6875 0.77083333 0.89221014 0.65942029 0.8125 0.82971014 - 0.76358696 0.7201087 0.74365942 0.67572464] - -mean value: 0.7555253623188405 - -key: train_roc_auc -value: [0.85849057 0.83490566 0.82805164 0.84920055 0.8374081 0.81862875 - 0.84251262 0.84724068 0.82373328 0.84489326] - -mean value: 0.8385065107626894 - -key: test_jcc -value: [0.55882353 0.64516129 0.79166667 0.48387097 0.71875 0.7037037 - 0.65625 0.61764706 0.61290323 0.59459459] - -mean value: 0.6383371037071227 - -key: train_jcc -value: [0.76653696 0.73880597 0.72556391 0.75757576 0.74349442 0.71375465 - 0.74230769 0.75095785 0.72014925 0.7480916 ] - -mean value: 0.7407238076610564 - -key: TN -value: 159 - -mean value: 159.0 - -key: FP -value: 38 - -mean value: 38.0 - -key: FN -value: 77 - -mean value: 77.0 - -key: TP -value: 198 - -mean value: 198.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.04 - -Accuracy on Blind test: 0.57 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02149534 0.01937175 0.02007341 0.02000952 0.01925445 0.02118301 - 0.01910329 0.01937222 0.02085876 0.01932025] - -mean value: 0.02000420093536377 - -key: score_time -value: [0.01150203 0.01241422 0.01137638 0.01128054 0.01172709 0.01148748 - 0.01127481 0.01146984 0.01192999 0.01131344] - -mean value: 0.011577582359313965 - -key: test_mcc -value: [0.62554324 0.83624201 0.87318841 0.57560058 0.7023605 0.87318841 - 0.53734864 0.83303222 0.75645593 0.53176131] - -mean value: 0.7144721256854312 - -key: train_mcc -value: [0.79372539 0.77482717 0.85412791 0.82151485 0.75250476 0.82132891 - 0.83090737 0.82588861 0.79806344 0.8074811 ] - -mean value: 0.8080369516528417 - -key: test_fscore -value: [0.80851064 0.91304348 0.93617021 0.77272727 0.84444444 0.93617021 - 0.75555556 0.91304348 0.86363636 0.7755102 ] - -mean value: 0.8518811860796796 - -key: train_fscore -value: [0.89320388 0.88349515 0.92705882 0.90952381 0.87104623 0.90995261 - 0.9138756 0.91252955 0.89688249 0.90167866] - -mean value: 0.9019246797517653 - -key: test_precision -value: [0.82608696 0.95454545 0.91666667 0.80952381 0.86363636 0.91666667 - 0.80952381 0.95454545 0.95 0.76 ] - -mean value: 0.8761195181629964 - -key: train_precision -value: [0.92 0.91 0.92924528 0.92270531 0.9040404 0.91866029 - 0.92718447 0.91469194 0.91219512 0.91707317] - -mean value: 0.917579598998058 - -key: test_recall -value: [0.79166667 0.875 0.95652174 0.73913043 0.82608696 0.95652174 - 0.70833333 0.875 0.79166667 0.79166667] - -mean value: 0.8311594202898551 - -key: train_recall -value: [0.86792453 0.85849057 0.92488263 0.89671362 0.84037559 0.90140845 - 0.9009434 0.91037736 0.88207547 0.88679245] - -mean value: 0.8869984055275045 - -key: test_accuracy -value: [0.8125 0.91666667 0.93617021 0.78723404 0.85106383 0.93617021 - 0.76595745 0.91489362 0.87234043 0.76595745] - -mean value: 0.855895390070922 - -key: train_accuracy -value: [0.89622642 0.88679245 0.92705882 0.91058824 0.87529412 0.91058824 - 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-mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.38 - -Accuracy on Blind test: 0.79 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.27547193 1.79986334 2.08108068 1.87422729 1.80815768 1.70918036 - 1.83464098 1.91837955 1.86248994 1.87331986] - -mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -1.8036811590194701 - -key: score_time -value: [0.01231146 0.01532292 0.01324606 0.01445222 0.01483798 0.01499081 - 0.01235771 0.0146966 0.01462078 0.01356864] - -mean value: 0.014040517807006835 - -key: test_mcc -value: [0.9591663 0.91986621 0.91833182 0.87318841 0.95833333 0.95833333 - 0.91804649 0.95825929 1. 0.8047833 ] - -mean value: 0.9268308495362693 - -key: train_mcc -value: [0.99061012 1. 0.99063185 0.99530506 0.99530506 1. - 0.99063227 0.99063227 0.99063227 0.99530516] - -mean value: 0.993905406072443 - -key: test_fscore -value: [0.97959184 0.95652174 0.95833333 0.93617021 0.9787234 0.9787234 - 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-mean value: 31.0 - -MCC on Blind test: 0.64 - -Accuracy on Blind test: 0.88 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02307987 0.02041054 0.01674342 0.0163703 0.01677394 0.01718926 - 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-key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.95744681 - 1. 1. 1. 0.95744681] - -mean value: 0.980895390070922 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.95833333 - 1. 1. 1. 0.95652174] - -mean value: 0.9809782608695654 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.92 - 1. 1. 1. 0.92307692] - -mean value: 0.9636410256410256 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.92 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.10994768 0.1118784 0.11284971 0.11134195 0.11294222 0.11214495 - 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1. 0.95833333 1. 1. ] - -mean value: 0.9659420289855072 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.9375 1. 0.89361702 0.91489362 0.9787234 - 0.9787234 0.9787234 1. 0.9787234 ] - -mean value: 0.959840425531915 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.9375 1. 0.89402174 0.91485507 0.97916667 - 0.97826087 0.97916667 1. 0.97826087] - -mean value: 0.959873188405797 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.88888889 0.875 1. 0.80769231 0.84 0.95833333 - 0.96 0.95833333 1. 0.96 ] - -mean value: 0.9248247863247865 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 225 - -mean value: 225.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 228 - -mean value: 228.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.4 - -Accuracy on Blind test: 0.83 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01066399 0.00968838 0.01067972 0.0100224 0.00957918 0.0111053 - 0.01065373 0.01077867 0.01065278 0.00983286] - -mean value: 0.010365700721740723 - -key: score_time -value: [0.00920367 0.00877094 0.00928211 0.00929713 0.0091722 0.0094471 - 0.00929952 0.00941586 0.00855613 0.0093224 ] - -mean value: 0.00917670726776123 - -key: test_mcc -value: [0.77459667 0.79235477 0.87979456 0.79418308 0.87979456 0.87979456 - 0.8047833 0.95833333 0.95825929 0.84147165] - -mean value: 0.8563365763657028 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.89361702 0.93877551 0.89795918 0.93877551 0.93877551 - 0.90566038 0.9787234 0.97959184 0.92307692] - -mean value: 0.9283844165876625 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.91304348 0.88461538 0.84615385 0.88461538 0.88461538 - 0.82758621 1. 0.96 0.85714286] - -mean value: 0.8857772542300278 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.875 1. 0.95652174 1. 1. - 1. 0.95833333 1. 1. ] - -mean value: 0.9789855072463768 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.89583333 0.93617021 0.89361702 0.93617021 0.93617021 - 0.89361702 0.9787234 0.9787234 0.91489362] - -mean value: 0.9238918439716312 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.89583333 0.9375 0.89492754 0.9375 0.9375 - 0.89130435 0.97916667 0.97826087 0.91304348] - -mean value: 0.9240036231884059 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.80769231 0.88461538 0.81481481 0.88461538 0.88461538 - 0.82758621 0.95833333 0.96 0.85714286] - -mean value: 0.8679415673726018 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 205 - -mean value: 205.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 31 - -mean value: 31.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.19 - -Accuracy on Blind test: 0.74 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.573318 1.54127455 1.53914356 1.51387715 1.52104235 1.51077247 - 1.55802274 1.57117796 1.55043197 1.55481386] - -mean value: 1.5433874607086182 - -key: score_time -value: [0.09027529 0.09085727 0.09054208 0.09096718 0.09015322 0.08981037 - 0.09099841 0.09189463 0.0910449 0.09459853] - -mean value: 0.09111418724060058 - -key: test_mcc -value: [1. 0.91986621 0.95833333 0.91485507 0.95833333 0.95833333 - 1. 0.95825929 1. 1. ] - -mean value: 0.9667980574877196 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.95652174 0.9787234 0.95652174 0.9787234 0.9787234 - 1. 0.97959184 1. 1. ] - -mean value: 0.982880552776152 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.95833333 0.95652174 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9791521739130434 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.91666667 1. 0.95652174 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9873188405797102 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.95833333 0.9787234 0.95744681 0.9787234 0.9787234 - 1. 0.9787234 1. 1. ] - -mean value: 0.9830673758865249 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95833333 0.97916667 0.95742754 0.97916667 0.97916667 - 1. 0.97826087 1. 1. ] - -mean value: 0.9831521739130433 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -key: test_jcc -value: [1. 0.91666667 0.95833333 0.91666667 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9668333333333333 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 231 - -mean value: 231.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 233 - -mean value: 233.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.91 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.91157484 0.93154073 0.91679835 0.99111915 0.93584204 0.93280792 - 0.90386271 0.93082833 0.93126726 0.91790628] - -mean value: 0.9303547620773316 - -key: score_time -value: [0.21734333 0.20787477 0.17531276 0.21779132 0.21399546 0.2189126 - 0.19838953 0.20722294 0.19999313 0.203017 ] - -mean value: 0.20598528385162354 - -key: test_mcc -value: [0.9591663 0.87576054 0.95833333 0.91485507 0.8729597 0.95833333 - 1. 0.95825929 1. 0.91804649] - -mean value: 0.9415714063303422 - -key: train_mcc -value: [0.98594778 0.98594778 0.99063185 0.99063185 0.99063185 0.99530506 - 0.99063227 0.98589335 0.98589335 0.985981 ] - -mean value: 0.9887496142619037 - -key: test_fscore -value: [0.97959184 0.93617021 0.9787234 0.95652174 0.93333333 0.9787234 - 1. 0.97959184 1. 0.96 ] - -mean value: 0.9702655767209751 - -key: train_fscore -value: [0.99297424 0.99297424 0.9953271 0.9953271 0.9953271 0.99765808 - 0.99530516 0.99294118 0.99294118 0.99297424] - -mean value: 0.9943749621924566 - -key: test_precision -value: [0.96 0.95652174 0.95833333 0.95652174 0.95454545 0.95833333 - 1. 0.96 1. 0.92307692] - -mean value: 0.9627332522549914 - -key: train_precision -value: [0.98604651 0.98604651 0.99069767 0.99069767 0.99069767 0.9953271 - 0.99065421 0.99061033 0.99061033 0.98604651] - -mean value: 0.9897434523827744 - -key: test_recall -value: [1. 0.91666667 1. 0.95652174 0.91304348 1. - 1. 1. 1. 1. ] - -mean value: 0.9786231884057971 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 0.99528302 0.99528302 1. ] - -mean value: 0.999056603773585 - -key: test_accuracy -value: [0.97916667 0.9375 0.9787234 0.95744681 0.93617021 0.9787234 - 1. 0.9787234 1. 0.95744681] - -mean value: 0.9703900709219859 - -key: train_accuracy -value: [0.99292453 0.99292453 0.99529412 0.99529412 0.99529412 0.99764706 - 0.99529412 0.99294118 0.99294118 0.99294118] - -mean value: 0.9943496115427303 - -key: test_roc_auc -value: [0.97916667 0.9375 0.97916667 0.95742754 0.93568841 0.97916667 - 1. 0.97826087 1. 0.95652174] - -mean value: 0.9702898550724639 - -key: train_roc_auc -value: [0.99292453 0.99292453 0.99528302 0.99528302 0.99528302 0.99764151 - 0.99530516 0.99294667 0.99294667 0.99295775] - -mean value: 0.9943495880946054 - -key: test_jcc -value: [0.96 0.88 0.95833333 0.91666667 0.875 0.95833333 - 1. 0.96 1. 0.92307692] - -mean value: 0.9431410256410256 - -key: train_jcc -value: [0.98604651 0.98604651 0.99069767 0.99069767 0.99069767 0.9953271 - 0.99065421 0.98598131 0.98598131 0.98604651] - -mean value: 0.9888176483373179 - -key: TN -value: 227 - -mean value: 227.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.08122516 0.0644002 0.06695461 0.06452489 0.06639028 0.06484818 - 0.066329 0.0656693 0.06637669 0.0666132 ] - -mean value: 0.06733314990997315 - -key: score_time -value: [0.01088309 0.010566 0.01057339 0.01108551 0.01054788 0.01051974 - 0.01045942 0.01042914 0.01063132 0.01047158] - -mean value: 0.010616707801818847 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.95825929 0.95833333 0.95833333 - 1. 0.95825929 1. 1. ] - -mean value: 0.9750684887473801 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.97777778 0.9787234 0.9787234 - 1. 0.97959184 1. 1. ] - -mean value: 0.9873131664013123 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 1. 0.95833333 1. 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9795 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9956521739130434 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.9787234 0.9787234 0.9787234 - 1. 0.9787234 1. 1. ] - -mean value: 0.9872783687943262 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.97826087 0.97916667 0.97916667 - 1. 0.97826087 1. 1. ] - -mean value: 0.9873188405797102 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9751521739130435 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 231 - -mean value: 231.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.0380466 0.0955472 0.06435776 0.05732799 0.03821635 0.03762507 - 0.09199166 0.03993559 0.03922415 0.08888984] - -mean value: 0.05911622047424316 - -key: score_time -value: [0.02059531 0.0233674 0.02036071 0.01197243 0.01196742 0.0157702 - 0.01203489 0.0119803 0.02232051 0.01203489] - -mean value: 0.016240406036376952 - -key: test_mcc -value: [0.91986621 0.9591663 0.87979456 0.8729597 0.95833333 0.84254172 - 0.91804649 0.84147165 1. 0.84147165] - -mean value: 0.9033651606243817 - -key: train_mcc -value: [0.97208751 0.97668677 0.98134942 0.98134942 0.98598008 0.98598008 - 0.97674215 0.98135106 0.97674215 0.97674215] - -mean value: 0.9795010789882053 - -key: test_fscore -value: [0.96 0.97959184 0.93877551 0.93333333 0.9787234 0.92 - 0.96 0.92307692 1. 0.92307692] - -mean value: 0.9516577930681274 - -key: train_fscore -value: [0.98604651 0.98834499 0.99069767 0.99069767 0.99300699 0.99300699 - 0.98834499 0.99065421 0.98834499 0.98834499] - -mean value: 0.9897490005466532 - -key: test_precision -value: [0.92307692 0.96 0.88461538 0.95454545 0.95833333 0.85185185 - 0.92307692 0.85714286 1. 0.85714286] - -mean value: 0.9169785584785586 - -key: train_precision -value: [0.97247706 0.97695853 0.98156682 0.98156682 0.98611111 0.98611111 - 0.97695853 0.98148148 0.97695853 0.97695853] - -mean value: 0.9797148509859372 - -key: test_recall -value: [1. 1. 1. 0.91304348 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9913043478260869 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95833333 0.97916667 0.93617021 0.93617021 0.9787234 0.91489362 - 0.95744681 0.91489362 1. 0.91489362] - -mean value: 0.9490691489361701 - -key: train_accuracy -value: [0.98584906 0.98820755 0.99058824 0.99058824 0.99294118 0.99294118 - 0.98823529 0.99058824 0.98823529 0.98823529] - -mean value: 0.9896409544950057 - -key: test_roc_auc -value: [0.95833333 0.97916667 0.9375 0.93568841 0.97916667 0.91666667 - 0.95652174 0.91304348 1. 0.91304348] - -mean value: 0.9489130434782608 - -key: train_roc_auc -value: [0.98584906 0.98820755 0.99056604 0.99056604 0.99292453 0.99292453 - 0.98826291 0.99061033 0.98826291 0.98826291] - -mean value: 0.989643679688192 - -key: test_jcc -value: [0.92307692 0.96 0.88461538 0.875 0.95833333 0.85185185 - 0.92307692 0.85714286 1. 0.85714286] - -mean value: 0.9090240130240129 - -key: train_jcc -value: [0.97247706 0.97695853 0.98156682 0.98156682 0.98611111 0.98611111 - 0.97695853 0.98148148 0.97695853 0.97695853] - -mean value: 0.9797148509859372 - -key: TN -value: 214 - -mean value: 214.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 234 - -mean value: 234.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.9 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01346302 0.01294136 0.00970078 0.00945663 0.00930452 0.00928974 - 0.00934792 0.00945902 0.0093205 0.00939202] - -mean value: 0.010167551040649415 - -key: score_time -value: [0.01154852 0.01079345 0.00881791 0.00856185 0.00858855 0.00848055 - 0.00855136 0.00852346 0.00851059 0.00855494] - -mean value: 0.009093117713928223 - -key: test_mcc -value: [0.37532595 0.41812101 0.4899891 0.53176131 0.31884058 0.65942029 - 0.4121128 0.67037015 0.40653424 0.19490273] - -mean value: 0.4477378148478753 - -key: train_mcc -value: [0.49998299 0.53349983 0.55776302 0.5578964 0.49179493 0.50351826 - 0.47945542 0.55012068 0.5560934 0.52144501] - -mean value: 0.52515699447063 - -key: test_fscore -value: [0.69387755 0.69565217 0.72727273 0.75555556 0.65217391 0.82608696 - 0.68181818 0.81818182 0.69565217 0.57777778] - -mean value: 0.7124048829017772 - -key: train_fscore -value: [0.72911392 0.76144578 0.77725118 0.77619048 0.74528302 0.73891626 - 0.72592593 0.7635468 0.76309227 0.73657289] - -mean value: 0.7517338526541064 - -key: test_precision -value: [0.68 0.72727273 0.76190476 0.77272727 0.65217391 0.82608696 - 0.75 0.9 0.72727273 0.61904762] - -mean value: 0.7416485977790325 - -key: train_precision -value: [0.78688525 0.77832512 0.784689 0.78743961 0.74881517 0.77720207 - 0.76165803 0.79896907 0.80952381 0.80446927] - -mean value: 0.7837976402731726 - -key: test_recall -value: [0.70833333 0.66666667 0.69565217 0.73913043 0.65217391 0.82608696 - 0.625 0.75 0.66666667 0.54166667] - -mean value: 0.6871376811594203 - -key: train_recall -value: [0.67924528 0.74528302 0.76995305 0.76525822 0.74178404 0.70422535 - 0.69339623 0.73113208 0.72169811 0.67924528] - -mean value: 0.7231220657276995 - -key: test_accuracy -value: [0.6875 0.70833333 0.74468085 0.76595745 0.65957447 0.82978723 - 0.70212766 0.82978723 0.70212766 0.59574468] - -mean value: 0.7225620567375886 - -key: train_accuracy -value: [0.74764151 0.76650943 0.77882353 0.77882353 0.74588235 0.75058824 - 0.73882353 0.77411765 0.77647059 0.75764706] - -mean value: 0.7615327413984463 - -key: test_roc_auc -value: [0.6875 0.70833333 0.74365942 0.76539855 0.65942029 0.82971014 - 0.70380435 0.83152174 0.70289855 0.59692029] - -mean value: 0.7229166666666667 - -key: train_roc_auc -value: [0.74764151 0.76650943 0.77884445 0.77885552 0.74589202 0.75069758 - 0.73871689 0.77401674 0.77634201 0.75746302] - -mean value: 0.7614979183275754 - -key: test_jcc -value: [0.53125 0.53333333 0.57142857 0.60714286 0.48387097 0.7037037 - 0.51724138 0.69230769 0.53333333 0.40625 ] - -mean value: 0.5579861838301772 - -key: train_jcc -value: [0.57370518 0.61478599 0.63565891 0.63424125 0.59398496 0.5859375 - 0.56976744 0.61752988 0.61693548 0.58299595] - -mean value: 0.6025542551398176 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 74 - -mean value: 74.0 - -key: FN -value: 57 - -mean value: 57.0 - -key: TP -value: 162 - -mean value: 162.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.29 - -Accuracy on Blind test: 0.72 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01703286 0.02359843 0.02437735 0.02722859 0.02431583 0.02274323 - 0.02081561 0.02550769 0.02908754 0.02610683] - -mean value: 0.02408139705657959 - -key: score_time -value: [0.00865674 0.01090193 0.01216245 0.01233125 0.01168108 0.01177025 - 0.01205945 0.01162004 0.01164269 0.01160455] - -mean value: 0.011443042755126953 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.87318841 0.95833333 0.95833333 - 0.29857543 0.87917396 1. 0.87917396] - -mean value: 0.8764278068547181 - -key: train_mcc -value: [0.98594778 0.98130676 0.98598008 0.98598008 0.98598008 0.97180822 - 0.35766119 0.985981 0.96758624 0.985981 ] - -mean value: 0.9194212407461956 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.93617021 0.9787234 0.9787234 - 0.28571429 0.94117647 1. 0.94117647] - -mean value: 0.9019999489157364 - -key: train_fscore -value: [0.99297424 0.99065421 0.99300699 0.99300699 0.99300699 0.98584906 - 0.36923077 0.99297424 0.9837587 0.99297424] - -mean value: 0.9287436427786687 - -key: test_precision -value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.95833333 - 1. 0.88888889 1. 0.88888889] - -mean value: 0.9529444444444444 - -key: train_precision -value: [0.98604651 0.98148148 0.98611111 0.98611111 0.98611111 0.99052133 - 1. 0.98604651 0.96803653 0.98604651] - -mean value: 0.9856512206393118 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 1. - 0.16666667 1. 1. 1. ] - -mean value: 0.9123188405797101 - -key: train_recall -value: [1. 1. 1. 1. 1. 0.98122066 - 0.22641509 1. 1. 1. ] - -mean value: 0.9207635751616617 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.93617021 0.9787234 0.9787234 - 0.57446809 0.93617021 1. 0.93617021] - -mean value: 0.929831560283688 - -key: train_accuracy -value: [0.99292453 0.99056604 0.99294118 0.99294118 0.99294118 0.98588235 - 0.61411765 0.99294118 0.98352941 0.99294118] - -mean value: 0.9531725860155383 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.9365942 0.97916667 0.97916667 - 0.58333333 0.93478261 1. 0.93478261] - -mean value: 0.9306159420289856 - -key: train_roc_auc -value: [0.99292453 0.99056604 0.99292453 0.99292453 0.99292453 0.98589335 - 0.61320755 0.99295775 0.98356808 0.99295775] - -mean value: 0.9530848613694747 - -key: test_jcc -value: [0.96 1. 0.95833333 0.88 0.95833333 0.95833333 - 0.16666667 0.88888889 1. 0.88888889] - -mean value: 0.8659444444444444 - -key: train_jcc -value: [0.98604651 0.98148148 0.98611111 0.98611111 0.98611111 0.97209302 - 0.22641509 0.98604651 0.96803653 0.98604651] - -mean value: 0.9064498996974336 - -key: TN -value: 224 - -mean value: 224.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 215 - -mean value: 215.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.92 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01574564 0.01656222 0.01745152 0.01749682 0.01709723 0.01979041 - 0.01725698 0.01651931 0.01879168 0.01649356] - -mean value: 0.017320537567138673 - -key: score_time -value: [0.01179242 0.01166368 0.01182699 0.01172543 0.01172519 0.01178741 - 0.01175237 0.01176429 0.01189065 0.01179552] - -mean value: 0.011772394180297852 - -key: test_mcc -value: [0.77459667 1. 0.95833333 0.75474102 0.95833333 0.95833333 - 0.29857543 0.73387289 1. 0.62296012] - -mean value: 0.8059746130758967 - -key: train_mcc -value: [0.94939206 0.95299984 0.98134942 0.95774367 0.96758129 0.98598008 - 0.42978041 0.84691467 0.96706971 0.89968417] - -mean value: 0.8938495321242821 - -key: test_fscore -value: [0.88888889 1. 0.9787234 0.85714286 0.9787234 0.9787234 - 0.28571429 0.87272727 1. 0.8 ] - -mean value: 0.8640643517239261 - -key: train_fscore -value: [0.97471264 0.97619048 0.99069767 0.9787234 0.98383372 0.99300699 - 0.47482014 0.92374728 0.98352941 0.94581281] - -mean value: 0.9225074550014183 - -key: test_precision -value: [0.8 1. 0.95833333 0.94736842 0.95833333 0.95833333 - 1. 0.77419355 1. 0.85714286] - -mean value: 0.9253704826582586 - -key: train_precision -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -[0.95067265 0.98557692 0.98156682 0.98571429 0.96818182 0.98611111 - 1. 0.8582996 0.98122066 0.98969072] - -mean value: 0.9687034578168727 - -key: test_recall -value: [1. 1. 1. 0.7826087 1. 1. - 0.16666667 1. 1. 0.75 ] - -mean value: 0.869927536231884 - -key: train_recall -value: [1. 0.96698113 1. 0.97183099 1. 1. - 0.31132075 1. 0.98584906 0.90566038] - -mean value: 0.914164230667021 - -key: test_accuracy -value: [0.875 1. 0.9787234 0.87234043 0.9787234 0.9787234 - 0.57446809 0.85106383 1. 0.80851064] - -mean value: 0.8917553191489361 - -key: train_accuracy -value: [0.9740566 0.97641509 0.99058824 0.97882353 0.98352941 0.99294118 - 0.65647059 0.91764706 0.98352941 0.94823529] - -mean value: 0.9402236403995563 - -key: test_roc_auc -value: [0.875 1. 0.97916667 0.87047101 0.97916667 0.97916667 - 0.58333333 0.84782609 1. 0.80978261] - -mean value: 0.8923913043478262 - -key: train_roc_auc -value: [0.9740566 0.97641509 0.99056604 0.97884002 0.98349057 0.99292453 - 0.65566038 0.91784038 0.98353486 0.94813535] - -mean value: 0.9401463814332536 - -key: test_jcc -value: [0.8 1. 0.95833333 0.75 0.95833333 0.95833333 - 0.16666667 0.77419355 1. 0.66666667] - -mean value: 0.8032526881720431 - -key: train_jcc -value: [0.95067265 0.95348837 0.98156682 0.95833333 0.96818182 0.98611111 - 0.31132075 0.8582996 0.96759259 0.89719626] - -mean value: 0.8832763304869211 - -key: TN -value: 216 - -mean value: 216.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 205 - -mean value: 205.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.46 - -Accuracy on Blind test: 0.84 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.1704247 0.15489531 0.15418124 0.15523338 0.1523037 0.15141606 - 0.1532445 0.15418482 0.15686536 0.15323281] - -mean value: 0.15559818744659423 - -key: score_time -value: [0.01525044 0.01562023 0.01537919 0.01555705 0.01544309 0.01569819 - 0.01545882 0.01533937 0.01538706 0.01550388] - -mean value: 0.015463733673095703 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.95833333 - 1. 0.95825929 1. 1. ] - -mean value: 0.970728066853194 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.9787234 - 1. 0.97959184 1. 1. ] - -mean value: 0.985187562536578 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9751521739130435 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9956521739130434 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.9787234 - 1. 0.9787234 1. 1. ] - -mean value: 0.9851507092198581 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.97916667 - 1. 0.97826087 1. 1. ] - -mean value: 0.985235507246377 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.95833333 - 1. 0.96 1. 1. ] - -mean value: 0.9711666666666666 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 230 - -mean value: 230.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.93 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.0479846 0.05868626 0.06571293 0.05242252 0.05591035 0.05214858 - 0.06340671 0.05452132 0.05256462 0.05731559] - -mean value: 0.05606734752655029 - -key: score_time -value: [0.02042913 0.03717065 0.02822971 0.02923608 0.02547407 0.03469443 - 0.02357244 0.01914525 0.02023339 0.0177238 ] - -mean value: 0.025590896606445312 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.8729597 - 1. 0.95825929 1. 0.95825929] - -mean value: 0.9580166321994591 - -key: train_mcc -value: [0.99529409 0.99056604 0.99058818 0.99063185 0.99530516 0.98589335 - 1. 0.99530506 0.99530506 0.99530506] - -mean value: 0.9934193859946688 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.93333333 - 1. 0.97959184 1. 0.97959184] - -mean value: 0.9786077391178487 - -key: train_fscore -value: [0.99763593 0.99528302 0.99530516 0.9953271 0.99764706 0.99294118 - 1. 0.99763593 0.99763593 0.99763593] - -mean value: 0.9967047256509615 - -key: test_precision -value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95454545 - 1. 0.96 1. 0.96 ] - -mean value: 0.9707733860342558 - -key: train_precision -value: [1. 0.99528302 0.99530516 0.99069767 1. 0.99528302 - 1. 1. 1. 1. ] - -mean value: 0.9976568876473703 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 0.91304348 - 1. 1. 1. 1. ] - -mean value: 0.9869565217391305 - -key: train_recall -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide - oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] -[0.99528302 0.99528302 0.99530516 1. 0.99530516 0.99061033 - 1. 0.99528302 0.99528302 0.99528302] - -mean value: 0.9957635751616618 - -key: test_accuracy -value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.93617021 - 1. 0.9787234 1. 0.9787234 ] - -mean value: 0.9787677304964539 - -key: train_accuracy -value: [0.99764151 0.99528302 0.99529412 0.99529412 0.99764706 0.99294118 - 1. 0.99764706 0.99764706 0.99764706] - -mean value: 0.996704217536071 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.93568841 - 1. 0.97826087 1. 0.97826087] - -mean value: 0.978713768115942 - -key: train_roc_auc -value: [0.99764151 0.99528302 0.99529409 0.99528302 0.99765258 0.99294667 - 1. 0.99764151 0.99764151 0.99764151] - -mean value: 0.996702542297812 - -key: test_jcc -value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.875 - 1. 0.96 1. 0.96 ] - -mean value: 0.9588333333333334 - -key: train_jcc -value: [0.99528302 0.99061033 0.99065421 0.99069767 0.99530516 0.98598131 - 1. 0.99528302 0.99528302 0.99528302] - -mean value: 0.9934380756866741 - -key: TN -value: 229 - -mean value: 229.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 233 - -mean value: 233.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.94 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.17006922 0.20242786 0.20251346 0.20794773 0.19582844 0.158283 - 0.16847277 0.16146183 0.14422131 0.1402185 ] - -mean value: 0.17514441013336182 - -key: score_time -value: [0.02313662 0.02319384 0.02319884 0.0233531 0.02322769 0.02406597 - 0.02945781 0.02915096 0.02370977 0.02334666] - -mean value: 0.024584126472473145 - -key: test_mcc -value: [0.60246408 0.83333333 0.95833333 0.65942029 0.80641033 1. - 0.7023605 0.79308818 0.74773263 0.57227835] - -mean value: 0.7675421024291873 - -key: train_mcc -value: [0.92933834 0.92518727 0.93907982 0.92000886 0.95399286 0.95399286 - 0.92050525 0.94390914 0.90604652 0.93933764] - -mean value: 0.9331398559963876 - -key: test_fscore -value: [0.81481481 0.91666667 0.9787234 0.82608696 0.90196078 1. - 0.85714286 0.90196078 0.86956522 0.80701754] - -mean value: 0.8873939029279801 - -key: train_fscore -value: [0.96487119 0.96296296 0.96983759 0.96018735 0.97706422 0.97706422 - 0.96055684 0.97209302 0.95327103 0.96983759] - -mean value: 0.9667746021193985 - -key: test_precision -value: [0.73333333 0.91666667 0.95833333 0.82608696 0.82142857 1. - 0.84 0.85185185 0.90909091 0.6969697 ] - -mean value: 0.8553761319196103 - -key: train_precision -value: [0.95813953 0.94545455 0.9587156 0.95794393 0.95515695 0.95515695 - 0.94520548 0.9587156 0.94444444 0.9543379 ] - -mean value: 0.9533270923017632 - -key: test_recall -value: [0.91666667 0.91666667 1. 0.82608696 1. 1. - 0.875 0.95833333 0.83333333 0.95833333] - -mean value: 0.9284420289855074 - -key: train_recall -value: [0.97169811 0.98113208 0.98122066 0.96244131 1. 1. - 0.97641509 0.98584906 0.96226415 0.98584906] - -mean value: 0.9806869519000797 - -key: test_accuracy -value: [0.79166667 0.91666667 0.9787234 0.82978723 0.89361702 1. - 0.85106383 0.89361702 0.87234043 0.76595745] - -mean value: 0.8793439716312056 - -key: train_accuracy -value: [0.96462264 0.96226415 0.96941176 0.96 0.97647059 0.97647059 - 0.96 0.97176471 0.95294118 0.96941176] - -mean value: 0.9663357380688125 - -key: test_roc_auc -value: [0.79166667 0.91666667 0.97916667 0.82971014 0.89583333 1. - 0.85054348 0.89221014 0.87318841 0.76177536] - -mean value: 0.8790760869565217 - -key: train_roc_auc -value: [0.96462264 0.96226415 0.96938391 0.95999424 0.97641509 0.97641509 - 0.96003853 0.97179777 0.95296306 0.96945035] - -mean value: 0.9663344848968022 - -key: test_jcc -value: [0.6875 0.84615385 0.95833333 0.7037037 0.82142857 1. - 0.75 0.82142857 0.76923077 0.67647059] - -mean value: 0.8034249383514089 - -key: train_jcc -value: [0.9321267 0.92857143 0.94144144 0.92342342 0.95515695 0.95515695 - 0.92410714 0.94570136 0.91071429 0.94144144] - -mean value: 0.9357841119093099 - -key: TN -value: 196 - -mean value: 196.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 40 - -mean value: 40.0 - -key: TP -value: 219 - -mean value: 219.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.07 - -Accuracy on Blind test: 0.68 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.59148049 0.55282736 0.5599308 0.55353999 0.55820084 0.54877162 - 0.55891895 0.55548716 0.56001878 0.56187654] - -mean value: 0.5601052522659302 - -key: score_time -value: [0.00914335 0.00937653 0.00928164 0.00906444 0.00925207 0.00907731 - 0.00926995 0.00922632 0.00914979 0.00905776] - -mean value: 0.009189915657043458 - -key: test_mcc -value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.91833182 - 1. 0.95825929 1. 0.95825929] - -mean value: 0.9625538447534631 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.95833333 - 1. 0.97959184 1. 0.97959184] - -mean value: 0.9811077391178488 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.92 - 1. 0.96 1. 0.96 ] - -mean value: 0.9673188405797102 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95652174 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9956521739130434 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -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/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -[0.97916667 1. 0.9787234 0.95744681 0.9787234 0.95744681 - 1. 0.9787234 1. 0.9787234 ] - -mean value: 0.980895390070922 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.95833333 - 1. 0.97826087 1. 0.97826087] - -mean value: 0.9809782608695652 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.92 - 1. 0.96 1. 0.96 ] - -mean value: 0.9633333333333333 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 228 - -mean value: 228.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 235 - -mean value: 235.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.95 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02428985 0.02749515 0.02805972 0.02740002 0.02796483 0.03774118 - 0.03575802 0.0446558 0.03393126 0.03918934] - -mean value: 0.03264851570129394 - -key: score_time -value: [0.01221967 0.01220965 0.01315665 0.01267314 0.01473403 0.01557922 - 0.01255774 0.01477599 0.01570821 0.0162406 ] - -mean value: 0.013985490798950196 - -key: test_mcc -value: [1. 0.83624201 1. 0.82971014 0.91804649 1. - 1. 0.91485507 1. 1. ] - -mean value: 0.9498853721073142 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.91304348 1. 0.91304348 0.95454545 1. - 1. 0.95833333 1. 1. ] - -mean value: 0.9738965744400527 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95454545 1. 0.91304348 1. 1. - 1. 0.95833333 1. 1. ] - -mean value: 0.9825922266139658 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.875 1. 0.91304348 0.91304348 1. - 1. 0.95833333 1. 1. ] - -mean value: 0.9659420289855072 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.91666667 1. 0.91489362 0.95744681 1. - 1. 0.95744681 1. 1. ] - -mean value: 0.974645390070922 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.91666667 1. 0.91485507 0.95652174 1. - 1. 0.95742754 1. 1. ] - -mean value: 0.9745471014492754 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.84 1. 0.84 0.91304348 1. - 1. 0.92 1. 1. ] - -mean value: 0.9513043478260869 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 233 - -mean value: 233.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 228 - -mean value: 228.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: -0.04 - -Accuracy on Blind test: 0.78 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01509428 0.01487875 0.01487017 0.03336596 0.03517532 0.03639007 - 0.03261375 0.03647685 0.03703356 0.03658605] - -mean value: 0.029248476028442383 - -key: score_time -value: [0.01185608 0.0118258 0.0119319 0.02261043 0.0208478 0.02019501 - 0.02254701 0.02088499 0.02249098 0.02036333] - -mean value: 0.018555331230163574 - -key: test_mcc -value: [0.9591663 0.9591663 0.95833333 0.83243502 0.91833182 0.95833333 - 0.87318841 0.91804649 1. 0.76896316] - -mean value: 0.9145964175646162 - -key: train_mcc -value: [0.97668677 0.97668677 0.98134942 0.97193552 0.98134942 0.9767396 - 0.96715612 0.98135106 0.97674215 0.97674215] - -mean value: 0.9766738992560577 - -key: test_fscore -value: [0.97959184 0.97959184 0.9787234 0.90909091 0.95833333 0.9787234 - 0.93617021 0.96 1. 0.88888889] - -mean value: 0.9569113826059116 - -key: train_fscore -value: [0.98834499 0.98834499 0.99069767 0.98604651 0.99069767 0.98839907 - 0.98360656 0.99065421 0.98834499 0.98834499] - -mean value: 0.9883481648755348 - -key: test_precision -value: [0.96 0.96 0.95833333 0.95238095 0.92 0.95833333 - 0.95652174 0.92307692 1. 0.8 ] - -mean value: 0.9388646281254978 - -key: train_precision -value: [0.97695853 0.97695853 0.98156682 0.97695853 0.98156682 0.97706422 - 0.97674419 0.98148148 0.97695853 0.97695853] - -mean value: 0.9783216154992586 - -key: test_recall -value: [1. 1. 1. 0.86956522 1. 1. - 0.91666667 1. 1. 1. ] - -mean value: 0.9786231884057971 - -key: train_recall -value: [1. 1. 1. 0.99530516 1. 1. - 0.99056604 1. 1. 1. ] - -mean value: 0.9985871202055098 - -key: test_accuracy -value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.9787234 - 0.93617021 0.95744681 1. 0.87234043] - -mean value: 0.9554078014184396 - -key: train_accuracy -value: [0.98820755 0.98820755 0.99058824 0.98588235 0.99058824 0.98823529 - 0.98352941 0.99058824 0.98823529 0.98823529] - -mean value: 0.9882297447280799 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.97916667 0.91394928 0.95833333 0.97916667 - 0.9365942 0.95652174 1. 0.86956522] - -mean value: 0.955163043478261 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.99056604 0.98586013 0.99056604 0.98820755 - 0.98354593 0.99061033 0.98826291 0.98826291] - -mean value: 0.9882296926211355 - -key: test_jcc -value: [0.96 0.96 0.95833333 0.83333333 0.92 0.95833333 - 0.88 0.92307692 1. 0.8 ] - -mean value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: 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 - rouC_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: 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 - rouC_CV['Resampling'] = rs_rouC -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: 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 - rouC_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: 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 - rouC_BT['Resampling'] = rs_rouC -0.9193076923076925 - -key: train_jcc -value: [0.97695853 0.97695853 0.98156682 0.97247706 0.98156682 0.97706422 - 0.96774194 0.98148148 0.97695853 0.97695853] - -mean value: 0.9769732443304507 - -key: TN -value: 220 - -mean value: 220.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.92 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.25742292 0.25813818 0.26374173 0.35750985 0.27137733 0.13077354 - 0.26217246 0.20676613 0.25405955 0.25714278] - -mean value: 0.2519104480743408 - -key: score_time -value: [0.02147627 0.02145267 0.02258754 0.02365971 0.0148344 0.012043 - 0.02242708 0.01192284 0.02006245 0.02252841] - -mean value: 0.01929943561553955 - -key: test_mcc -value: [0.9591663 0.9591663 0.95833333 0.83243502 0.91833182 0.95833333 - 0.87318841 0.91804649 1. 0.76896316] - -mean value: 0.9145964175646162 - -key: train_mcc -value: [0.97668677 0.97668677 0.98134942 0.97193552 0.98134942 0.9767396 - 0.96715612 0.98135106 0.97674215 0.97674215] - -mean value: 0.9766738992560577 - -key: test_fscore -value: [0.97959184 0.97959184 0.9787234 0.90909091 0.95833333 0.9787234 - 0.93617021 0.96 1. 0.88888889] - -mean value: 0.9569113826059116 - -key: train_fscore -value: [0.98834499 0.98834499 0.99069767 0.98604651 0.99069767 0.98839907 - 0.98360656 0.99065421 0.98834499 0.98834499] - -mean value: 0.9883481648755348 - -key: test_precision -value: [0.96 0.96 0.95833333 0.95238095 0.92 0.95833333 - 0.95652174 0.92307692 1. 0.8 ] - -mean value: 0.9388646281254978 - -key: train_precision -value: [0.97695853 0.97695853 0.98156682 0.97695853 0.98156682 0.97706422 - 0.97674419 0.98148148 0.97695853 0.97695853] - -mean value: 0.9783216154992586 - -key: test_recall -value: [1. 1. 1. 0.86956522 1. 1. - 0.91666667 1. 1. 1. ] - -mean value: 0.9786231884057971 - -key: train_recall -value: [1. 1. 1. 0.99530516 1. 1. - 0.99056604 1. 1. 1. ] - -mean value: 0.9985871202055098 - -key: test_accuracy -value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.9787234 - 0.93617021 0.95744681 1. 0.87234043] - -mean value: 0.9554078014184396 - -key: train_accuracy -value: [0.98820755 0.98820755 0.99058824 0.98588235 0.99058824 0.98823529 - 0.98352941 0.99058824 0.98823529 0.98823529] - -mean value: 0.9882297447280799 - -key: test_roc_auc -value: [0.97916667 0.97916667 0.97916667 0.91394928 0.95833333 0.97916667 - 0.9365942 0.95652174 1. 0.86956522] - -mean value: 0.955163043478261 - -key: train_roc_auc -value: [0.98820755 0.98820755 0.99056604 0.98586013 0.99056604 0.98820755 - 0.98354593 0.99061033 0.98826291 0.98826291] - -mean value: 0.9882296926211355 - -key: test_jcc -value: [0.96 0.96 0.95833333 0.83333333 0.92 0.95833333 - 0.88 0.92307692 1. 0.8 ] - -mean value: 0.9193076923076925 - -key: train_jcc -value: [0.97695853 0.97695853 0.98156682 0.97247706 0.98156682 0.97706422 - 0.96774194 0.98148148 0.97695853 0.97695853] - -mean value: 0.9769732443304507 - -key: TN -value: 220 - -mean value: 220.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 231 - -mean value: 231.0 - -key: trainingY_neg -value: 236 - -mean value: 236.0 - -key: trainingY_pos -value: 236 - -mean value: 236.0 - -key: blindY_neg -value: 117 - -mean value: 117.0 - -key: blindY_pos -value: 31 - -mean value: 31.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.92 - -PASS: sorting df by score that is mapped onto the order I want - -Concatenating dfs with different resampling methods [WF]: 70/30 -No. of dfs combining: 10 -The sampling methods are: - Resampling -Logistic Regression none -Logistic Regression smnc -Logistic Regression ros -Logistic Regression rus -Logistic Regression rouC - -PASS: 10 dfs successfully combined -nrows in combined_df_wf: 240 -ncols in combined_df_wf: 9 - -Concatenating dfs with different resampling methods: 70/30 -No. of dfs combining: 5 -The sampling methods are: - Resampling training_size -0 none 300 -24 smnc 472 -48 ros 472 -72 rus 128 -96 rouC 472 - -PASS: 5 dfs successfully combined -nrows in combined_df: 120 -ncols in combined_df: 32 - -File successfully written: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/embb_baselineC_7030.csv - -File successfully written: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/embb_baselineC_ext_7030.csv diff --git a/scripts/ml/log_gid_7030.txt b/scripts/ml/log_gid_7030.txt deleted file mode 100644 index 4939633..0000000 --- a/scripts/ml/log_gid_7030.txt +++ /dev/null @@ -1,24347 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -/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 - -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: 531 - -PASS: my_features_df and aa_df successfully combined -nrows: 531 -ncols: 286 -count of NULL values before imputation - -or_mychisq 263 -log10_or_mychisq 263 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 173 - -PASS: ML data with input features, training and test generated... - -Total no. of input features: 173 ---------No. of numerical features: 167 ---------No. of categorical features: 6 - -Total no. of evolutionary features: 3 - -Total no. of stability features: 28 ---------Common stabilty cols: 5 ---------Foldx cols: 23 - -Total no. of affinity features: 4 ---------Common affinity cols: 3 ---------Gene specific affinity cols: 1 - -Total no. of residue level features: 132 ---------AA index cols: 123 ---------Residue Prop cols: 3 ---------AA change Prop cols: 6 - -Total no. of genomic features: 6 ---------MAF+OR cols: 2 ---------Lineage cols: 4 ---------Other cols: 0 - -------------------------------------------------------------- -Successfully split data: ALL features -actual values: training set -imputed values: blind test set - -Total data size: 119 - -Train data size: (79, 173) -y_train numbers: Counter({0: 50, 1: 29}) - -Test data size: (40, 173) -y_test_numbers: Counter({0: 26, 1: 14}) - -y_train ratio: 1.7241379310344827 -y_test ratio: 1.8571428571428572 -------------------------------------------------------------- - -index: 0 -ind: 1 - -Mask count check: True - -index: 1 -ind: 2 - -Mask count check: True -Original Data - Counter({0: 50, 1: 29}) Data dim: (79, 173) -Simple Random OverSampling - Counter({1: 50, 0: 50}) -(100, 173) -Simple Random UnderSampling - Counter({0: 29, 1: 29}) -(58, 173) -Simple Combined Over and UnderSampling - Counter({0: 50, 1: 50}) -(100, 173) - -SMOTE_NC OverSampling - Counter({1: 50, 0: 50}) -(100, 173) - -##################################################################### - -Running ML analysis: feature groups -Gene name: gid -Drug name: streptomycin - -Output directory: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/ - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/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( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -key: fit_time -value: [0.0242238 0.03297615 0.02694082 0.02446318 0.02704215 0.04720712 - 0.03565717 0.02282596 0.04037237 0.05070901] - -mean value: 0.033241772651672365 - -key: score_time -value: [0.0121789 0.01169276 0.01170754 0.011518 0.01153922 0.0120914 - 0.01173162 0.01155663 0.01343775 0.0119257 ] - -mean value: 0.011937952041625977 - -key: test_mcc -value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.25819889 - 0.48795004 -0.29277002 0.48795004 0.3 ] - -mean value: 0.35987075924776607 - -key: train_mcc -value: [0.94010481 0.91067388 0.94010481 0.94010481 0.90865445 0.96986363 - 0.91067388 0.90865445 0.96986363 0.97058178] - -mean value: 0.9369280134082192 - -key: test_fscore -value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.57142857 - 0.5 0. 0.5 0.5 ] - -mean value: 0.5338095238095238 - -key: train_fscore -value: [0.96 0.93877551 0.96 0.96 0.94117647 0.98039216 - 0.93877551 0.94117647 0.98039216 0.98113208] - -mean value: 0.9581820350781823 - -key: test_precision -value: [1. 0.66666667 1. 0.33333333 0.66666667 0.5 - 1. 0. 1. 0.5 ] - -mean value: 0.6666666666666666 - -key: train_precision -value: [1. 1. 1. 1. 0.96 1. 1. 0.96 1. 1. ] - -mean value: 0.992 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 0.66666667 - 0.33333333 0. 0.33333333 0.5 ] - -mean value: 0.4833333333333333 - -key: train_recall -value: [0.92307692 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 - 0.88461538 0.92307692 0.96153846 0.96296296] - -mean value: 0.9270655270655273 - -key: test_accuracy -value: [0.875 0.75 0.875 0.5 0.75 0.625 - 0.75 0.5 0.75 0.71428571] - -mean value: 0.7089285714285715 - -key: train_accuracy -value: [0.97183099 0.95774648 0.97183099 0.97183099 0.95774648 0.98591549 - 0.95774648 0.95774648 0.98591549 0.98611111] - -mean value: 0.9704420970266041 - -key: test_roc_auc -value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.63333333 - 0.66666667 0.4 0.66666667 0.65 ] - -mean value: 0.6616666666666667 - -key: train_roc_auc -value: [0.96153846 0.94230769 0.96153846 0.96153846 0.95042735 0.98076923 - 0.94230769 0.95042735 0.98076923 0.98148148] - -mean value: 0.9613105413105412 - -key: test_jcc -value: [0.66666667 0.5 0.66666667 0.2 0.5 0.4 - 0.33333333 0. 0.33333333 0.33333333] - -mean value: 0.39333333333333337 - -key: train_jcc -value: [0.92307692 0.88461538 0.92307692 0.92307692 0.88888889 0.96153846 - 0.88461538 0.88888889 0.96153846 0.96296296] - -mean value: 0.9202279202279204 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 14 - -mean value: 14.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.72 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.92556214 0.62680626 0.74799728 0.69733357 0.53420568 0.79944897 - 0.80795002 0.70474362 0.61180806 0.56007266] - -mean value: 0.7015928268432617 - -key: score_time -value: [0.01263237 0.01342344 0.01227355 0.01217771 0.01460266 0.01221347 - 0.01453018 0.01216531 0.01223826 0.01225662] - -mean value: 0.012851357460021973 - -key: test_mcc -value: [ 0. 0.46666667 0.48795004 -0.06666667 0.46666667 0.6 - 1. 0. 0.1490712 0. ] - -mean value: 0.3103687901640919 - -key: train_mcc -value: [0. 1. 0.79523358 0.72919664 1. 1. - 1. 0.72919664 0.96986363 0. ] - -mean value: 0.7223490489333553 - -key: test_fscore -value: [0. 0.66666667 0.5 0.33333333 0.66666667 0.75 - 1. 0. 0.4 0. ] - -mean value: 0.43166666666666664 - -key: train_fscore -value: [0. 1. 0.84444444 0.8 1. 1. - 1. 0.8 0.98039216 0. ] - -mean value: 0.742483660130719 - -key: test_precision -value: [0. 0.66666667 1. 0.33333333 0.66666667 0.6 - 1. 0. 0.5 0. ] - -mean value: 0.4766666666666667 - -key: train_precision -value: [0. 1. 1. 0.94736842 1. 1. - 1. 0.94736842 1. 0. ] - -mean value: 0.7894736842105263 - -key: test_recall -value: [0. 0.66666667 0.33333333 0.33333333 0.66666667 1. - 1. 0. 0.33333333 0. ] - -mean value: 0.4333333333333333 - -key: train_recall -value: [0. 1. 0.73076923 0.69230769 1. 1. - 1. 0.69230769 0.96153846 0. ] - -mean value: 0.7076923076923076 - -key: test_accuracy -value: [0.625 0.75 0.75 0.5 0.75 0.75 - 1. 0.625 0.625 0.71428571] - -mean value: 0.7089285714285715 - -key: train_accuracy -value: [0.63380282 1. 0.90140845 0.87323944 1. 1. - 1. 0.87323944 0.98591549 0.625 ] - -mean value: 0.8892605633802816 - -key: test_roc_auc -value: [0.5 0.73333333 0.66666667 0.46666667 0.73333333 0.8 - 1. 0.5 0.56666667 0.5 ] - -mean value: 0.6466666666666667 - -key: train_roc_auc -value: [0.5 1. 0.86538462 0.83504274 1. 1. - 1. 0.83504274 0.98076923 0.5 ] - -mean value: 0.8516239316239316 - -key: test_jcc -value: [0. 0.5 0.33333333 0.2 0.5 0.6 - 1. 0. 0.25 0. ] - -mean value: 0.3383333333333333 - -key: train_jcc -value: [0. 1. 0.73076923 0.66666667 1. 1. - 1. 0.66666667 0.96153846 0. ] - -mean value: 0.7025641025641025 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 13 - -mean value: 13.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.39 - -Accuracy on Blind test: 0.72 - -Running classifier: 3 -Model_name: Gaussian NB -Model func: GaussianNB() - -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)) -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01347423 0.01884365 0.00855184 0.00828695 0.00806451 0.00827241 - 0.00818229 0.00826859 0.00821042 0.00816202] - -mean value: 0.009831690788269043 - -key: score_time -value: [0.01358652 0.00921679 0.00873518 0.00868773 0.00849938 0.00838518 - 0.00836921 0.00838017 0.00841665 0.00839853] - -mean value: 0.009067535400390625 - -key: test_mcc -value: [-0.1490712 0. -0.1490712 -0.46666667 0. 0.6 - 0.06666667 -0.06666667 -0.46666667 0.25819889] - -mean value: -0.03732768405861442 - -key: train_mcc -value: [0.53350008 0.46369578 0.44297451 0.61021596 0.39343135 0.5954372 - 0.40122444 0.55355298 0.55355298 0.54074074] - -mean value: 0.5088326024907828 - -key: test_fscore -value: [0.44444444 0.54545455 0.44444444 0.25 0.54545455 0.75 - 0.5 0.33333333 0.25 0.5 ] - -mean value: 0.45631313131313134 - -key: train_fscore -value: [0.71428571 0.67567568 0.65822785 0.75757576 0.64102564 0.74285714 - 0.64864865 0.72463768 0.72463768 0.72222222] - -mean value: 0.7009794012710908 - -key: test_precision -value: [0.33333333 0.375 0.33333333 0.2 0.375 0.6 - 0.4 0.33333333 0.2 0.33333333] - -mean value: 0.3483333333333334 - -key: train_precision -value: [0.66666667 0.52083333 0.49056604 0.625 0.48076923 0.59090909 - 0.5 0.58139535 0.58139535 0.57777778] - -mean value: 0.5615312834866366 - -key: test_recall -value: [0.66666667 1. 0.66666667 0.33333333 1. 1. - 0.66666667 0.33333333 0.33333333 1. ] - -mean value: 0.7 - -key: train_recall -value: [0.76923077 0.96153846 1. 0.96153846 0.96153846 1. - 0.92307692 0.96153846 0.96153846 0.96296296] - -mean value: 0.9462962962962964 - -key: test_accuracy -value: [0.375 0.375 0.375 0.25 0.375 0.75 - 0.5 0.5 0.25 0.42857143] - -mean value: 0.41785714285714287 - -key: train_accuracy -value: [0.77464789 0.66197183 0.61971831 0.77464789 0.6056338 0.74647887 - 0.63380282 0.73239437 0.73239437 0.72222222] - -mean value: 0.7003912363067293 - -key: test_roc_auc -value: [0.43333333 0.5 0.43333333 0.26666667 0.5 0.8 - 0.53333333 0.46666667 0.26666667 0.6 ] - -mean value: 0.47999999999999987 - -key: train_roc_auc -value: [0.77350427 0.72521368 0.7 0.81410256 0.68076923 0.8 - 0.69487179 0.78076923 0.78076923 0.77037037] - -mean value: 0.7520370370370371 - -key: test_jcc -value: [0.28571429 0.375 0.28571429 0.14285714 0.375 0.6 - 0.33333333 0.2 0.14285714 0.33333333] - -mean value: 0.30738095238095237 - -key: train_jcc -value: [0.55555556 0.51020408 0.49056604 0.6097561 0.47169811 0.59090909 - 0.48 0.56818182 0.56818182 0.56521739] - -mean value: 0.5410270004269655 - -key: TN -value: 13 - -mean value: 13.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 37 - -mean value: 37.0 - -key: TP -value: 20 - -mean value: 20.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.27 - -Accuracy on Blind test: 0.52 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.0091784 0.00914526 0.00893593 0.00925088 0.00869727 0.00930667 - 0.00948238 0.00864649 0.00869966 0.00828242] - -mean value: 0.008962535858154297 - -key: score_time -value: [0.00917268 0.00923753 0.00889874 0.00905013 0.00859499 0.00902128 - 0.00948811 0.00878263 0.00849462 0.00845385] - -mean value: 0.008919453620910645 - -key: test_mcc -value: [-0.29277002 -0.06666667 0.48795004 0. 0.46666667 -0.06666667 - -0.29277002 -0.29277002 -0.29277002 -0.25819889] - -mean value: -0.060799560747780104 - -key: train_mcc -value: [0.40170738 0.51530373 0.43729399 0.35928426 0.40170738 0.50503962 - 0.48250404 0.50503962 0.39606306 0.4233902 ] - -mean value: 0.44273332643919316 - -key: test_fscore -value: [0. 0.33333333 0.5 0. 0.66666667 0.33333333 - 0. 0. 0. 0. ] - -mean value: 0.18333333333333332 - -key: train_fscore -value: [0.48648649 0.57894737 0.52631579 0.47368421 0.48648649 0.6 - 0.54054054 0.6 0.51282051 0.51282051] - -mean value: 0.5318101907575592 - -key: test_precision -value: [0. 0.33333333 1. 0. 0.66666667 0.33333333 - 0. 0. 0. 0. ] - -mean value: 0.2333333333333333 - -key: train_precision -value: [0.81818182 0.91666667 0.83333333 0.75 0.81818182 0.85714286 - 0.90909091 0.85714286 0.76923077 0.83333333] - -mean value: 0.8362304362304362 - -key: test_recall -value: [0. 0.33333333 0.33333333 0. 0.66666667 0.33333333 - 0. 0. 0. 0. ] - -mean value: 0.16666666666666666 - -key: train_recall -value: [0.34615385 0.42307692 0.38461538 0.34615385 0.34615385 0.46153846 - 0.38461538 0.46153846 0.38461538 0.37037037] - -mean value: 0.3908831908831909 - -key: test_accuracy -value: [0.5 0.5 0.75 0.625 0.75 0.5 - 0.5 0.5 0.5 0.57142857] - -mean value: 0.5696428571428571 - -key: train_accuracy -value: [0.73239437 0.77464789 0.74647887 0.71830986 0.73239437 0.77464789 - 0.76056338 0.77464789 0.73239437 0.73611111] - -mean value: 0.7482589984350548 - -key: test_roc_auc -value: [0.4 0.46666667 0.66666667 0.5 0.73333333 0.46666667 - 0.4 0.4 0.4 0.4 ] - -mean value: 0.4833333333333334 - -key: train_roc_auc -value: [0.6508547 0.70042735 0.67008547 0.63974359 0.6508547 0.70854701 - 0.68119658 0.70854701 0.65897436 0.66296296] - -mean value: 0.6732193732193733 - -key: test_jcc -value: [0. 0.2 0.33333333 0. 0.5 0.2 - 0. 0. 0. 0. ] - -mean value: 0.12333333333333334 - -key: train_jcc -value: [0.32142857 0.40740741 0.35714286 0.31034483 0.32142857 0.42857143 - 0.37037037 0.42857143 0.34482759 0.34482759] - -mean value: 0.36349206349206353 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 5 - -mean value: 5.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.16 - -Accuracy on Blind test: 0.55 - -Running classifier: 5 /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)) -/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)) -/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)) - -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00837946 0.01102352 0.00812745 0.00867653 0.00784159 0.00877905 - 0.00860858 0.00786567 0.0082624 0.00899577] - -mean value: 0.008656001091003418 - -key: score_time -value: [0.04542685 0.03425503 0.00936675 0.00973725 0.00988531 0.00930572 - 0.01529074 0.01369047 0.01387525 0.01388526] - -mean value: 0.017471861839294434 - -key: test_mcc -value: [ 0.48795004 -0.06666667 0.25819889 0.1490712 0.46666667 0.25819889 - 0.48795004 0. -0.06666667 1. ] - -mean value: 0.2974702384276175 - -key: train_mcc -value: [0.532629 0.49965897 0.332975 0.49787306 0.46412056 0.4660252 - 0.46504888 0.43897987 0.53764379 0.48034053] - -mean value: 0.4715294855036312 - -key: test_fscore -value: [0.5 0.33333333 0.57142857 0.4 0.66666667 0.57142857 - 0.5 0. 0.33333333 1. ] - -mean value: 0.4876190476190477 - -key: train_fscore -value: [0.65116279 0.61904762 0.53333333 0.63636364 0.60465116 0.58536585 - 0.62222222 0.625 0.63414634 0.62222222] - -mean value: 0.6133515181799357 - -key: test_precision -value: [1. 0.33333333 0.5 0.5 0.66666667 0.5 - 1. 0. 0.33333333 1. ] - -mean value: 0.5833333333333333 - -key: train_precision -value: [0.82352941 0.8125 0.63157895 0.77777778 0.76470588 0.8 - 0.73684211 0.68181818 0.86666667 0.77777778] - -mean value: 0.7673196750789629 - -key: test_recall -value: [0.33333333 0.33333333 0.66666667 0.33333333 0.66666667 0.66666667 - 0.33333333 0. 0.33333333 1. ] - -mean value: 0.4666666666666666 - -key: train_recall -value: [0.53846154 0.5 0.46153846 0.53846154 0.5 0.46153846 - 0.53846154 0.57692308 0.5 0.51851852] - -mean value: 0.5133903133903133 - -key: test_accuracy -value: [0.75 0.5 0.625 0.625 0.75 0.625 0.75 0.625 0.5 1. ] - -mean value: 0.675 - -key: train_accuracy -value: [0.78873239 0.77464789 0.70422535 0.77464789 0.76056338 0.76056338 - 0.76056338 0.74647887 0.78873239 0.76388889] - -mean value: 0.7623043818466353 - -key: test_roc_auc -value: [0.66666667 0.46666667 0.63333333 0.56666667 0.73333333 0.63333333 - 0.66666667 0.5 0.46666667 1. ] - -mean value: 0.6333333333333333 - -key: train_roc_auc -value: [0.73589744 0.71666667 0.65299145 0.72478632 0.70555556 0.6974359 - 0.71367521 0.71068376 0.72777778 0.71481481] - -mean value: 0.7100284900284901 - -key: test_jcc -value: [0.33333333 0.2 0.4 0.25 0.5 0.4 - 0.33333333 0. 0.2 1. ] - -mean value: 0.3616666666666667 - -key: train_jcc -value: [0.48275862 0.44827586 0.36363636 0.46666667 0.43333333 0.4137931 - 0.4516129 0.45454545 0.46428571 0.4516129 ] - -mean value: 0.4430520925126042 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 13 - -mean value: 13.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.16 - -Accuracy on Blind test: 0.55 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00934434 0.00849128 0.00862622 0.00854397 0.00879931 0.00910807 - 0.00843096 0.00899863 0.00908327 0.00870323] - -mean value: 0.008812928199768066 - -key: score_time -value: [0.00875854 0.00884771 0.00850749 0.00841236 0.00861263 0.00835824 - 0.0086937 0.00896144 0.00847411 0.00863171] - -mean value: 0.00862579345703125 - -key: test_mcc -value: [ 0. -0.4472136 0. 0. 0. 0.48795004 - 0. 0. 0. 0. ] - -mean value: 0.004073644097430868 - -key: train_mcc -value: [0.46880723 0.53266562 0.46880723 0.53266562 0.43508951 0.50123916 - 0.50123916 0.56330071 0.46880723 0.51847585] - -mean value: 0.49910973205557047 - -key: test_fscore -value: [0. 0. 0. 0. 0. 0.5 0. 0. 0. 0. ] - -mean value: 0.05 - -key: train_fscore -value: [0.47058824 0.55555556 0.47058824 0.55555556 0.42424242 0.51428571 - 0.51428571 0.59459459 0.47058824 0.54054054] - -mean value: 0.5110824804942451 - -key: test_precision -value: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] - -mean value: 0.1 - -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. 0. 0.33333333 - 0. 0. 0. 0. ] - -mean value: 0.03333333333333333 - -key: train_recall -value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385 - 0.34615385 0.42307692 0.30769231 0.37037037] - -mean value: 0.3447293447293447 - -key: test_accuracy -value: [0.625 0.375 0.625 0.625 0.625 0.75 - 0.625 0.625 0.625 0.71428571] - -mean value: 0.6214285714285714 - -key: train_accuracy -value: [0.74647887 0.77464789 0.74647887 0.77464789 0.73239437 0.76056338 - 0.76056338 0.78873239 0.74647887 0.76388889] - -mean value: 0.7594874804381847 - -key: test_roc_auc -value: [0.5 0.3 0.5 0.5 0.5 0.66666667 - 0.5 0.5 0.5 0.5 ] - -mean value: 0.4966666666666667 - -key: train_roc_auc -value: [0.65384615 0.69230769 0.65384615 0.69230769 0.63461538 0.67307692 - 0.67307692 0.71153846 0.65384615 0.68518519] - -mean value: 0.6723646723646725 - -key: test_jcc -value: [0. 0. 0. 0. 0. 0.33333333 - 0. 0. 0. 0. ] - -mean value: 0.03333333333333333 - -key: train_jcc -value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385 - 0.34615385 0.42307692 0.30769231 0.37037037] - -mean value: 0.3447293447293447 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 2 - -mean value: 2.0 - -key: TP -value: 1 - -mean value: 1.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.68 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.4739027 0.3656857 0.50512409 0.53201818 0.40765285 0.37933254 - 0.71146369 1.25982904 0.67421913 0.39366579] - -mean value: 0.5702893733978271 - -key: score_time -value: [0.01205778 0.01223612 0.01199913 0.01212883 0.01202846 0.01200676 - 0.012532 0.01227117 0.01231146 0.01202655] - -mean value: 0.01215982437133789 - -key: test_mcc -value: [ 0.74535599 0.46666667 0.74535599 0.06666667 0.46666667 0.6 - 0.1490712 -0.29277002 -0.06666667 0.3 ] - -mean value: 0.3180346494948619 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.66666667 0.8 0.5 0.66666667 0.75 - 0.4 0. 0.33333333 0.5 ] - -mean value: 0.5416666666666666 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.66666667 1. 0.4 0.66666667 0.6 - 0.5 0. 0.33333333 0.5 ] - -mean value: 0.5666666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 1. - 0.33333333 0. 0.33333333 0.5 ] - -mean value: 0.5499999999999999 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.75 0.875 0.5 0.75 0.75 - 0.625 0.5 0.5 0.71428571] - -mean value: 0.6839285714285714 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 0.73333333 0.83333333 0.53333333 0.73333333 0.8 - 0.56666667 0.4 0.46666667 0.65 ] - -mean value: 0.655 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.5 0.66666667 0.33333333 0.5 0.6 - 0.25 0. 0.2 0.33333333] - -mean value: 0.40499999999999997 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 16 - -mean value: 16.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.16 - -Accuracy on Blind test: 0.62 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01303053 0.01276302 0.00972843 0.00934529 0.00922871 0.00918818 - 0.00910521 0.0092113 0.00886583 0.00934553] - -mean value: 0.009981203079223632 - -key: score_time -value: [0.01174879 0.01063251 0.00858688 0.00828457 0.00867963 0.00826287 - 0.00824618 0.00817752 0.00813031 0.00817704] - -mean value: 0.008892631530761719 - -key: test_mcc -value: [0.74535599 0.74535599 0.77459667 0.46666667 1. 0.6 - 0.46666667 1. 0.74535599 0.54772256] - -mean value: 0.7091720537579772 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.85714286 0.66666667 1. 0.75 - 0.66666667 1. 0.8 0.66666667] - -mean value: 0.8007142857142856 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.75 0.66666667 1. 0.6 - 0.66666667 1. 1. 0.5 ] - -mean value: 0.8183333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 1. 0.66666667 1. 1. - 0.66666667 1. 0.66666667 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.875 0.875 0.75 1. 0.75 - 0.75 1. 0.875 0.71428571] - -mean value: 0.8464285714285713 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.9 0.73333333 1. 0.8 - 0.73333333 1. 0.83333333 0.8 ] - -mean value: 0.8466666666666667 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.66666667 0.75 0.5 1. 0.6 - 0.5 1. 0.66666667 0.5 ] - -mean value: 0.685 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.0797112 0.08011031 0.0798316 0.07908225 0.07965994 0.07991409 - 0.08132887 0.07901454 0.07934308 0.07978773] - -mean value: 0.07977836132049561 - -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)) -[0.01661205 0.01663494 0.01658773 0.01665282 0.01672363 0.01666379 - 0.01657057 0.01656389 0.01663399 0.0166142 ] - -mean value: 0.01662576198577881 - -key: test_mcc -value: [ 0.48795004 -0.06666667 0.74535599 -0.06666667 0.1490712 0.25819889 - 0.1490712 -0.29277002 0.1490712 0.73029674] - -mean value: 0.22429119023436436 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.33333333 0.8 0.33333333 0.4 0.57142857 - 0.4 0. 0.4 0.8 ] - -mean value: 0.4538095238095238 - -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 1. 0.33333333 0.5 0.5 - 0.5 0. 0.5 0.66666667] - -mean value: 0.5333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.33333333 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667 - 0.33333333 0. 0.33333333 1. ] - -mean value: 0.4333333333333334 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.5 0.875 0.5 0.625 0.625 - 0.625 0.5 0.625 0.85714286] - -mean value: 0.6482142857142856 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.66666667 0.46666667 0.83333333 0.46666667 0.56666667 0.63333333 - 0.56666667 0.4 0.56666667 0.9 ] - -mean value: 0.6066666666666667 - -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 0.2 0.66666667 0.2 0.25 0.4 - 0.25 0. 0.25 0.66666667] - -mean value: 0.32166666666666666 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 12 - -mean value: 12.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.28 - -Accuracy on Blind test: 0.7 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00881672 0.00906944 0.00841808 0.00802994 0.00884914 0.00797772 - 0.0080471 0.00805855 0.00790024 0.00817227] - -mean value: 0.008333921432495117 - -key: score_time -value: [0.00838375 0.0091083 0.0088253 0.00814772 0.00830793 0.008255 - 0.00831819 0.00809455 0.00816226 0.00925803] - -mean value: 0.008486104011535645 - -key: test_mcc -value: [ 0. -0.06666667 0.74535599 -0.6 0.48795004 0.4472136 - 0.1490712 -0.06666667 0.25819889 -0.09128709] - -mean value: 0.12631692864704402 - -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 0.8 0. 0.5 0.66666667 - 0.4 0.33333333 0.57142857 0.33333333] - -mean value: 0.39380952380952383 - -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 1. 0. 1. 0.5 - 0.5 0.33333333 0.5 0.25 ] - -mean value: 0.4416666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0. 0.33333333 0.66666667 0. 0.33333333 1. - 0.33333333 0.33333333 0.66666667 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: [0.625 0.5 0.875 0.25 0.75 0.625 - 0.625 0.5 0.625 0.42857143] - -mean value: 0.5803571428571429 - -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 0.46666667 0.83333333 0.2 0.66666667 0.7 - 0.56666667 0.46666667 0.63333333 0.45 ] - -mean value: 0.5483333333333333 - -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 0.66666667 0. 0.33333333 0.5 - 0.25 0.2 0.4 0.2 ] - -mean value: 0.275 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 12 - -mean value: 12.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.08 - -Accuracy on Blind test: 0.57 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [0.98950601 0.99312663 0.9990921 1.0047307 0.99263144 0.98464394 - 0.99300337 0.98910856 0.98675776 0.99708605] - -mean value: 0.9929686546325683 - -key: score_time -value: [0.08685112 0.08616352 0.0863297 0.09210467 0.08822775 0.08625507 - 0.08599234 0.08730912 0.0862937 0.08622384] - -mean value: 0.08717508316040039 - -key: test_mcc -value: [0.74535599 0.46666667 0.74535599 0.25819889 0.48795004 0.74535599 - 0.74535599 0. 0.1490712 0.73029674] - -mean value: 0.5073607504728022 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.66666667 0.8 0.57142857 0.5 0.8 - 0.8 0. 0.4 0.8 ] - -mean value: 0.6138095238095238 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.66666667 1. 0.5 1. 1. - 1. 0. 0.5 0.66666667] - -mean value: 0.7333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 - 0.66666667 0. 0.33333333 1. ] - -mean value: 0.5666666666666667 - -key: train_recall -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/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/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( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/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 -[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.75 0.875 0.625 0.75 0.875 - 0.875 0.625 0.625 0.85714286] - -mean value: 0.7732142857142856 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 0.73333333 0.83333333 0.63333333 0.66666667 0.83333333 - 0.83333333 0.5 0.56666667 0.9 ] - -mean value: 0.7333333333333333 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.5 0.66666667 0.4 0.33333333 0.66666667 - 0.66666667 0. 0.25 0.66666667] - -mean value: 0.48166666666666674 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 16 - -mean value: 16.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.8 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.82440186 0.80704331 0.8464756 0.91650128 0.80716181 0.7972815 - 0.81476235 0.90086365 0.79608941 0.80462122] - -mean value: 0.8315201997756958 - -key: score_time -value: [0.19199228 0.18694234 0.19604993 0.18045735 0.1642139 0.16165352 - 0.17684007 0.18403292 0.14751863 0.19382191] - -mean value: 0.1783522844314575 - -key: test_mcc -value: [0.74535599 0.48795004 0.46666667 0.48795004 0.48795004 0.74535599 - 0.74535599 0. 0. 0.73029674] - -mean value: 0.48968814969294777 - -key: train_mcc -value: [0.88152145 0.91067388 0.94010481 0.94010481 0.94010481 0.96986363 - 0.91067388 0.90865445 0.94010481 0.94155447] - -mean value: 0.9283361007134785 - -key: test_fscore -value: [0.8 0.5 0.66666667 0.5 0.5 0.8 - 0.8 0. 0. 0.8 ] - -mean value: 0.5366666666666666 - -key: train_fscore -value: [0.91666667 0.93877551 0.96 0.96 0.96 0.98039216 - 0.93877551 0.94117647 0.96 0.96153846] - -mean value: 0.9517324776064273 - -key: test_precision -value: [1. 1. 0.66666667 1. 1. 1. - 1. 0. 0. 0.66666667] - -mean value: 0.7333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 0.96 1. 1. ] - -mean value: 0.9960000000000001 - -key: test_recall -value: [0.66666667 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667 - 0.66666667 0. 0. 1. ] - -mean value: 0.4666666666666666 - -key: train_recall -value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 - 0.88461538 0.92307692 0.92307692 0.92592593] - -mean value: 0.9118233618233619 - -key: test_accuracy -value: [0.875 0.75 0.75 0.75 0.75 0.875 - 0.875 0.625 0.625 0.85714286] - -mean value: 0.7732142857142856 - -key: train_accuracy -value: [0.94366197 0.95774648 0.97183099 0.97183099 0.97183099 0.98591549 - 0.95774648 0.95774648 0.97183099 0.97222222] - -mean value: 0.9662363067292643 - -key: test_roc_auc -value: [0.83333333 0.66666667 0.73333333 0.66666667 0.66666667 0.83333333 - 0.83333333 0.5 0.5 0.9 ] - -mean value: 0.7133333333333333 - -key: train_roc_auc -value: [0.92307692 0.94230769 0.96153846 0.96153846 0.96153846 0.98076923 - 0.94230769 0.95042735 0.96153846 0.96296296] - -mean value: 0.9548005698005699 - -key: test_jcc -value: [0.66666667 0.33333333 0.5 0.33333333 0.33333333 0.66666667 - 0.66666667 0. 0. 0.66666667] - -mean value: 0.4166666666666667 - -key: train_jcc -value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 - 0.88461538 0.88888889 0.92307692 0.92592593] - -mean value: 0.9084045584045584 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 2 - -mean value: 2.0 - -key: TP -value: 13 - -mean value: 13.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.63 - -Accuracy on Blind test: 0.82 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.25267649 0.03277755 0.03268933 0.0326879 0.03371954 0.0320909 - 0.03316617 0.03263474 0.03360939 0.03158092] - -mean value: 0.054763293266296385 - -key: score_time -value: [0.01154733 0.01026154 0.01063204 0.01008058 0.010144 0.00997472 - 0.01005268 0.01010799 0.01003933 0.01033354] - -mean value: 0.010317373275756835 - -key: test_mcc -value: [1. 0.74535599 0.46666667 1. 1. 0.6 - 1. 1. 0.74535599 0.73029674] - -mean value: 0.8287675395006747 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] -/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: 1.0 - -key: test_fscore -value: [1. 0.8 0.66666667 1. 1. 0.75 - 1. 1. 0.8 0.8 ] - -mean value: 0.8816666666666668 - -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 1. 1. 0.6 - 1. 1. 1. 0.66666667] - -mean value: 0.8933333333333332 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.66666667 0.66666667 1. 1. 1. - 1. 1. 0.66666667 1. ] - -mean value: 0.9 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.875 0.75 1. 1. 0.75 - 1. 1. 0.875 0.85714286] - -mean value: 0.9107142857142858 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.83333333 0.73333333 1. 1. 0.8 - 1. 1. 0.83333333 0.9 ] - -mean value: 0.9099999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.66666667 0.5 1. 1. 0.6 - 1. 1. 0.66666667 0.66666667] - -mean value: 0.8099999999999999 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 46 - -mean value: 46.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 26 - -mean value: 26.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02152681 0.03794527 0.03800631 0.03840089 0.03812551 0.03813028 - 0.038131 0.03790116 0.04037714 0.04405189] - -mean value: 0.03725962638854981 - -key: score_time -value: [0.02359128 0.01147771 0.02012229 0.02056122 0.01671767 0.02125883 - 0.02173471 0.02235389 0.02048898 0.01903772] - -mean value: 0.01973443031311035 - -key: test_mcc -value: [ 0.46666667 0.46666667 1. -0.46666667 0.46666667 0.46666667 - 0.74535599 0.25819889 0.06666667 -0.09128709] - -mean value: 0.33789344559962303 - -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. 0.25 0.66666667 0.66666667 - 0.8 0.57142857 0.5 0.33333333] - -mean value: 0.6121428571428571 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.66666667 1. 0.2 0.66666667 0.66666667 - 1. 0.5 0.4 0.25 ] - -mean value: 0.6016666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 1. 0.33333333 0.66666667 0.66666667 - 0.66666667 0.66666667 0.66666667 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: [0.75 0.75 1. 0.25 0.75 0.75 - 0.875 0.625 0.5 0.42857143] - -mean value: 0.6678571428571429 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.73333333 0.73333333 1. 0.26666667 0.73333333 0.73333333 - 0.83333333 0.63333333 0.53333333 0.45 ] - -mean value: 0.6649999999999999 - -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. 0.14285714 0.5 0.5 - 0.66666667 0.4 0.33333333 0.2 ] - -mean value: 0.4742857142857142 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 19 - -mean value: 19.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.04 - -Accuracy on Blind test: 0.52 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01962996 0.00853753 0.00832033 0.00821114 0.00803685 0.00808215 - 0.00803947 0.00806713 0.00811481 0.00799322] - -mean value: 0.00930325984954834 - -key: score_time -value: [0.00881076 0.00864339 0.00856471 0.00838065 0.00816441 0.00830078 - 0.00829577 0.00821209 0.00824976 0.00826263] - -mean value: 0.008388495445251465 - -key: test_mcc -value: [-0.06666667 -0.06666667 0.1490712 -0.06666667 0.48795004 0.48795004 - 0. -0.29277002 0.1490712 -0.4 ] - -mean value: 0.038127244806394525 - -key: train_mcc -value: [0.35808137 0.39561212 0.39440661 0.39440661 0.43729399 0.36890287 - 0.39561212 0.39901194 0.39901194 0.41403934] - -mean value: 0.3956378889330924 - -key: test_fscore -value: [0.33333333 0.33333333 0.4 0.33333333 0.5 0.5 - 0. 0. 0.4 0. ] - -mean value: 0.27999999999999997 - -key: train_fscore -value: [0.5 0.55813953 0.53658537 0.53658537 0.52631579 0.56521739 - 0.55813953 0.57777778 0.57777778 0.55813953] - -mean value: 0.5494678072692067 - -key: test_precision -value: [0.33333333 0.33333333 0.5 0.33333333 1. 1. - 0. 0. 0.5 0. ] - -mean value: 0.4 - -key: train_precision -value: [0.71428571 0.70588235 0.73333333 0.73333333 0.83333333 0.65 - 0.70588235 0.68421053 0.68421053 0.75 ] - -mean value: 0.7194471472799646 - -key: test_recall -value: [0.33333333 0.33333333 0.33333333 0.33333333 0.33333333 0.33333333 - 0. 0. 0.33333333 0. ] - -mean value: 0.23333333333333334 - -key: train_recall -value: [0.38461538 0.46153846 0.42307692 0.42307692 0.38461538 0.5 - 0.46153846 0.5 0.5 0.44444444] - -mean value: 0.4482905982905983 - -key: test_accuracy -value: [0.5 0.5 0.625 0.5 0.75 0.75 - 0.625 0.5 0.625 0.42857143] -/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)) - -mean value: 0.5803571428571429 - -key: train_accuracy -value: [0.71830986 0.73239437 0.73239437 0.73239437 0.74647887 0.71830986 - 0.73239437 0.73239437 0.73239437 0.73611111] - -mean value: 0.7313575899843506 - -key: test_roc_auc -value: [0.46666667 0.46666667 0.56666667 0.46666667 0.66666667 0.66666667 - 0.5 0.4 0.56666667 0.3 ] - -mean value: 0.5066666666666666 - -key: train_roc_auc -value: [0.64786325 0.67521368 0.66709402 0.66709402 0.67008547 0.67222222 - 0.67521368 0.68333333 0.68333333 0.67777778] - -mean value: 0.6719230769230771 - -key: test_jcc -value: [0.2 0.2 0.25 0.2 0.33333333 0.33333333 - 0. 0. 0.25 0. ] - -mean value: 0.17666666666666667 - -key: train_jcc -value: [0.33333333 0.38709677 0.36666667 0.36666667 0.35714286 0.39393939 - 0.38709677 0.40625 0.40625 0.38709677] - -mean value: 0.3791539240329563 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 7 - -mean value: 7.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.06 - -Accuracy on Blind test: 0.57 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01078463 0.01264167 0.01373768 0.01230025 0.01327467 0.01310778 - 0.01337218 0.01272774 0.01252699 0.01253462] - -mean value: 0.012700819969177246 - -key: score_time -value: [0.00824046 0.01123905 0.01123929 0.01127291 0.01127601 0.0113132 - 0.01129079 0.01124072 0.01128697 0.01134467] - -mean value: 0.010974407196044922 - -key: test_mcc -value: [ 0.74535599 0.74535599 0.48795004 -0.06666667 0.46666667 0.6 - 1. 0.1490712 -0.29277002 0.73029674] - -mean value: 0.45652599414297745 - -key: train_mcc -value: [0.88152145 0.88152145 0.91067388 0.79523358 1. 0.9703421 - 1. 0.94196687 0.88152145 0.89081333] - -mean value: 0.9153594107114607 - -key: test_fscore -value: [0.8 0.8 0.5 0.33333333 0.66666667 0.75 - 1. 0.4 0. 0.8 ] - -mean value: 0.605 - -key: train_fscore -value: [0.91666667 0.91666667 0.93877551 0.84444444 1. 0.98113208 - 1. 0.96296296 0.91666667 0.93103448] - -mean value: 0.9408349475841808 - -key: test_precision -value: [1. 1. 1. 0.33333333 0.66666667 0.6 - 1. 0.5 0. 0.66666667] - -mean value: 0.6766666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 0.96296296 - 1. 0.92857143 1. 0.87096774] - -mean value: 0.9762502133469877 - -key: test_recall -value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 1. - 1. 0.33333333 0. 1. ] - -mean value: 0.6 - -key: train_recall -value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 1. - 1. 1. 0.84615385 1. ] - -mean value: 0.9153846153846154 - -key: test_accuracy -value: [0.875 0.875 0.75 0.5 0.75 0.75 - 1. 0.625 0.5 0.85714286] - -mean value: 0.7482142857142857 - -key: train_accuracy -value: [0.94366197 0.94366197 0.95774648 0.90140845 1. 0.98591549 - 1. 0.97183099 0.94366197 0.94444444] - -mean value: 0.9592331768388107 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.66666667 0.46666667 0.73333333 0.8 - 1. 0.56666667 0.4 0.9 ] - -mean value: 0.72 - -key: train_roc_auc -value: [0.92307692 0.92307692 0.94230769 0.86538462 1. 0.98888889 - 1. 0.97777778 0.92307692 0.95555556] - -mean value: 0.94991452991453 - -key: test_jcc -value: [0.66666667 0.66666667 0.33333333 0.2 0.5 0.6 - 1. 0.25 0. 0.66666667] - -mean value: 0.4883333333333334 - -key: train_jcc -value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 0.96296296 - 1. 0.92857143 0.84615385 0.87096774] - -mean value: 0.8916348287316029 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 17 - -mean value: 17.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.72 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00896978 0.01184177 0.0120883 0.01203012 0.01236367 0.01237869 - 0.01220155 0.01421261 0.01220298 0.0120194 ] - -mean value: 0.012030887603759765 - -key: score_time -value: [0.00857139 0.01130891 0.01136184 0.01203728 0.011446 0.01147342 - 0.01199031 0.01191354 0.01148462 0.01145744] - -mean value: 0.011304473876953125 - -key: test_mcc -value: [0.46666667 0.48795004 0.48795004 0.4472136 0.46666667 0.25819889 - 0. 0. 0.1490712 0.73029674] - -mean value: 0.3494013833369193 - -key: train_mcc -value: [0.81567142 0.36210341 0.93931624 0.9703421 1. 1. - 0.68088097 0.65199786 0.88152145 0.97058178] - -mean value: 0.8272415231599487 - -key: test_fscore -value: [0.66666667 0.5 0.5 0.66666667 0.66666667 0.57142857 - 0. 0. 0.4 0.8 ] - -mean value: 0.47714285714285715 - -key: train_fscore -value: [0.88135593 0.32258065 0.96153846 0.98113208 1. 1. - 0.73170732 0.7 0.91666667 0.98113208] - -mean value: 0.8476113173586375 - -key: test_precision -value: [0.66666667 1. 1. 0.5 0.66666667 0.5 - 0. 0. 0.5 0.66666667] - -mean value: 0.55 - -key: train_precision -value: [0.78787879 1. 0.96153846 0.96296296 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9712380212380213 - -key: test_recall -value: [0.66666667 0.33333333 0.33333333 1. 0.66666667 0.66666667 - 0. 0. 0.33333333 1. ] - -mean value: 0.5 - -key: train_recall -value: [1. 0.19230769 0.96153846 1. 1. 1. - 0.57692308 0.53846154 0.84615385 0.96296296] - -mean value: 0.8078347578347579 - -key: test_accuracy -value: [0.75 0.75 0.75 0.625 0.75 0.625 - 0.625 0.625 0.625 0.85714286] - -mean value: 0.6982142857142857 - -key: train_accuracy -value: [0.90140845 0.70422535 0.97183099 0.98591549 1. 1. - 0.84507042 0.83098592 0.94366197 0.98611111] - -mean value: 0.9169209702660407 - -key: test_roc_auc -value: [0.73333333 0.66666667 0.66666667 0.7 0.73333333 0.63333333 - 0.5 0.5 0.56666667 0.9 ] - -mean value: 0.6599999999999999 - -key: train_roc_auc -value: [0.92222222 0.59615385 0.96965812 0.98888889 1. 1. - 0.78846154 0.76923077 0.92307692 0.98148148] - -mean value: 0.893917378917379 - -key: test_jcc -value: [0.5 0.33333333 0.33333333 0.5 0.5 0.4 - 0. 0. 0.25 0.66666667] - -mean value: 0.3483333333333333 - -key: train_jcc -value: [0.78787879 0.19230769 0.92592593 0.96296296 1. 1. - 0.57692308 0.53846154 0.84615385 0.96296296] - -mean value: 0.7793576793576793 - -key: TN -value: 41 - -mean value: 41.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 14 - -mean value: 14.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.06 - -Accuracy on Blind test: 0.5 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.07852364 0.07593727 0.07648396 0.07617307 0.07601118 0.07563257 - 0.07694602 0.07653785 0.07624555 0.07758784] - -mean value: 0.07660789489746093 - -key: score_time -value: [0.01418972 0.0142076 0.014153 0.01459336 0.01418948 0.01423931 - 0.01444244 0.01423192 0.01443076 0.0149498 ] - -mean value: 0.014362740516662597 - -key: test_mcc -value: [0.74535599 0.74535599 0.25819889 1. 1. 0.6 - 1. 1. 0.74535599 0.54772256] - -mean value: 0.7641989424752118 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.57142857 1. 1. 0.75 - 1. 1. 0.8 0.66666667] - -mean value: 0.8388095238095238 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.5 1. 1. 0.6 1. 1. 1. 0.5] - -mean value: 0.86 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 1. 1. 1. - 1. 1. 0.66666667 1. ] - -mean value: 0.8666666666666668 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.875 0.625 1. 1. 0.75 - 1. 1. 0.875 0.71428571] - -mean value: 0.8714285714285713 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.63333333 1. 1. 0.8 - 1. 1. 0.83333333 0.8 ] - -mean value: 0.8733333333333333 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.66666667 0.4 1. 1. 0.6 - 1. 1. 0.66666667 0.5 ] - -mean value: 0.7500000000000001 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 25 - -mean value: 25.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.72 - -Accuracy on Blind test: 0.88 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02951646 0.03389382 0.02654004 0.02991986 0.03635168 0.03002954 - 0.03156233 0.04396296 0.03435445 0.02904296] - -mean value: 0.032517409324645995 - -key: score_time -value: [0.02055669 0.02374935 0.01841545 0.01818538 0.01966786 0.01749253 - 0.02351284 0.02446222 0.01901722 0.01872277] - -mean value: 0.0203782320022583 - -key: test_mcc -value: [0.74535599 0.74535599 0.74535599 1. 1. 0.6 - 0.74535599 0.74535599 0.74535599 0.73029674] - -mean value: 0.7802432698339802 - -key: train_mcc -value: [0.96986363 0.96986363 1. 1. 0.94010481 1. - 1. 1. 0.96986363 1. ] - -mean value: 0.9849695685748777 - -key: test_fscore -value: [0.8 0.8 0.8 1. 1. 0.75 0.8 0.8 0.8 0.8 ] - -mean value: 0.835 - -key: train_fscore -value: [0.98039216 0.98039216 1. 1. 0.96 1. - 1. 1. 0.98039216 1. ] - -mean value: 0.9901176470588234 - -key: test_precision -value: [1. 1. 1. 1. 1. 0.6 - 1. 1. 1. 0.66666667] - -mean value: 0.9266666666666665 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 1. 1. 1. - 0.66666667 0.66666667 0.66666667 1. ] - -mean value: 0.8 - -key: train_recall -value: [0.96153846 0.96153846 1. 1. 0.92307692 1. - 1. 1. 0.96153846 1. ] - -mean value: 0.9807692307692308 - -key: test_accuracy -value: [0.875 0.875 0.875 1. 1. 0.75 - 0.875 0.875 0.875 0.85714286] - -mean value: 0.8857142857142858 - -key: train_accuracy -value: [0.98591549 0.98591549 1. 1. 0.97183099 1. - 1. 1. 0.98591549 1. ] - -mean value: 0.9929577464788732 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.83333333 1. 1. 0.8 - 0.83333333 0.83333333 0.83333333 0.9 ] - -mean value: 0.8699999999999999 - -key: train_roc_auc -value: [0.98076923 0.98076923 1. 1. 0.96153846 1. - 1. 1. 0.98076923 1. ] - -mean value: 0.9903846153846153 - -key: test_jcc -value: [0.66666667 0.66666667 0.66666667 1. 1. 0.6 - 0.66666667 0.66666667 0.66666667 0.66666667] - -mean value: 0.7266666666666668 - -key: train_jcc -value: [0.96153846 0.96153846 1. 1. 0.92307692 1. - 1. 1. 0.96153846 1. ] - -mean value: 0.9807692307692308 - -key: TN -value: 47 - -mean value: 47.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 23 - -mean value: 23.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01245141 0.01464605 0.01453471 0.01492357 0.01498866 0.01470661 - 0.01472354 0.01476574 0.01528478 0.01498938] - -mean value: 0.014601445198059082 - -key: score_time -value: [0.01121616 0.01147175 0.01149368 0.01152205 0.01179218 0.01165199 - 0.01153016 0.01156569 0.01180983 0.01152635] - -mean value: 0.011557984352111816 - -key: test_mcc -value: [-0.29277002 -0.06666667 0.74535599 -0.4472136 0.1490712 0.77459667 - 0.1490712 -0.29277002 0.48795004 0.73029674] - -mean value: 0.1936921532620129 - -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 0.8 0. 0.4 0.85714286 - 0.4 0. 0.5 0.8 ] - -mean value: 0.409047619047619 - -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 1. 0. 0.5 0.75 - 0.5 0. 1. 0.66666667] - -mean value: 0.475 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0. 0.33333333 0.66666667 0. 0.33333333 1. - 0.33333333 0. 0.33333333 1. ] - -mean value: 0.4 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.5 0.5 0.875 0.375 0.625 0.875 - 0.625 0.5 0.75 0.85714286] - -mean value: 0.6482142857142856 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.4 0.46666667 0.83333333 0.3 0.56666667 0.9 - 0.56666667 0.4 0.66666667 0.9 ] - -mean value: 0.6000000000000001 - -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 0.66666667 0. 0.25 0.75 - 0.25 0. 0.33333333 0.66666667] - -mean value: 0.31166666666666665 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 11 - -mean value: 11.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.18 - -Accuracy on Blind test: 0.65 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.17009902 0.14949942 0.16850829 0.15098715 0.15430665 0.15416098 - 0.1536386 0.15502644 0.12563252 0.17038202] - -mean value: 0.15522410869598388 - -key: score_time -value: [0.00882673 0.00872111 0.00868773 0.00874734 0.00878859 0.0092032 - 0.00890565 0.00874686 0.00879812 0.00924945] - -mean value: 0.008867478370666504 - -key: test_mcc -value: [0.74535599 0.74535599 0.46666667 0.77459667 1. 0.6 - 0.74535599 1. 0.74535599 0.54772256] - -mean value: 0.7370409863413036 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.66666667 0.85714286 1. 0.75 - 0.8 1. 0.8 0.66666667] - -mean value: 0.8140476190476191 - -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 0.75 1. 0.6 - 1. 1. 1. 0.5 ] - -mean value: 0.8516666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 1. 1. 1. - 0.66666667 1. 0.66666667 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.875 0.75 0.875 1. 0.75 - 0.875 1. 0.875 0.71428571] - -mean value: 0.8589285714285714 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.73333333 0.9 1. 0.8 - 0.83333333 1. 0.83333333 0.8 ] - -mean value: 0.8566666666666667 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.66666667 0.5 0.75 1. 0.6 - 0.66666667 1. 0.66666667 0.5 ] - -mean value: 0.7016666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis() - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01000357 0.01294374 0.01351094 0.01364636 0.01392603 0.01322412 - 0.0132308 0.01357937 0.01318026 0.01335979] - -mean value: 0.013060498237609863 - -key: score_time -value: [0.01163244 0.0116744 0.01197124 0.01201701 0.01205063 0.01258826 - 0.01453471 0.01262641 0.01148725 0.01319456] - -mean value: 0.012377691268920899 - -key: test_mcc -value: [-0.6 -0.06666667 0.74535599 -0.29277002 0.25819889 0.46666667 - -0.46666667 -0.29277002 -0.06666667 0. ] - -mean value: -0.03153184948553623 - -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 0.8 0. 0.57142857 0.66666667 - 0.25 0. 0.33333333 0. ] - -mean value: 0.2954761904761905 - -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 1. 0. 0.5 0.66666667 - 0.2 0. 0.33333333 0. ] - -mean value: 0.30333333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0. 0.33333333 0.66666667 0. 0.66666667 0.66666667 - 0.33333333 0. 0.33333333 0. ] - -mean value: 0.3 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.25 0.5 0.875 0.5 0.625 0.75 - 0.25 0.5 0.5 0.71428571] - -mean value: 0.5464285714285715 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.2 0.46666667 0.83333333 0.4 0.63333333 0.73333333 - 0.26666667 0.4 0.46666667 0.5 ] - -mean value: 0.48999999999999994 - -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 0.66666667 0. 0.4 0.5 - 0.14285714 0. 0.2 0. ] - -mean value: 0.21095238095238095 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 9 - -mean value: 9.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.17 - -Accuracy on Blind test: 0.48 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.04468751 0.05357623 0.05370712 0.03210664 0.01244235 0.01242089 - 0.01257348 0.01239419 0.01240015 0.03188992] - -mean value: 0.02781984806060791 - -key: score_time -value: [0.02110004 0.02102828 0.02099276 0.02104115 0.01156378 0.01158667 - 0.0115962 0.01159286 0.01153088 0.02093101] - -mean value: 0.01629636287689209 - -key: test_mcc -value: [ 0.74535599 0.46666667 0.74535599 0.6 0.46666667 0.6 - 0.77459667 0.1490712 -0.06666667 0.73029674] - -mean value: 0.5211343262748217 - -key: train_mcc -value: [0.96986363 0.96986363 1. 1. 1. 1. - 1. 0.96986363 1. 1. ] - -mean value: 0.9909590875629279 - -key: test_fscore -value: [0.8 0.66666667 0.8 0.75 0.66666667 0.75 - 0.85714286 0.4 0.33333333 0.8 ] - -mean value: 0.6823809523809523 - -key: train_fscore -value: [0.98039216 0.98039216 1. 1. 1. 1. - 1. 0.98039216 1. 1. ] - -mean value: 0.9941176470588236 - -key: test_precision -value: [1. 0.66666667 1. 0.6 0.66666667 0.6 - 0.75 0.5 0.33333333 0.66666667] - -mean value: 0.6783333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 1. 0.66666667 1. - 1. 0.33333333 0.33333333 1. ] - -mean value: 0.7333333333333333 - -key: train_recall -value: [0.96153846 0.96153846 1. 1. 1. 1. - 1. 0.96153846 1. 1. ] - -mean value: 0.9884615384615385 - -key: test_accuracy -value: [0.875 0.75 0.875 0.75 0.75 0.75 - 0.875 0.625 0.5 0.85714286] - -mean value: 0.7607142857142857 - -key: train_accuracy -value: [0.98591549 0.98591549 1. 1. 1. 1. - 1. 0.98591549 1. 1. ] - -mean value: 0.995774647887324 - -key: test_roc_auc -value: [0.83333333 0.73333333 0.83333333 0.8 0.73333333 0.8 - 0.9 0.56666667 0.46666667 0.9 ] - -mean value: 0.7566666666666666 - -key: train_roc_auc -value: [0.98076923 0.98076923 1. 1. 1. 1. - 1. 0.98076923 1. 1. ] - -mean value: 0.9942307692307694 - -key: test_jcc -value: [0.66666667 0.5 0.66666667 0.6 0.5 0.6 - 0.75 0.25 0.2 0.66666667] - -mean value: 0.54 - -key: train_jcc -value: [0.96153846 0.96153846 1. 1. 1. 1. - 1. 0.96153846 1. 1. ] - -mean value: 0.9884615384615385 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 21 - -mean value: 21.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.68 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: 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 - baseline_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: 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 - baseline_CV['Resampling'] = rs_none -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: 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 - baseline_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: 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 - baseline_BT['Resampling'] = rs_none -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.12132359 0.10357308 0.14754748 0.10202861 0.10264635 0.11086059 - 0.16924834 0.22648358 0.17872405 0.17529178] - -mean value: 0.14377274513244628 - -key: score_time -value: [0.02111197 0.01191688 0.02108765 0.01181531 0.02225852 0.01208162 - 0.02574563 0.02083993 0.02090573 0.01990175] - -mean value: 0.018766498565673827 - -key: test_mcc -value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.6 - 0.77459667 0.1490712 0.06666667 0.3 ] - -mean value: 0.4247713186074663 - -key: train_mcc -value: [1. 0.96986363 1. 0.81830122 1. 1. - 1. 0.96986363 1. 0.77297107] - -mean value: 0.953099954418505 - -key: test_fscore -value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.75 - 0.85714286 0.4 0.5 0.5 ] - -mean value: 0.6273809523809524 - -key: train_fscore -value: [1. 0.98039216 1. 0.875 1. 1. - 1. 0.98039216 1. 0.82608696] - -mean value: 0.9661871270247229 - -key: test_precision -value: [1. 0.66666667 1. 0.33333333 0.66666667 0.6 - 0.75 0.5 0.4 0.5 ] - -mean value: 0.6416666666666667 - -key: train_precision -value: [1. 1. 1. 0.95454545 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9954545454545455 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 1. - 1. 0.33333333 0.66666667 0.5 ] - -mean value: 0.65 - -key: train_recall -value: [1. 0.96153846 1. 0.80769231 1. 1. - 1. 0.96153846 1. 0.7037037 ] - -mean value: 0.9434472934472934 - -key: test_accuracy -value: [0.875 0.75 0.875 0.5 0.75 0.75 - 0.875 0.625 0.5 0.71428571] - -mean value: 0.7214285714285714 - -key: train_accuracy -value: [1. 0.98591549 1. 0.91549296 1. 1. - 1. 0.98591549 1. 0.88888889] - -mean value: 0.9776212832550861 - -key: test_roc_auc -value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.8 - 0.9 0.56666667 0.53333333 0.65 ] - -mean value: 0.705 - -key: train_roc_auc -value: [1. 0.98076923 1. 0.89273504 1. 1. - 1. 0.98076923 1. 0.85185185] - -mean value: 0.9706125356125355 - -key: test_jcc -value: [0.66666667 0.5 0.66666667 0.2 0.5 0.6 - 0.75 0.25 0.33333333 0.33333333] - -mean value: 0.47999999999999987 - -key: train_jcc -value: [1. 0.96153846 1. 0.77777778 1. 1. - 1. 0.96153846 1. 0.7037037 ] - -mean value: 0.9404558404558406 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 19 - -mean value: 19.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.09 - -Accuracy on Blind test: 0.6 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02095628 0.02766466 0.02307987 0.02544475 0.02728295 0.0233891 - 0.02444839 0.02446628 0.02351332 0.02731419] - -mean value: 0.024755978584289552 - -key: score_time -value: [0.01138926 0.01142073 0.01142931 0.01147127 0.01143646 0.01138425 - 0.01142192 0.0114305 0.0113945 0.01140404] - -mean value: 0.01141822338104248 - -key: test_mcc -value: [0.81649658 0.81649658 1. 0.21821789 0.40824829 0.81649658 - 0.6 0.65465367 0.21821789 0.6 ] - -mean value: 0.6148827484427002 - -key: train_mcc -value: [0.97801929 0.95555556 0.95555556 0.95555556 0.95555556 0.97801929 - 0.95555556 0.95555556 1. 0.93356387] - -mean value: 0.962293579241685 - -key: test_fscore -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.90909091 0.90909091 1. 0.66666667 0.72727273 0.88888889 - 0.8 0.83333333 0.66666667 0.8 ] - -mean value: 0.8201010101010102 - -key: train_fscore -value: [0.98876404 0.97777778 0.97777778 0.97777778 0.97777778 0.98876404 - 0.97777778 0.97777778 1. 0.96703297] - -mean value: 0.9811227723587275 - -key: test_precision -value: [0.83333333 0.83333333 1. 0.57142857 0.66666667 1. - 0.8 0.71428571 0.57142857 0.8 ] - -mean value: 0.779047619047619 - -key: train_precision -value: [1. 0.97777778 0.97777778 0.97777778 0.97777778 1. - 0.97777778 0.97777778 1. 0.95652174] - -mean value: 0.9823188405797103 - -key: test_recall -value: [1. 1. 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8] - -mean value: 0.8800000000000001 - -key: train_recall -value: [0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 - 0.97777778 0.97777778 1. 0.97777778] - -mean value: 0.9800000000000001 - -key: test_accuracy -value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8] - -mean value: 0.8 - -key: train_accuracy -value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889 - 0.97777778 0.97777778 1. 0.96666667] - -mean value: 0.9811111111111112 - -key: test_roc_auc -value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8] - -mean value: 0.8000000000000002 - -key: train_roc_auc -value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889 - 0.97777778 0.97777778 1. 0.96666667] - -mean value: 0.9811111111111112 - -key: test_jcc -value: [0.83333333 0.83333333 1. 0.5 0.57142857 0.8 - 0.66666667 0.71428571 0.5 0.66666667] - -mean value: 0.7085714285714286 - -key: train_jcc -value: [0.97777778 0.95652174 0.95652174 0.95652174 0.95652174 0.97777778 - 0.95652174 0.95652174 1. 0.93617021] - -mean value: 0.9630856203104123 - -key: TN -value: 36 - -mean value: 36.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.23 - -Accuracy on Blind test: 0.65 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.63891172 0.51611137 0.76950169 0.56394839 0.5907445 0.58179784 - 0.55293965 0.53497767 0.65838146 0.52853274] - -mean value: 0.5935847043991089 - -key: score_time -value: [0.01189089 0.01190567 0.01321769 0.01461577 0.01429796 0.0120244 - 0.01439905 0.01183438 0.01506233 0.01375699] - -mean value: 0.013300514221191407 - -key: test_mcc -value: [1. 0.81649658 1. 0.21821789 0.6 0.81649658 - 0.40824829 0.81649658 0.40824829 0.81649658] - -mean value: 0.6900700794874622 - -key: train_mcc -value: [0.97801929 1. 1. 1. 1. 0.97801929 - 1. 1. 1. 1. ] - -mean value: 0.9956038587687303 - -key: test_fscore -value: [1. 0.90909091 1. 0.66666667 0.8 0.88888889 - 0.66666667 0.90909091 0.72727273 0.88888889] - -mean value: 0.8456565656565657 - -key: train_fscore -value: [0.98876404 1. 1. 1. 1. 0.98876404 - 1. 1. 1. 1. ] - -mean value: 0.997752808988764 - -key: test_precision -value: [1. 0.83333333 1. 0.57142857 0.8 1. - 0.75 0.83333333 0.66666667 1. ] - -mean value: 0.8454761904761906 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.8 0.8 0.8 0.6 1. 0.8 0.8] - -mean value: 0.86 - -key: train_recall -value: [0.97777778 1. 1. 1. 1. 0.97777778 - 1. 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_accuracy -value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_accuracy -value: [0.98888889 1. 1. 1. 1. 0.98888889 - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_roc_auc -value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_roc_auc -value: [0.98888889 1. 1. 1. 1. 0.98888889 - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_jcc -value: [1. 0.83333333 1. 0.5 0.66666667 0.8 - 0.5 0.83333333 0.57142857 0.8 ] - -mean value: 0.7504761904761905 - -key: train_jcc -value: [0.97777778 1. 1. 1. 1. 0.97777778 - 1. 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: TN -value: 41 - -mean value: 41.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.68 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.011446 0.01138425 0.00847435 0.0083313 0.00806546 0.00832367 - 0.0079782 0.00797153 0.00808334 0.00805235] - -mean value: 0.008811044692993163 - -key: score_time -value: [0.01148987 0.01026154 0.00851727 0.00848866 0.00831866 0.00834775 - 0.00825739 0.00826335 0.00835299 0.00837731] - -mean value: 0.008867478370666504 - -key: test_mcc -value: [ 0.40824829 0.65465367 0.33333333 0.2 0.5 -0.21821789 - 0.2 0.33333333 -0.33333333 0. ] - -mean value: 0.20780174042691812 - -key: train_mcc -value: [0.4412613 0.57055978 0.57055978 0.66097134 0.57055978 0.51708769 - 0.76486616 0.5527708 0.6681531 0.63737744] - -mean value: 0.5954167177357577 - -key: test_fscore -value: [0.72727273 0.83333333 0.71428571 0.6 0.76923077 0.5 - 0.6 0.71428571 0.57142857 0.66666667] - -mean value: 0.6696503496503496 - -key: train_fscore -value: [0.75229358 0.8 0.8 0.84 0.8 0.77876106 - 0.88659794 0.79279279 0.83870968 0.82568807] - -mean value: 0.8114843121679529 - -key: test_precision -value: [0.66666667 0.71428571 0.55555556 0.6 0.625 0.42857143 - 0.6 0.55555556 0.44444444 0.5 ] - -mean value: 0.5690079365079365 - -key: train_precision -value: [0.640625 0.67692308 0.67692308 0.76363636 0.67692308 0.64705882 - 0.82692308 0.66666667 0.8125 0.703125 ] - -mean value: 0.709130416152475 - -key: test_recall -value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 1. ] - -mean value: 0.8400000000000001 - -key: train_recall -value: [0.91111111 0.97777778 0.97777778 0.93333333 0.97777778 0.97777778 - 0.95555556 0.97777778 0.86666667 1. ] - -mean value: 0.9555555555555555 - -key: test_accuracy -value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5] - -mean value: 0.5900000000000001 - -key: train_accuracy -value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222 - 0.87777778 0.74444444 0.83333333 0.78888889] - -mean value: 0.7755555555555554 - -key: test_roc_auc -value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5] - -mean value: 0.5900000000000001 - -key: train_roc_auc -value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222 - 0.87777778 0.74444444 0.83333333 0.78888889] - -mean value: 0.7755555555555554 - -key: test_jcc -value: [0.57142857 0.71428571 0.55555556 0.42857143 0.625 0.33333333 - 0.42857143 0.55555556 0.4 0.5 ] - -mean value: 0.5112301587301588 - -key: train_jcc -value: [0.60294118 0.66666667 0.66666667 0.72413793 0.66666667 0.63768116 - 0.7962963 0.65671642 0.72222222 0.703125 ] - -mean value: 0.6843120203354327 - -key: TN -value: 17 - -mean value: 17.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 33 - -mean value: 33.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.55 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00854492 0.00822186 0.00823379 0.00817966 0.00828838 0.00816011 - 0.00876474 0.00883484 0.00813937 0.00822973] - -mean value: 0.008359742164611817 - -key: score_time -value: [0.00832868 0.00838923 0.00824618 0.00832224 0.00821042 0.00837898 - 0.00903296 0.00819707 0.00829124 0.00826311] - -mean value: 0.008366012573242187 - -key: test_mcc -value: [ 0.65465367 -0.40824829 0.81649658 0. 0.40824829 0.40824829 - 0.6 0.40824829 -0.40824829 0. ] - -mean value: 0.2479398542099566 - -key: train_mcc -value: [0.60238451 0.73994007 0.65487619 0.64700558 0.69162666 0.62988978 - 0.71554175 0.78086881 0.64508188 0.65487619] - -mean value: 0.6762091428071246 - -key: test_fscore -value: [0.75 0.36363636 0.90909091 0.61538462 0.72727273 0.66666667 - 0.8 0.72727273 0.36363636 0.54545455] - -mean value: 0.6468414918414919 - -key: train_fscore -value: [0.80851064 0.875 0.83673469 0.82978723 0.85106383 0.82474227 - 0.86315789 0.89361702 0.81818182 0.83673469] - -mean value: 0.8437530092119255 - -key: test_precision -value: [1. 0.33333333 0.83333333 0.5 0.66666667 0.75 - 0.8 0.66666667 0.33333333 0.5 ] - -mean value: 0.6383333333333334 - -key: train_precision -value: [0.7755102 0.82352941 0.77358491 0.79591837 0.81632653 0.76923077 - 0.82 0.85714286 0.8372093 0.77358491] - -mean value: 0.8042037253825484 - -key: test_recall -value: [0.6 0.4 1. 0.8 0.8 0.6 0.8 0.8 0.4 0.6] - -mean value: 0.6799999999999999 - -key: train_recall -value: [0.84444444 0.93333333 0.91111111 0.86666667 0.88888889 0.88888889 - 0.91111111 0.93333333 0.8 0.91111111] - -mean value: 0.8888888888888887 - -key: test_accuracy -value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5] - -mean value: 0.62 - -key: train_accuracy -value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111 - 0.85555556 0.88888889 0.82222222 0.82222222] - -mean value: 0.8355555555555554 - -key: test_roc_auc -value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5] - -mean value: 0.62 - -key: train_roc_auc -value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111 - 0.85555556 0.88888889 0.82222222 0.82222222] - -mean value: 0.8355555555555556 - -key: test_jcc -value: [0.6 0.22222222 0.83333333 0.44444444 0.57142857 0.5 - 0.66666667 0.57142857 0.22222222 0.375 ] - -mean value: 0.5006746031746031 - -key: train_jcc -value: [0.67857143 0.77777778 0.71929825 0.70909091 0.74074074 0.70175439 - 0.75925926 0.80769231 0.69230769 0.71929825] - -mean value: 0.7305790992633099 - -key: TN -value: 28 - -mean value: 28.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.1 - -Accuracy on Blind test: 0.52 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01051331 0.00788808 0.0079484 0.00784755 0.00783825 0.00789666 - 0.00844765 0.00793743 0.00793314 0.00828576] - -mean value: 0.00825362205505371 - -key: score_time -value: [0.01452923 0.00902128 0.00907946 0.00902486 0.00899076 0.00922275 - 0.00933123 0.00924468 0.00922418 0.00955987] - -mean value: 0.00972282886505127 - -key: test_mcc -value: [ 0.2 0.6 0.81649658 0.5 0.65465367 -0.40824829 - 0.5 0.65465367 0.21821789 0.40824829] - -mean value: 0.41440218125796735 - -key: train_mcc -value: [0.60540551 0.65487619 0.60540551 0.62237591 0.58969198 0.67082039 - 0.62360956 0.58137767 0.73624773 0.60540551] - -mean value: 0.6295215990305435 - -key: test_fscore -value: [0.6 0.8 0.90909091 0.76923077 0.83333333 0.36363636 - 0.76923077 0.83333333 0.66666667 0.72727273] - -mean value: 0.7271794871794872 - -key: train_fscore -value: [0.8125 0.83673469 0.8125 0.81318681 0.80808081 0.84210526 - 0.8172043 0.8 0.87234043 0.8125 ] - -mean value: 0.822715230491025 - -key: test_precision -value: [0.6 0.8 0.83333333 0.625 0.71428571 0.33333333 - 0.625 0.71428571 0.57142857 0.66666667] - -mean value: 0.6483333333333333 - -key: train_precision -value: [0.76470588 0.77358491 0.76470588 0.80434783 0.74074074 0.8 - 0.79166667 0.76 0.83673469 0.76470588] - -mean value: 0.7801192480091116 - -key: test_recall -value: [0.6 0.8 1. 1. 1. 0.4 1. 1. 0.8 0.8] - -mean value: 0.8400000000000001 - -key: train_recall -value: [0.86666667 0.91111111 0.86666667 0.82222222 0.88888889 0.88888889 - 0.84444444 0.84444444 0.91111111 0.86666667] - -mean value: 0.8711111111111112 - -key: test_accuracy -value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7] - -mean value: 0.69 - -key: train_accuracy -value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333 - 0.81111111 0.78888889 0.86666667 0.8 ] - -mean value: 0.8122222222222224 - -key: test_roc_auc -value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7] - -mean value: 0.69 - -key: train_roc_auc -value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333 - 0.81111111 0.78888889 0.86666667 0.8 ] - -mean value: 0.8122222222222222 - -key: test_jcc -value: [0.42857143 0.66666667 0.83333333 0.625 0.71428571 0.22222222 - 0.625 0.71428571 0.5 0.57142857] - -mean value: 0.5900793650793651 - -key: train_jcc -value: [0.68421053 0.71929825 0.68421053 0.68518519 0.6779661 0.72727273 - 0.69090909 0.66666667 0.77358491 0.68421053] - -mean value: 0.6993514501950366 - -key: TN -value: 27 - -mean value: 27.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.04 - -Accuracy on Blind test: 0.48 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00914907 0.00866842 0.00909472 0.00902867 0.00853419 0.00859928 - 0.0086832 0.00868559 0.0086875 0.00870299] - -mean value: 0.008783364295959472 - -key: score_time -value: [0.00892496 0.0084846 0.00915527 0.00837111 0.00835466 0.00840259 - 0.00836229 0.00844765 0.00840688 0.0084002 ] - -mean value: 0.008531022071838378 - -key: test_mcc -value: [0.40824829 0.6 1. 0.40824829 0.40824829 0.81649658 - 0.6 0.65465367 0.21821789 0.40824829] - -mean value: 0.5522361303727148 - -key: train_mcc -value: [0.8230355 0.86666667 0.84632727 0.84465303 0.82548988 0.87011096 - 0.88910845 0.86666667 0.91111111 0.84632727] - -mean value: 0.8589496793973822 - -key: test_fscore -value: [0.66666667 0.8 1. 0.72727273 0.72727273 0.88888889 - 0.8 0.83333333 0.66666667 0.66666667] - -mean value: 0.7776767676767677 - -key: train_fscore -value: [0.91304348 0.93333333 0.92473118 0.92134831 0.91489362 0.93617021 - 0.94505495 0.93333333 0.95555556 0.92473118] - -mean value: 0.9302195155523411 - -key: test_precision -value: [0.75 0.8 1. 0.66666667 0.66666667 1. - 0.8 0.71428571 0.57142857 0.75 ] - -mean value: 0.7719047619047619 - -key: train_precision -value: [0.89361702 0.93333333 0.89583333 0.93181818 0.87755102 0.89795918 - 0.93478261 0.93333333 0.95555556 0.89583333] - -mean value: 0.9149616904760952 - -key: test_recall -value: [0.6 0.8 1. 0.8 0.8 0.8 0.8 1. 0.8 0.6] - -mean value: 0.8 - -key: train_recall -value: [0.93333333 0.93333333 0.95555556 0.91111111 0.95555556 0.97777778 - 0.95555556 0.93333333 0.95555556 0.95555556] - -mean value: 0.9466666666666667 - -key: test_accuracy -value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7] - -mean value: 0.77 - -key: train_accuracy -value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333 - 0.94444444 0.93333333 0.95555556 0.92222222] - -mean value: 0.928888888888889 - -key: test_roc_auc -value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7] - -mean value: 0.77 - -key: train_roc_auc -value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333 - 0.94444444 0.93333333 0.95555556 0.92222222] - -mean value: 0.928888888888889 - -key: test_jcc -value: [0.5 0.66666667 1. 0.57142857 0.57142857 0.8 - 0.66666667 0.71428571 0.5 0.5 ] - -mean value: 0.6490476190476191 - -key: train_jcc -value: [0.84 0.875 0.86 0.85416667 0.84313725 0.88 - 0.89583333 0.875 0.91489362 0.86 ] - -mean value: 0.8698030871923237 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.1 - -Accuracy on Blind test: 0.5 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.34990168 0.50119901 0.34612966 0.31338263 0.36281276 0.32218599 - 0.34134626 0.46834445 0.28037453 0.3393724 ] - -mean value: 0.3625049352645874 - -key: score_time -value: [0.01199079 0.01198435 0.011935 0.01188803 0.01190996 0.01193237 - 0.01193595 0.0119586 0.01192045 0.01193857] - -mean value: 0.011939406394958496 - -key: test_mcc -value: [0.65465367 0.81649658 1. 0. 0.2 0.81649658 - 0.40824829 0.65465367 0. 0.81649658] - -mean value: 0.5367045374662995 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.75 0.90909091 1. 0.54545455 0.6 0.88888889 - 0.66666667 0.83333333 0.61538462 0.88888889] - -mean value: 0.7697707847707849 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 0.5 0.6 1. - 0.75 0.71428571 0.5 1. ] - -mean value: 0.7897619047619048 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.6 1. 1. 0.6 0.6 0.8 0.6 1. 0.8 0.8] - -mean value: 0.78 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9] - -mean value: 0.76 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9] - -mean value: 0.76 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.6 0.83333333 1. 0.375 0.42857143 0.8 - 0.5 0.71428571 0.44444444 0.8 ] - -mean value: 0.6495634920634921 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 39 - -mean value: 39.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.13 - -Accuracy on Blind test: 0.62 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01446295 0.01370049 0.01050329 0.01081491 0.01074243 0.01266026 - 0.01105475 0.01161981 0.01020932 0.01007462] - -mean value: 0.011584281921386719 - -key: score_time -value: [0.01130819 0.01019692 0.00851107 0.00835633 0.00995207 0.00970793 - 0.00899029 0.00867486 0.00860381 0.00912476] - -mean value: 0.009342622756958009 - -key: test_mcc -value: [1. 0.81649658 0.65465367 0.65465367 1. 0.6 - 0.65465367 0.40824829 0.40824829 0.81649658] - -mean value: 0.701345075490711 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 0.83333333 0.83333333 1. 0.8 - 0.75 0.72727273 0.72727273 0.88888889] - -mean value: 0.846919191919192 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 0.71428571 0.71428571 1. 0.8 - 1. 0.66666667 0.66666667 1. ] - -mean value: 0.8395238095238096 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.6 0.8 0.8 0.8] - -mean value: 0.8800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 0.71428571 0.71428571 1. 0.66666667 - 0.6 0.57142857 0.57142857 0.8 ] - -mean value: 0.7471428571428571 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.88 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.08087611 0.08038592 0.08071065 0.08093286 0.08061075 0.0805552 - 0.08088231 0.08353758 0.08065701 0.08074427] - -mean value: 0.08098926544189453 - -key: score_time -value: [0.01645565 0.01689053 0.01666975 0.01656699 0.0165782 0.01668191 - 0.01906228 0.01703238 0.01756358 0.01673245] - -mean value: 0.017023372650146484 - -key: test_mcc -value: [0.81649658 0.6 1. 0.40824829 0.6 0.5 - 0.2 0.65465367 0.21821789 0.65465367] - -mean value: 0.5652270103043536 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.8 1. 0.72727273 0.8 0.57142857 - 0.6 0.83333333 0.66666667 0.75 ] - -mean value: 0.7637590187590187 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.8 1. 0.66666667 0.8 1. - 0.6 0.71428571 0.57142857 1. ] - -mean value: 0.8152380952380952 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 0.8 1. 0.8 0.8 0.4 0.6 1. 0.8 0.6] - -mean value: 0.76 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8] - -mean value: 0.77 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8] - -mean value: 0.77 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.66666667 1. 0.57142857 0.66666667 0.4 - 0.42857143 0.71428571 0.5 0.6 ] - -mean value: 0.6347619047619047 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 38 - -mean value: 38.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.3 - -Accuracy on Blind test: 0.7 - -Running classifier: 10 -Model_name: Extra Tree -Model func: ExtraTreeClassifier(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( -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00929761 0.0082314 0.00812483 0.0081141 0.00811553 0.00808263 - 0.00860763 0.00804114 0.00888228 0.00918865] - -mean value: 0.00846858024597168 - -key: score_time -value: [0.00910568 0.00822258 0.00867009 0.00826836 0.00844622 0.00843167 - 0.00880265 0.00870919 0.00882745 0.00881433] - -mean value: 0.008629822731018066 - -key: test_mcc -value: [ 0.6 0.6 0.40824829 0.5 0. 0.40824829 - 0.21821789 0.5 -0.40824829 0.40824829] - -mean value: 0.3234714471163718 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.72727273 0.76923077 0.44444444 0.66666667 - 0.5 0.76923077 0.36363636 0.66666667] - -mean value: 0.6507148407148406 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.8 0.66666667 0.625 0.5 0.75 - 0.66666667 0.625 0.33333333 0.75 ] - -mean value: 0.6516666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 0.8 0.8 1. 0.4 0.6 0.4 1. 0.4 0.6] - -mean value: 0.68 - -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 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7] - -mean value: 0.65 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.8 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7] - -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: [0.66666667 0.66666667 0.57142857 0.625 0.28571429 0.5 - 0.33333333 0.625 0.22222222 0.5 ] - -mean value: 0.4996031746031746 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 31 - -mean value: 31.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.02 - -Accuracy on Blind test: 0.55 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.08314943 1.05717731 1.05498576 1.04640746 1.04371905 1.05806494 - 1.05173302 1.04391503 1.03815794 1.03380728] - -mean value: 1.0511117219924926 - -key: score_time -value: [0.09315157 0.0880518 0.08978987 0.0893538 0.08839059 0.08848882 - 0.0899272 0.09106064 0.08935261 0.0950737 ] - -mean value: 0.09026405811309815 - -key: test_mcc -value: [0.81649658 0.65465367 1. 0.65465367 0.81649658 0.65465367 - 0.65465367 0.65465367 0.40824829 0.65465367] - -mean value: 0.6969163476567177 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.83333333 1. 0.75 0.90909091 0.75 - 0.75 0.83333333 0.72727273 0.75 ] - -mean value: 0.8191919191919192 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.71428571 1. 1. 0.83333333 1. - 1. 0.71428571 0.66666667 1. ] - -mean value: 0.892857142857143 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 0.6] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8] - -mean value: 0.8300000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8] - -mean value: 0.8300000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.71428571 1. 0.6 0.83333333 0.6 - 0.6 0.71428571 0.57142857 0.6 ] - -mean value: 0.7033333333333334 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.6 - -Accuracy on Blind test: 0.82 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.8008554 0.86556268 0.83195925 0.85484838 0.86451149 0.86783385 - 0.90637803 0.84007192 0.82203436 0.86065936] - -mean value: 0.8514714717864991 - -key: score_time -value: [0.17614007 0.23751974 0.16117907 0.16920114 0.17847824 0.17201066 - 0.16586041 0.17395544 0.19984627 0.19024968] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -mean value: 0.18244407176971436 - -key: test_mcc -value: [0.81649658 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658 - 0.81649658 0.40824829 0.40824829 0.65465367] - -mean value: 0.6862940497690287 - -key: train_mcc -value: [1. 0.97801929 0.97801929 0.95555556 0.97801929 1. - 0.97801929 0.97801929 1. 1. ] - -mean value: 0.9845652024773812 - -key: test_fscore -value: [0.90909091 0.83333333 0.88888889 0.75 0.90909091 0.88888889 - 0.88888889 0.72727273 0.72727273 0.75 ] - -mean value: 0.8272727272727272 - -key: train_fscore -value: [1. 0.98901099 0.98901099 0.97777778 0.98901099 1. - 0.98901099 0.98901099 1. 1. ] - -mean value: 0.9922832722832723 - -key: test_precision -value: [0.83333333 0.71428571 1. 1. 0.83333333 1. - 1. 0.66666667 0.66666667 1. ] - -mean value: 0.8714285714285716 - -key: train_precision -value: [1. 0.97826087 0.97826087 0.97777778 0.97826087 1. - 0.97826087 0.97826087 1. 1. ] - -mean value: 0.9869082125603864 - -key: test_recall -value: [1. 1. 0.8 0.6 1. 0.8 0.8 0.8 0.8 0.6] - -mean value: 0.8200000000000001 - -key: train_recall -value: [1. 1. 1. 0.97777778 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_accuracy -value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8] - -mean value: 0.8300000000000001 - -key: train_accuracy -value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1. - 0.98888889 0.98888889 1. 1. ] - -mean value: 0.9922222222222222 - -key: test_roc_auc -value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8] - -mean value: 0.8300000000000001 - -key: train_roc_auc -value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1. - 0.98888889 0.98888889 1. 1. ] - -mean value: 0.9922222222222222 - -key: test_jcc -value: [0.83333333 0.71428571 0.8 0.6 0.83333333 0.8 - 0.8 0.57142857 0.57142857 0.6 ] - -mean value: 0.7123809523809523 - -key: train_jcc -value: [1. 0.97826087 0.97826087 0.95652174 0.97826087 1. - 0.97826087 0.97826087 1. 1. ] - -mean value: 0.9847826086956522 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.6 - -Accuracy on Blind test: 0.82 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.03647876 0.03303075 0.03647852 0.03536677 0.03443265 0.03521061 - 0.03624892 0.03567147 0.03307509 0.03529716] - -mean value: 0.03512907028198242 - -key: score_time -value: [0.0158205 0.01005316 0.06378174 0.00998211 0.01029038 0.01069117 - 0.00997925 0.00992107 0.01004457 0.0107832 ] - -mean value: 0.01613471508026123 - -key: test_mcc -value: [1. 0.81649658 1. 1. 1. 0.6 - 0.81649658 0.6 0.5 0.81649658] - -mean value: 0.8149489742783178 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 1. 1. 1. 0.8 - 0.88888889 0.8 0.76923077 0.88888889] - -mean value: 0.9056099456099458 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 1. 1. 0.8 - 1. 0.8 0.625 1. ] - -mean value: 0.9058333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9] - -mean value: 0.9 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9] - -mean value: 0.9 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 1. 1. 1. 0.66666667 - 0.8 0.66666667 0.625 0.8 ] - -mean value: 0.8391666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02231097 0.04178953 0.03549719 0.04157138 0.04162025 0.03488564 - 0.04136801 0.04148436 0.04149842 0.04407692] - -mean value: 0.03861026763916016 - -key: score_time -value: [0.02140141 0.02215552 0.01681995 0.02330375 0.01159453 0.02025843 - 0.01248741 0.01387024 0.02162814 0.02118063] - -mean value: 0.018470001220703126 - -key: test_mcc -value: [0.81649658 0.81649658 0.6 0.40824829 0.40824829 0.21821789 - 0.65465367 0.5 0.40824829 0. ] - -mean value: 0.4830609594191011 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.90909091 0.8 0.72727273 0.72727273 0.66666667 - 0.83333333 0.76923077 0.72727273 0.61538462] - -mean value: 0.7684615384615385 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.83333333 0.8 0.66666667 0.66666667 0.57142857 - 0.71428571 0.625 0.66666667 0.5 ] - -mean value: 0.6877380952380954 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 0.8 0.8 0.8 1. 1. 0.8 0.8] - -mean value: 0.8800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5] - -mean value: 0.73 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5] - -mean value: 0.73 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.83333333 0.66666667 0.57142857 0.57142857 0.5 - 0.71428571 0.625 0.57142857 0.44444444] - -mean value: 0.6331349206349206 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 29 - -mean value: 29.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.16 - -Accuracy on Blind test: 0.62 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01321387 0.01240396 0.00874496 0.0092957 0.00834417 0.00813174 - 0.00837374 0.00808525 0.00824094 0.00825596] - -mean value: 0.009309029579162598 - -key: score_time -value: [0.01152873 0.01367998 0.00886726 0.00834537 0.00828481 0.00844979 - 0.00816917 0.00834799 0.00844622 0.00816035] - -mean value: 0.009227967262268067 - -key: test_mcc -value: [ 0. 0.6 0.2 0.2 0.40824829 0.6 - 0. 0. -0.40824829 0.40824829] - -mean value: 0.20082482904638627 - -key: train_mcc -value: [0.4260261 0.53665631 0.51571581 0.64444444 0.68888889 0.60238451 - 0.62360956 0.51111111 0.51161666 0.62237591] - -mean value: 0.5682829320802611 - -key: test_fscore -value: [0.54545455 0.8 0.6 0.6 0.72727273 0.8 - 0.44444444 0.44444444 0.36363636 0.66666667] - -mean value: 0.599191919191919 - -key: train_fscore -value: [0.72916667 0.75294118 0.73809524 0.82222222 0.84444444 0.80851064 - 0.8045977 0.75555556 0.76086957 0.80898876] - -mean value: 0.7825391972164348 - -key: test_precision -value: [0.5 0.8 0.6 0.6 0.66666667 0.8 - 0.5 0.5 0.33333333 0.75 ] - -mean value: 0.605 - -key: train_precision -value: [0.68627451 0.8 0.79487179 0.82222222 0.84444444 0.7755102 - 0.83333333 0.75555556 0.74468085 0.81818182] - -mean value: 0.7875074733558554 - -key: test_recall -value: [0.6 0.8 0.6 0.6 0.8 0.8 0.4 0.4 0.4 0.6] - -mean value: 0.6 - -key: train_recall -value: [0.77777778 0.71111111 0.68888889 0.82222222 0.84444444 0.84444444 - 0.77777778 0.75555556 0.77777778 0.8 ] - -mean value: 0.78 - -key: test_accuracy -value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7] - -mean value: 0.6 - -key: train_accuracy -value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8 - 0.81111111 0.75555556 0.75555556 0.81111111] - -mean value: 0.7833333333333333 - -key: test_roc_auc -value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7] - -mean value: 0.6 - -key: train_roc_auc -value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8 - 0.81111111 0.75555556 0.75555556 0.81111111] - -mean value: 0.7833333333333332 - -key: test_jcc -value: [0.375 0.66666667 0.42857143 0.42857143 0.57142857 0.66666667 - 0.28571429 0.28571429 0.22222222 0.5 ] - -mean value: 0.44305555555555554 - -key: train_jcc -value: [0.57377049 0.60377358 0.58490566 0.69811321 0.73076923 0.67857143 - 0.67307692 0.60714286 0.61403509 0.67924528] - -mean value: 0.6443403754932071 - -key: TN -value: 30 - -mean value: 30.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 30 - -mean value: 30.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.26 - -Accuracy on Blind test: 0.35 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01003146 0.01199222 0.01306844 0.01315403 0.01348281 0.01319456 - 0.01266003 0.0128665 0.01410055 0.01346374] - -mean value: 0.012801432609558105 - -key: score_time -value: [0.00822568 0.01112962 0.01109433 0.0114522 0.01152349 0.01129174 - 0.0113523 0.0113461 0.01147819 0.01160097] - -mean value: 0.011049461364746094 - -key: test_mcc -value: [1. 0.33333333 1. 0. 0.6 0.81649658 - 0.40824829 0.81649658 0.40824829 1. ] - -mean value: 0.6382823076116511 - -key: train_mcc -value: [1. 0.51730613 0.97801929 0.97801929 1. 0.95650071 - 0.89442719 0.87447463 1. 1. ] - -mean value: 0.9198747257089167 - -key: test_fscore -value: [1. 0.33333333 1. 0.54545455 0.8 0.90909091 - 0.72727273 0.88888889 0.72727273 1. ] - -mean value: 0.7931313131313131 - -key: train_fscore -value: [1. 0.59375 0.98876404 0.98876404 1. 0.97826087 - 0.94736842 0.92857143 1. 1. ] - -mean value: 0.9425478809076917 - -key: test_precision -value: [1. 1. 1. 0.5 0.8 0.83333333 - 0.66666667 1. 0.66666667 1. ] - -mean value: 0.8466666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 0.95744681 - 0.9 1. 1. 1. ] - -mean value: 0.9857446808510637 - -key: test_recall -value: [1. 0.2 1. 0.6 0.8 1. 0.8 0.8 0.8 1. ] - -mean value: 0.8 - -key: train_recall -value: [1. 0.42222222 0.97777778 0.97777778 1. 1. - 1. 0.86666667 1. 1. ] - -mean value: 0.9244444444444444 - -key: test_accuracy -value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ] - -mean value: 0.8100000000000002 - -key: train_accuracy -value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778 - 0.94444444 0.93333333 1. 1. ] - -mean value: 0.9544444444444444 - -key: test_roc_auc -value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ] - -mean value: 0.8100000000000002 - -key: train_roc_auc -value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778 - 0.94444444 0.93333333 1. 1. ] - -mean value: 0.9544444444444444 - -key: test_jcc -value: [1. 0.2 1. 0.375 0.66666667 0.83333333 - 0.57142857 0.8 0.57142857 1. ] - -mean value: 0.7017857142857142 - -key: train_jcc -value: [1. 0.42222222 0.97777778 0.97777778 1. 0.95744681 - 0.9 0.86666667 1. 1. ] - -mean value: 0.9101891252955083 - -key: TN -value: 41 - -mean value: 41.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.28 - -Accuracy on Blind test: 0.7 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00887799 0.01214647 0.01226735 0.01229191 0.01261067 0.01222086 - 0.01222086 0.01250696 0.01220679 0.01205516] - -mean value: 0.011940503120422363 - -key: score_time -value: [0.00837612 0.0112083 0.0112288 0.0112474 0.01141977 0.01135349 - 0.01139736 0.01148057 0.0116353 0.01141691] - -mean value: 0.011076402664184571 - -key: test_mcc -value: [0.6 0.81649658 1. 0. 0.6 0.40824829 - 0.6 0.6 0.40824829 0.6 ] - -mean value: 0.5632993161855452 - -key: train_mcc -value: [0.95555556 0.95555556 0.93541435 1. 1. 0.95555556 - 0.97801929 1. 1. 0.93541435] - -mean value: 0.9715514653897289 - -key: test_fscore -value: [0.8 0.90909091 1. 0.54545455 0.8 0.72727273 - 0.8 0.8 0.66666667 0.8 ] - -mean value: 0.7848484848484849 - -key: train_fscore -value: [0.97777778 0.97777778 0.96551724 1. 1. 0.97777778 - 0.98876404 1. 1. 0.96774194] - -mean value: 0.9855356555140335 - -key: test_precision -value: [0.8 0.83333333 1. 0.5 0.8 0.66666667 - 0.8 0.8 0.75 0.8 ] - -mean value: 0.775 - -key: train_precision -value: [0.97777778 0.97777778 1. 1. 1. 0.97777778 - 1. 1. 1. 0.9375 ] - -mean value: 0.9870833333333333 - -key: test_recall -value: [0.8 1. 1. 0.6 0.8 0.8 0.8 0.8 0.6 0.8] - -mean value: 0.8 - -key: train_recall -value: [0.97777778 0.97777778 0.93333333 1. 1. 0.97777778 - 0.97777778 1. 1. 1. ] - -mean value: 0.9844444444444445 - -key: test_accuracy -value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8] - -mean value: 0.78 - -key: train_accuracy -value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778 - 0.98888889 1. 1. 0.96666667] - -mean value: 0.9855555555555556 - -key: test_roc_auc -value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8] - -mean value: 0.78 - -key: train_roc_auc -value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778 - 0.98888889 1. 1. 0.96666667] - -mean value: 0.9855555555555556 - -key: test_jcc -value: [0.66666667 0.83333333 1. 0.375 0.66666667 0.57142857 - 0.66666667 0.66666667 0.5 0.66666667] - -mean value: 0.6613095238095238 - -key: train_jcc -value: [0.95652174 0.95652174 0.93333333 1. 1. 0.95652174 - 0.97777778 1. 1. 0.9375 ] - -mean value: 0.9718176328502416 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.04 - -Accuracy on Blind test: 0.52 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.08591294 0.0870769 0.08603144 0.08604956 0.08540297 0.08636951 - 0.08703423 0.09059286 0.08867574 0.08594322] - -mean value: 0.08690893650054932 - -key: score_time -value: [0.01451015 0.01454926 0.01424074 0.01429844 0.01511765 0.01439977 - 0.01509309 0.01519299 0.01466537 0.01518536] - -mean value: 0.014725279808044434 - -key: test_mcc -value: [0.81649658 0.81649658 1. 1. 1. 0.6 - 1. 0.81649658 0.5 0.81649658] - -mean value: 0.8365986323710904 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.90909091 1. 1. 1. 0.8 - 1. 0.90909091 0.76923077 0.88888889] - -mean value: 0.9185392385392387 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.83333333 1. 1. 1. 0.8 - 1. 0.83333333 0.625 1. ] - -mean value: 0.8925000000000001 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 1. 1. 1. 0.8] - -mean value: 0.9600000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9] - -mean value: 0.9099999999999999 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9] - -mean value: 0.9099999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.83333333 1. 1. 1. 0.66666667 - 1. 0.83333333 0.625 0.8 ] - -mean value: 0.8591666666666666 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02613139 0.03143096 0.02982998 0.02666664 0.03157735 0.03069139 - 0.02667904 0.02771163 0.0300796 0.02920628] - -mean value: 0.02900042533874512 - -key: score_time -value: [0.01907611 0.02446222 0.01595473 0.02101159 0.01648688 0.02567482 - 0.01766539 0.01646376 0.02223778 0.02316689] - -mean value: 0.020220017433166503 - -key: test_mcc -value: [1. 0.81649658 1. 0.81649658 1. 0.81649658 - 0.81649658 1. 0.6 0.81649658] - -mean value: 0.8682482904638629 - -key: train_mcc -value: [0.97801929 0.97801929 0.97801929 1. 1. 1. - 0.97801929 1. 1. 1. ] - -mean value: 0.9912077175374605 - -key: test_fscore -value: [1. 0.90909091 1. 0.90909091 1. 0.88888889 - 0.88888889 1. 0.8 0.88888889] - -mean value: 0.9284848484848485 - -key: train_fscore -value: [0.98876404 0.98876404 0.98876404 1. 1. 1. - 0.98901099 1. 1. 1. ] - -mean value: 0.9955303123842449 - -key: test_precision -value: [1. 0.83333333 1. 0.83333333 1. 1. - 1. 1. 0.8 1. ] - -mean value: 0.9466666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.97826087 1. 1. 1. ] - -mean value: 0.9978260869565216 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [0.97777778 0.97777778 0.97777778 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9933333333333334 - -key: test_accuracy -value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9] - -mean value: 0.93 - -key: train_accuracy -value: [0.98888889 0.98888889 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_roc_auc -value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9] - -mean value: 0.93 - -key: train_roc_auc -value: [0.98888889 0.98888889 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_jcc -value: [1. 0.83333333 1. 0.83333333 1. 0.8 - 0.8 1. 0.66666667 0.8 ] - -mean value: 0.8733333333333334 - -key: train_jcc -value: [0.97777778 0.97777778 0.97777778 1. 1. 1. - 0.97826087 1. 1. 1. ] - -mean value: 0.991159420289855 - -key: TN -value: 47 - -mean value: 47.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01337504 0.01569223 0.01567912 0.01595926 0.01574159 0.01597762 - 0.01579881 0.01569128 0.01606631 0.01576662] - -mean value: 0.015574789047241211 - -key: score_time -value: [0.0116837 0.01153421 0.01165748 0.01159096 0.01157904 0.01181698 - 0.0116744 0.01153874 0.0116055 0.01170111] - -mean value: 0.011638212203979491 - -key: test_mcc -value: [ 0.6 0.40824829 1. 0.40824829 0.40824829 0. - 0.21821789 0.65465367 -0.21821789 0.40824829] - -mean value: 0.3887646832563429 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.66666667 1. 0.72727273 0.72727273 0.44444444 - 0.66666667 0.83333333 0.5 0.66666667] - -mean value: 0.7032323232323232 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.75 1. 0.66666667 0.66666667 0.5 - 0.57142857 0.71428571 0.42857143 0.75 ] - -mean value: 0.6847619047619048 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 0.6 1. 0.8 0.8 0.4 0.8 1. 0.6 0.6] - -mean value: 0.74 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7] - -mean value: 0.6900000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7] - -mean value: 0.6900000000000002 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.5 1. 0.57142857 0.57142857 0.28571429 - 0.5 0.71428571 0.33333333 0.5 ] - -mean value: 0.5642857142857143 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 32 - -mean value: 32.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 18 - -mean value: 18.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.15 - -Accuracy on Blind test: 0.6 - -Running classifier: 21 -Model_name: Gradient Boosting -Model func: GradientBoostingClassifier(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.22175002 0.20551085 0.21514559 0.19258976 0.19375014 0.21860838 - 0.19352555 0.22049546 0.18577838 0.19826937] - -mean value: 0.20454235076904298 - -key: score_time -value: [0.00891447 0.00950909 0.00972724 0.00901461 0.00917888 0.00884056 - 0.00981069 0.00889659 0.01069164 0.00902009] - -mean value: 0.009360384941101075 - -key: test_mcc -value: [1. 0.81649658 1. 0.40824829 0.81649658 0.6 - 0.81649658 0.81649658 0.40824829 0.65465367] - -mean value: 0.7337136575346608 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 1. 0.66666667 0.88888889 0.8 - 0.88888889 0.88888889 0.72727273 0.75 ] - -mean value: 0.8519696969696969 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 0.75 1. 0.8 - 1. 1. 0.66666667 1. ] - -mean value: 0.9049999999999999 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.6 0.8 0.8 0.8 0.8 0.8 0.6] - -mean value: 0.8200000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8] - -mean value: 0.86 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8] - -mean value: 0.86 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 1. 0.5 0.8 0.66666667 - 0.8 0.8 0.57142857 0.6 ] - -mean value: 0.7571428571428571 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01039934 0.0146544 0.01419163 0.01512098 0.01400018 0.01431561 - 0.01405144 0.01417041 0.01411915 0.01453304] - -mean value: 0.01395561695098877 - -key: score_time -value: [0.01178169 0.01186323 0.01167774 0.01193404 0.01269293 0.01163626 - 0.014534 0.01400065 0.01272821 0.01334691] - -mean value: 0.012619566917419434 - -key: test_mcc -value: [0.65465367 0.6 0.81649658 0.6 0.81649658 0.40824829 - 0.40824829 0.65465367 0.6 0.5 ] - -mean value: 0.6058797084199132 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.83333333 0.8 0.90909091 0.8 0.90909091 0.72727273 - 0.72727273 0.83333333 0.8 0.76923077] - -mean value: 0.8108624708624708 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.8 0.83333333 0.8 0.83333333 0.66666667 - 0.66666667 0.71428571 0.8 0.625 ] - -mean value: 0.7453571428571428 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 1. 0.8 1. 0.8 0.8 1. 0.8 1. ] - -mean value: 0.9 - -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 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7] - -mean value: 0.79 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.8 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7] - -mean value: 0.79 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.71428571 0.66666667 0.83333333 0.66666667 0.83333333 0.57142857 - 0.57142857 0.71428571 0.66666667 0.625 ] - -mean value: 0.6863095238095238 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.68 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.0339694 0.0281744 0.02761769 0.02563477 0.03163433 0.03103805 - 0.03008103 0.02972746 0.02814841 0.02832222] - -mean value: 0.029434776306152342 - -key: score_time -value: [0.01380706 0.02080441 0.02085018 0.02093291 0.02080822 0.02095509 - 0.02182198 0.02258253 0.02093005 0.020823 ] - -mean value: 0.02043154239654541 - -key: test_mcc -value: [0.81649658 0.81649658 1. 0.5 0.81649658 0.6 - 0.6 0.65465367 0.21821789 0.81649658] - -mean value: 0.6838857884654874 - -key: train_mcc -value: [1. 1. 0.97801929 1. 1. 1. - 0.97801929 1. 1. 1. ] - -mean value: 0.9956038587687303 - -key: test_fscore -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: 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 - smnc_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: 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 - smnc_CV['Resampling'] = rs_smnc -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: 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 - smnc_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: 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 - smnc_BT['Resampling'] = rs_smnc -[0.90909091 0.90909091 1. 0.76923077 0.90909091 0.8 - 0.8 0.83333333 0.66666667 0.88888889] - -mean value: 0.8485392385392385 - -key: train_fscore -value: [1. 1. 0.98876404 1. 1. 1. - 0.98876404 1. 1. 1. ] - -mean value: 0.997752808988764 - -key: test_precision -value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.8 - 0.8 0.71428571 0.57142857 1. ] - -mean value: 0.8010714285714287 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 0.97777778 1. 1. 1. - 0.97777778 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_accuracy -value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9] - -mean value: 0.8300000000000001 - -key: train_accuracy -value: [1. 1. 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_roc_auc -value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9] - -mean value: 0.8300000000000001 - -key: train_roc_auc -value: [1. 1. 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_jcc -value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.66666667 - 0.66666667 0.71428571 0.5 0.8 ] - -mean value: 0.7472619047619047 - -key: train_jcc -value: [1. 1. 0.97777778 1. 1. 1. - 0.97777778 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.16 - -Accuracy on Blind test: 0.62 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.1647613 0.21787906 0.20268655 0.18552685 0.1953547 0.18074489 - 0.18419719 0.18565655 0.18220353 0.20094514] - -mean value: 0.18999557495117186 - -key: score_time -value: [0.02277732 0.02263975 0.02119422 0.02119184 0.02137756 0.02144146 - 0.02110291 0.02121115 0.01738858 0.02114844] - -mean value: 0.021147322654724122 - -key: test_mcc -value: [0.81649658 0.65465367 0.81649658 0.5 1. 0.40824829 - 0.81649658 0.65465367 0.21821789 0.81649658] - -mean value: 0.6701759845826715 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.83333333 0.90909091 0.76923077 1. 0.72727273 - 0.90909091 0.83333333 0.66666667 0.88888889] - -mean value: 0.8445998445998445 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.66666667 - 0.83333333 0.71428571 0.57142857 1. ] - -mean value: 0.7791666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 1. 1. 0.8 0.8] - -mean value: 0.9400000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9] - -mean value: 0.8200000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9] - -mean value: 0.8200000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.57142857 - 0.83333333 0.71428571 0.5 0.8 ] - -mean value: 0.7424999999999999 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.05 - -Accuracy on Blind test: 0.57 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02255177 0.02571154 0.02445078 0.02460313 0.02436972 0.02525306 - 0.02503276 0.02916312 0.02374983 0.02672315] - -mean value: 0.025160884857177733 - -key: score_time -value: [0.01158285 0.01172376 0.01152539 0.01155019 0.01155806 0.01169276 - 0.0116291 0.01217937 0.01163387 0.01157951] - -mean value: 0.01166548728942871 - -key: test_mcc -value: [1. 0.6 1. 0.65465367 0.5 0.81649658 - 0.6 0.40824829 0. 0.81649658] - -mean value: 0.6395895123027293 - -key: train_mcc -value: [0.97801929 0.93356387 0.93356387 0.95650071 0.91201231 0.91111111 - 0.91201231 0.93356387 0.95555556 0.93541435] - -mean value: 0.936131725451825 - -key: test_fscore -value: [1. 0.8 1. 0.83333333 0.76923077 0.88888889 - 0.8 0.72727273 0.54545455 0.88888889] - -mean value: 0.8253069153069154 - -key: train_fscore -value: [0.98901099 0.96703297 0.96703297 0.97826087 0.95652174 0.95555556 - 0.95652174 0.96703297 0.97777778 0.96774194] - -mean value: 0.9682489506753182 - -key: test_precision -value: [1. 0.8 1. 0.71428571 0.625 1. - 0.8 0.66666667 0.5 1. ] - -mean value: 0.8105952380952381 - -key: train_precision -value: [0.97826087 0.95652174 0.95652174 0.95744681 0.93617021 0.95555556 - 0.93617021 0.95652174 0.97777778 0.9375 ] - -mean value: 0.9548446654332409 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8] - -mean value: 0.86 - -key: train_recall -value: [1. 0.97777778 0.97777778 1. 0.97777778 0.95555556 - 0.97777778 0.97777778 0.97777778 1. ] - -mean value: 0.9822222222222223 - -key: test_accuracy -value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9] - -mean value: 0.8099999999999999 - -key: train_accuracy -value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556 - 0.95555556 0.96666667 0.97777778 0.96666667] - -mean value: 0.9677777777777778 - -key: test_roc_auc -value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9] - -mean value: 0.8099999999999999 - -key: train_roc_auc -value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556 - 0.95555556 0.96666667 0.97777778 0.96666667] - -mean value: 0.9677777777777778 - -key: test_jcc -value: [1. 0.66666667 1. 0.71428571 0.625 0.8 - 0.66666667 0.57142857 0.375 0.8 ] - -mean value: 0.7219047619047619 - -key: train_jcc -value: [0.97826087 0.93617021 0.93617021 0.95744681 0.91666667 0.91489362 - 0.91666667 0.93617021 0.95652174 0.9375 ] - -mean value: 0.9386467005858773 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.72 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.54653025 0.52189708 0.73818922 0.56501412 0.52099514 0.58223414 - 0.6111896 0.52498031 0.51234984 0.58435845] - -mean value: 0.5707738161087036 - -key: score_time -value: [0.01446366 0.01307082 0.01306009 0.0130105 0.01183724 0.01319456 - 0.01186347 0.01306581 0.01307225 0.01450109] - -mean value: 0.013113951683044434 - -key: test_mcc -value: [1. 0.6 1. 0.5 0.65465367 0.81649658 - 0.81649658 0.40824829 0.5 0.81649658] - -mean value: 0.7112391703955019 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.97801929 1. 1. ] - -mean value: 0.9978019293843652 - -key: test_fscore -value: [1. 0.8 1. 0.76923077 0.83333333 0.88888889 - 0.88888889 0.72727273 0.76923077 0.88888889] - -mean value: 0.8565734265734266 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.98901099 1. 1. ] - -mean value: 0.9989010989010989 - -key: test_precision -value: [1. 0.8 1. 0.625 0.71428571 1. - 1. 0.66666667 0.625 1. ] - -mean value: 0.843095238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.97826087 1. 1. ] - -mean value: 0.9978260869565216 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 1. 0.8] - -mean value: 0.9 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.98888889 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9] - -mean value: 0.8400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98888889 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_jcc -value: [1. 0.66666667 1. 0.625 0.71428571 0.8 - 0.8 0.57142857 0.625 0.8 ] - -mean value: 0.7602380952380952 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.97826087 1. 1. ] - -mean value: 0.9978260869565216 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.68 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01176167 0.01163745 0.00861454 0.0102284 0.0082593 0.0084765 - 0.00813198 0.00795722 0.00810385 0.00791645] - -mean value: 0.009108734130859376 - -key: score_time -value: [0.01180577 0.00988865 0.00878239 0.00933552 0.00824714 0.00900102 - 0.00843334 0.00827384 0.00830865 0.00864124] - -mean value: 0.009071755409240722 - -key: test_mcc -value: [ 0. 0.40824829 0.33333333 0.21821789 0.33333333 -0.21821789 - 0.21821789 0. 0. 0. ] - -mean value: 0.1293132847366522 - -key: train_mcc -value: [0.41781451 0.57601843 0.47133199 0.70004007 0.50917508 0.43808583 - 0.57906602 0.52094589 0.55610507 0.51854497] - -mean value: 0.528712785434277 - -key: test_fscore -value: [0.54545455 0.72727273 0.71428571 0.66666667 0.71428571 0.5 - 0.66666667 0.61538462 0.61538462 0.61538462] - -mean value: 0.638078588078588 - -key: train_fscore -value: [0.73786408 0.80373832 0.76363636 0.85714286 0.77777778 0.74509804 - 0.79569892 0.78181818 0.7826087 0.78095238] - -mean value: 0.7826335616353517 - -key: test_precision -value: [0.5 0.66666667 0.55555556 0.57142857 0.55555556 0.42857143 - 0.57142857 0.5 0.5 0.5 ] - -mean value: 0.5349206349206349 - -key: train_precision -value: [0.65517241 0.69354839 0.64615385 0.79245283 0.66666667 0.66666667 - 0.77083333 0.66153846 0.76595745 0.68333333] - -mean value: 0.7002323385579375 - -key: test_recall -value: [0.6 0.8 1. 0.8 1. 0.6 0.8 0.8 0.8 0.8] - -mean value: 0.8 - -key: train_recall -value: [0.84444444 0.95555556 0.93333333 0.93333333 0.93333333 0.84444444 - 0.82222222 0.95555556 0.8 0.91111111] - -mean value: 0.8933333333333333 - -key: test_accuracy -value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5] - -mean value: 0.55 - -key: train_accuracy -value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111 - 0.78888889 0.73333333 0.77777778 0.74444444] - -mean value: 0.7511111111111111 - -key: test_roc_auc -value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5] - -mean value: 0.55 - -key: train_roc_auc -value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111 - 0.78888889 0.73333333 0.77777778 0.74444444] - -mean value: 0.7511111111111111 - -key: test_jcc -value: [0.375 0.57142857 0.55555556 0.5 0.55555556 0.33333333 - 0.5 0.44444444 0.44444444 0.44444444] - -mean value: 0.47242063492063496 - -key: train_jcc -value: [0.58461538 0.671875 0.61764706 0.75 0.63636364 0.59375 - 0.66071429 0.64179104 0.64285714 0.640625 ] - -mean value: 0.6440238553150099 - -key: TN -value: 15 - -mean value: 15.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 35 - -mean value: 35.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.39 - -Accuracy on Blind test: 0.65 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01204419 0.00879407 0.00843763 0.00823641 0.00820732 0.00811648 - 0.00903416 0.0081718 0.00817847 0.00820756] - -mean value: 0.008742809295654297 - -key: score_time -value: [0.01041579 0.00831723 0.0082469 0.00830102 0.00827026 0.00827599 - 0.00908613 0.00831842 0.00828457 0.00827765] - -mean value: 0.008579397201538086 - -key: test_mcc -value: [0.81649658 0. 0.81649658 0.40824829 0.40824829 0.5 - 0. 0. 0. 0.21821789] - -mean value: 0.31677076330191706 - -key: train_mcc -value: [0.64700558 0.62609903 0.51571581 0.57792049 0.44992127 0.60238451 - 0.60059347 0.60059347 0.69162666 0.55610507] - -mean value: 0.5867965370349755 - -key: test_fscore -value: [0.88888889 0.54545455 0.88888889 0.72727273 0.72727273 0.57142857 - 0.44444444 0.54545455 0.44444444 0.5 ] - -mean value: 0.6283549783549783 - -key: train_fscore -value: [0.81395349 0.82105263 0.77083333 0.78651685 0.69879518 0.79069767 - 0.79545455 0.80434783 0.8372093 0.7826087 ] - -mean value: 0.7901469531877712 - -key: test_precision -value: [1. 0.5 1. 0.66666667 0.66666667 1. - 0.5 0.5 0.5 0.66666667] - -mean value: 0.7 - -key: train_precision -value: [0.85365854 0.78 0.7254902 0.79545455 0.76315789 0.82926829 - 0.81395349 0.78723404 0.87804878 0.76595745] - -mean value: 0.7992223223759711 - -key: test_recall -value: [0.8 0.6 0.8 0.8 0.8 0.4 0.4 0.6 0.4 0.4] - -mean value: 0.6000000000000001 - -key: train_recall -value: [0.77777778 0.86666667 0.82222222 0.77777778 0.64444444 0.75555556 - 0.77777778 0.82222222 0.8 0.8 ] - -mean value: 0.7844444444444444 - -key: test_accuracy -value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6] - -mean value: 0.65 - -key: train_accuracy -value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8 - 0.8 0.8 0.84444444 0.77777778] - -mean value: 0.7922222222222222 - -key: test_roc_auc -value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6] - -mean value: 0.65 - -key: train_roc_auc -value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8 - 0.8 0.8 0.84444444 0.77777778] - -mean value: 0.7922222222222223 - -key: test_jcc -value: [0.8 0.375 0.8 0.57142857 0.57142857 0.4 - 0.28571429 0.375 0.28571429 0.33333333] - -mean value: 0.4797619047619047 - -key: train_jcc -value: [0.68627451 0.69642857 0.62711864 0.64814815 0.53703704 0.65384615 - 0.66037736 0.67272727 0.72 0.64285714] - -mean value: 0.6544814838406611 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 30 - -mean value: 30.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.06 - -Accuracy on Blind test: 0.5 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.0103817 0.00782585 0.00789905 0.0080235 0.00852895 0.00891089 - 0.00892091 0.00831962 0.00858092 0.00874972] - -mean value: 0.008614110946655273 - -key: score_time -value: [0.00999832 0.00927591 0.01990318 0.01393294 0.01388645 0.01472855 - 0.00999117 0.00985885 0.00980926 0.01003838] - -mean value: 0.012142300605773926 - -key: test_mcc -value: [ 0.40824829 0.21821789 0. 0.40824829 0.81649658 -0.2 - 0.21821789 0. 0. 0.40824829] - -mean value: 0.22776772327912997 - -key: train_mcc -value: [0.53452248 0.53990552 0.43808583 0.51314236 0.56980288 0.60971232 - 0.58137767 0.49897013 0.60540551 0.50418417] - -mean value: 0.5395108888868825 - -key: test_fscore -value: [0.72727273 0.5 0.61538462 0.72727273 0.90909091 0.4 - 0.66666667 0.61538462 0.61538462 0.72727273] - -mean value: 0.6503729603729604 - -key: train_fscore -value: [0.77419355 0.78350515 0.74509804 0.76595745 0.8 0.81632653 - 0.8 0.76767677 0.8125 0.77227723] - -mean value: 0.7837534715062253 - -key: test_precision -value: [0.66666667 0.66666667 0.5 0.66666667 0.83333333 0.4 - 0.57142857 0.5 0.5 0.66666667] - -mean value: 0.5971428571428572 - -key: train_precision -value: [0.75 0.73076923 0.66666667 0.73469388 0.72727273 0.75471698 - 0.76 0.7037037 0.76470588 0.69642857] - -mean value: 0.7288957640876937 - -key: test_recall -value: [0.8 0.4 0.8 0.8 1. 0.4 0.8 0.8 0.8 0.8] - -mean value: 0.74 - -key: train_recall -value: [0.8 0.84444444 0.84444444 0.8 0.88888889 0.88888889 - 0.84444444 0.84444444 0.86666667 0.86666667] - -mean value: 0.8488888888888889 - -key: test_accuracy -value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7] - -mean value: 0.6100000000000001 - -key: train_accuracy -value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8 - 0.78888889 0.74444444 0.8 0.74444444] - -mean value: 0.7655555555555555 - -key: test_roc_auc -value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7] - -mean value: 0.6100000000000001 - -key: train_roc_auc -value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8 - 0.78888889 0.74444444 0.8 0.74444444] - -mean value: 0.7655555555555555 - -key: test_jcc -value: [0.57142857 0.33333333 0.44444444 0.57142857 0.83333333 0.25 - 0.5 0.44444444 0.44444444 0.57142857] - -mean value: 0.49642857142857133 - -key: train_jcc -value: [0.63157895 0.6440678 0.59375 0.62068966 0.66666667 0.68965517 - 0.66666667 0.62295082 0.68421053 0.62903226] - -mean value: 0.6449268508950567 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.07 - -Accuracy on Blind test: 0.48 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00865793 0.00888205 0.00870848 0.00853825 0.0088017 0.00992823 - 0.00926638 0.00858331 0.00977707 0.00882268] - -mean value: 0.00899660587310791 - -key: score_time -value: [0.00861406 0.00844789 0.00833035 0.008533 0.00839972 0.00914431 - 0.00835848 0.00843406 0.00843239 0.00836515] - -mean value: 0.008505940437316895 - -key: test_mcc -value: [0.65465367 0.40824829 0.6 0.6 0.81649658 0.21821789 - 0.40824829 0.21821789 0. 0.65465367] - -mean value: 0.4578736283743391 - -key: train_mcc -value: [0.76486616 0.75574218 0.77777778 0.76026311 0.73405869 0.78478493 - 0.77777778 0.84632727 0.80498447 0.78086881] - -mean value: 0.7787451171317092 - -key: test_fscore -value: [0.75 0.66666667 0.8 0.8 0.90909091 0.5 - 0.72727273 0.66666667 0.54545455 0.75 ] - -mean value: 0.7115151515151515 - -key: train_fscore -value: [0.86746988 0.87640449 0.88888889 0.87058824 0.86363636 0.88095238 - 0.88888889 0.91954023 0.89411765 0.88372093] - -mean value: 0.8834207938737174 - -key: test_precision -value: [1. 0.75 0.8 0.8 0.83333333 0.66666667 - 0.66666667 0.57142857 0.5 1. ] - -mean value: 0.7588095238095238 - -key: train_precision -value: [0.94736842 0.88636364 0.88888889 0.925 0.88372093 0.94871795 - 0.88888889 0.95238095 0.95 0.92682927] - -mean value: 0.9198158934818188 - -key: test_recall -value: [0.6 0.6 0.8 0.8 1. 0.4 0.8 0.8 0.6 0.6] - -mean value: 0.7 - -key: train_recall -value: [0.8 0.86666667 0.88888889 0.82222222 0.84444444 0.82222222 - 0.88888889 0.88888889 0.84444444 0.84444444] - -mean value: 0.8511111111111112 - -key: test_accuracy -value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8] - -mean value: 0.72 - -key: train_accuracy -value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889 - 0.88888889 0.92222222 0.9 0.88888889] - -mean value: 0.8877777777777778 - -key: test_roc_auc -value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8] - -mean value: 0.72 - -key: train_roc_auc -value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889 - 0.88888889 0.92222222 0.9 0.88888889] - -mean value: 0.8877777777777778 - -key: test_jcc -value: [0.6 0.5 0.66666667 0.66666667 0.83333333 0.33333333 - 0.57142857 0.5 0.375 0.6 ] - -mean value: 0.5646428571428571 - -key: train_jcc -value: [0.76595745 0.78 0.8 0.77083333 0.76 0.78723404 - 0.8 0.85106383 0.80851064 0.79166667] - -mean value: 0.791526595744681 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.04 - -Accuracy on Blind test: 0.62 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.35339808 0.34032393 0.34420347 0.40963769 0.37293458 0.36176634 - 0.34549356 0.38289881 0.32440186 0.49558091] - -mean value: 0.37306392192840576 - -key: score_time -value: [0.01189828 0.01193237 0.01191092 0.012012 0.01190543 0.01191354 - 0.0119226 0.01200247 0.01195073 0.01197505] - -mean value: 0.011942338943481446 - -key: test_mcc -value: [1. 0.6 1. 0.5 0.65465367 0.40824829 - 0.40824829 0.5 0.65465367 0.81649658] - -mean value: 0.6542300503271407 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.8 1. 0.76923077 0.83333333 0.66666667 - 0.72727273 0.76923077 0.83333333 0.88888889] - -mean value: 0.8287956487956487 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.8 1. 0.625 0.71428571 0.75 - 0.66666667 0.625 0.71428571 1. ] - -mean value: 0.7895238095238095 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.6 0.8 1. 1. 0.8] - -mean value: 0.9 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9] - -mean value: 0.8099999999999999 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9] - -mean value: 0.8099999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.66666667 1. 0.625 0.71428571 0.5 - 0.57142857 0.625 0.71428571 0.8 ] - -mean value: 0.7216666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 36 - -mean value: 36.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.6 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01333547 0.01328611 0.0099473 0.00956702 0.0093472 0.00933528 - 0.00904536 0.00918818 0.00934958 0.00936794] - -mean value: 0.010176944732666015 - -key: score_time -value: [0.01137042 0.01056337 0.00862908 0.00830317 0.00825858 0.00827336 - 0.00822067 0.00820446 0.00816536 0.00829554] - -mean value: 0.008828401565551758 - -key: test_mcc -value: [0.81649658 0.81649658 1. 0.65465367 0.65465367 0.6 - 0.81649658 0.40824829 0.81649658 0.81649658] - -mean value: 0.7400038536518447 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.90909091 1. 0.83333333 0.83333333 0.8 - 0.88888889 0.72727273 0.90909091 0.88888889] - -mean value: 0.8698989898989898 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.8 - 1. 0.66666667 0.83333333 1. ] - -mean value: 0.8395238095238096 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9] - -mean value: 0.8600000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9] - -mean value: 0.8600000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.66666667 - 0.8 0.57142857 0.83333333 0.8 ] - -mean value: 0.7766666666666666 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.08124042 0.07976699 0.08055592 0.08180213 0.0852704 0.08435702 - 0.0828104 0.08187795 0.08418012 0.08399606] - -mean value: 0.0825857400894165 - -key: score_time -value: [0.01652694 0.01657915 0.01681757 0.01673293 0.0175488 0.01702619 - 0.0173912 0.01703215 0.01830769 0.01708388] - -mean value: 0.01710464954376221 - -key: test_mcc -value: [1. 0.40824829 0.81649658 0.65465367 0.81649658 0.5 - 0.40824829 0.40824829 0.65465367 0.65465367] - -mean value: 0.6321699045370972 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.66666667 0.88888889 0.83333333 0.90909091 0.57142857 - 0.72727273 0.72727273 0.83333333 0.75 ] - -mean value: 0.7907287157287157 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.75 1. 0.71428571 0.83333333 1. - 0.66666667 0.66666667 0.71428571 1. ] - -mean value: 0.8345238095238094 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.6 0.8 1. 1. 0.4 0.8 0.8 1. 0.6] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8] - -mean value: 0.8 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8] - -mean value: 0.8 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.5 0.8 0.71428571 0.83333333 0.4 - 0.57142857 0.57142857 0.71428571 0.6 ] - -mean value: 0.6704761904761904 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.78 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.0081377 0.00820899 0.00818825 0.00807595 0.00807285 0.00856829 - 0.00839448 0.00841856 0.00827169 0.00830626] - -mean value: 0.008264303207397461 - -key: score_time -value: [0.00828624 0.00839925 0.00824952 0.00820351 0.00828624 0.00887203 - 0.00863862 0.00841212 0.00832582 0.00837159] - -mean value: 0.00840449333190918 - -key: test_mcc -value: [ 1. 0.40824829 0.81649658 0.65465367 0.81649658 -0.5 - 0.40824829 0.21821789 0.81649658 0.65465367] - -mean value: 0.5293511555362851 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.72727273 0.88888889 0.83333333 0.90909091 0.46153846 - 0.72727273 0.66666667 0.90909091 0.75 ] - -mean value: 0.7873154623154623 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.66666667 1. 0.71428571 0.83333333 0.375 - 0.66666667 0.57142857 0.83333333 1. ] - -mean value: 0.7660714285714285 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 0.8 1. 1. 0.6 0.8 0.8 1. 0.6] - -mean value: 0.8400000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8] - -mean value: 0.76 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8] - -mean value: 0.76 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.57142857 0.8 0.71428571 0.83333333 0.3 - 0.57142857 0.5 0.83333333 0.6 ] - -mean value: 0.6723809523809523 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.56 - -Accuracy on Blind test: 0.8 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.0857687 1.02543187 1.06359982 1.03614068 1.06371474 1.02588892 - 1.02457833 1.05441475 1.06075287 1.04914474] - -mean value: 1.048943543434143 - -key: score_time -value: [0.09435582 0.09166527 0.087533 0.09539175 0.08773398 0.0948875 - 0.094208 0.09529018 0.09117222 0.09602284] - -mean value: 0.09282605648040772 - -key: test_mcc -value: [1. 0.81649658 1. 0.81649658 1. 0.65465367 - 0.81649658 0.40824829 0.65465367 0.65465367] - -mean value: 0.7821699045370971 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.88888889 1. 0.90909091 1. 0.75 - 0.88888889 0.72727273 0.83333333 0.75 ] - -mean value: 0.8747474747474746 - -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. 0.83333333 1. 1. - 1. 0.66666667 0.71428571 1. ] - -mean value: 0.9214285714285714 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.6 0.8 0.8 1. 0.6] - -mean value: 0.86 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8] - -mean value: 0.8800000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8] - -mean value: 0.8800000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.8 1. 0.83333333 1. 0.6 - 0.8 0.57142857 0.71428571 0.6 ] - -mean value: 0.7919047619047619 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.55 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -Accuracy on Blind test: 0.8 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.89968204 0.85457993 0.8354466 0.8666656 0.85017776 0.82677722 - 0.88512731 0.85213256 0.84619737 0.91214228] - -mean value: 0.8628928661346436 - -key: score_time -value: [0.13997507 0.14476967 0.14723778 0.16803837 0.16983438 0.17666507 - 0.17594314 0.1609211 0.17945981 0.17022514] - -mean value: 0.16330695152282715 - -key: test_mcc -value: [0.81649658 0.81649658 0.81649658 0.81649658 1. 0.81649658 - 0.6 0.40824829 0.81649658 0.65465367] - -mean value: 0.7561881446738197 - -key: train_mcc -value: [0.95555556 0.95555556 0.93356387 0.95555556 0.95650071 0.95650071 - 0.95650071 0.93356387 1. 0.97801929] - -mean value: 0.9581315847088575 - -key: test_fscore -value: [0.90909091 0.88888889 0.88888889 0.90909091 1. 0.88888889 - 0.8 0.72727273 0.90909091 0.75 ] - -mean value: 0.867121212121212 - -key: train_fscore -value: [0.97777778 0.97777778 0.96629213 0.97777778 0.97826087 0.97727273 - 0.97826087 0.96629213 1. 0.98876404] - -mean value: 0.9788476114343239 - -key: test_precision -value: [0.83333333 1. 1. 0.83333333 1. 1. - 0.8 0.66666667 0.83333333 1. ] - -mean value: 0.8966666666666667 - -key: train_precision -value: [0.97777778 0.97777778 0.97727273 0.97777778 0.95744681 1. - 0.95744681 0.97727273 1. 1. ] - -mean value: 0.9802772404900064 - -key: test_recall -value: [1. 0.8 0.8 1. 1. 0.8 0.8 0.8 1. 0.6] - -mean value: 0.86 - -key: train_recall -value: [0.97777778 0.97777778 0.95555556 0.97777778 1. 0.95555556 - 1. 0.95555556 1. 0.97777778] - -mean value: 0.9777777777777779 - -key: test_accuracy -value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8] - -mean value: 0.8700000000000001 - -key: train_accuracy -value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778 - 0.97777778 0.96666667 1. 0.98888889] - -mean value: 0.9788888888888889 - -key: test_roc_auc -value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8] - -mean value: 0.8700000000000001 - -key: train_roc_auc -value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778 - 0.97777778 0.96666667 1. 0.98888889] - -mean value: 0.9788888888888889 - -key: test_jcc -value: [0.83333333 0.8 0.8 0.83333333 1. 0.8 - 0.66666667 0.57142857 0.83333333 0.6 ] - -mean value: 0.7738095238095237 - -key: train_jcc -value: [0.95652174 0.95652174 0.93478261 0.95652174 0.95744681 0.95555556 - 0.95744681 0.93478261 1. 0.97777778] - -mean value: 0.9587357385137218 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.61 - -Accuracy on Blind test: 0.82 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.04202104 0.08573484 0.03951287 0.03615594 0.03516817 0.03643227 - 0.03565812 0.03513455 0.03435874 0.03614831] - -mean value: 0.04163248538970947 - -key: score_time -value: [0.01022768 0.01082444 0.01064587 0.01083302 0.0103128 0.01038003 - 0.01106954 0.01054955 0.01016593 0.01010156] - -mean value: 0.010511040687561035 - -key: test_mcc -value: [1. 0.81649658 1. 1. 1. 0.6 - 0.81649658 0.6 0.5 1. ] - -mean value: 0.8332993161855452 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 1. 1. 1. 0.8 - 0.88888889 0.8 0.76923077 1. ] - -mean value: 0.9167210567210569 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 1. 1. 0.8 - 1. 0.8 0.625 1. ] - -mean value: 0.9058333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 1. ] - -mean value: 0.9400000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ] - -mean value: 0.9099999999999999 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ] - -mean value: 0.9099999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 1. 1. 1. 0.66666667 - 0.8 0.66666667 0.625 1. ] - -mean value: 0.8591666666666666 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02282596 0.03948879 0.03882122 0.04212666 0.0416882 0.04012561 - 0.04158378 0.0415206 0.04133034 0.04192233] - -mean value: 0.03914334774017334 - -key: score_time -value: [0.02050471 0.02193856 0.02390456 0.02119875 0.02115989 0.02110076 - 0.01587558 0.02117825 0.02245665 0.020365 ] - -mean value: 0.02096827030181885 - -key: test_mcc -value: [0.81649658 0.21821789 1. 0.65465367 0.81649658 0. - 0.6 0.65465367 0.5 0.81649658] - -mean value: 0.6077014974435124 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.66666667 1. 0.83333333 0.90909091 0.61538462 - 0.8 0.83333333 0.76923077 0.88888889] - -mean value: 0.8225019425019425 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.57142857 1. 0.71428571 0.83333333 0.5 - 0.8 0.71428571 0.625 1. ] - -mean value: 0.7591666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.8 0.8 1. 1. 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9] - -mean value: 0.79 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9] - -mean value: 0.79 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.5 1. 0.71428571 0.83333333 0.44444444 - 0.66666667 0.71428571 0.625 0.8 ] - -mean value: 0.7131349206349207 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.5 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02296448 0.0084753 0.00825715 0.00825763 0.00829768 0.00805593 - 0.00811934 0.00863338 0.00841451 0.00900841] - -mean value: 0.009848380088806152 - -key: score_time -value: [0.01073384 0.00855923 0.00831366 0.00836492 0.00844288 0.00820661 - 0.00821209 0.00819874 0.00902271 0.00844789] - -mean value: 0.008650255203247071 - -key: test_mcc -value: [ 0. 0.40824829 0.6 0.2 0.40824829 0. - -0.2 0.2 -0.2 0.21821789] - -mean value: 0.16347144711637185 - -key: train_mcc -value: [0.4000988 0.38118125 0.40492914 0.4949134 0.40249224 0.48900965 - 0.44992127 0.40249224 0.33366304 0.40249224] - -mean value: 0.41611932587025435 - -key: test_fscore -value: [0.54545455 0.66666667 0.8 0.6 0.66666667 0.54545455 - 0.4 0.6 0.4 0.5 ] - -mean value: 0.5724242424242425 - -key: train_fscore -value: [0.69662921 0.66666667 0.6746988 0.72289157 0.68235294 0.74725275 - 0.69879518 0.68235294 0.65909091 0.68235294] - -mean value: 0.6913083902191556 - -key: test_precision -value: [0.5 0.75 0.8 0.6 0.75 0.5 - 0.4 0.6 0.4 0.66666667] - -mean value: 0.5966666666666668 - -key: train_precision -value: [0.70454545 0.71794872 0.73684211 0.78947368 0.725 0.73913043 - 0.76315789 0.725 0.6744186 0.725 ] - -mean value: 0.730051689613847 - -key: test_recall -value: [0.6 0.6 0.8 0.6 0.6 0.6 0.4 0.6 0.4 0.4] - -mean value: 0.56 - -key: train_recall -value: [0.68888889 0.62222222 0.62222222 0.66666667 0.64444444 0.75555556 - 0.64444444 0.64444444 0.64444444 0.64444444] - -mean value: 0.6577777777777778 - -key: test_accuracy -value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6] - -mean value: 0.58 - -key: train_accuracy -value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444 - 0.72222222 0.7 0.66666667 0.7 ] - -mean value: 0.7066666666666667 - -key: test_roc_auc -value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6] - -mean value: 0.5800000000000001 - -key: train_roc_auc -value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444 - 0.72222222 0.7 0.66666667 0.7 ] - -mean value: 0.7066666666666667 - -key: test_jcc -value: [0.375 0.5 0.66666667 0.42857143 0.5 0.375 - 0.25 0.42857143 0.25 0.33333333] - -mean value: 0.4107142857142857 - -key: train_jcc -value: [0.53448276 0.5 0.50909091 0.56603774 0.51785714 0.59649123 - 0.53703704 0.51785714 0.49152542 0.51785714] - -mean value: 0.528823652096811 - -key: TN -value: 30 - -mean value: 30.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.14 - -Accuracy on Blind test: 0.48 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01120234 0.01247239 0.01248765 0.01223207 0.01297379 0.01218271 - 0.01372743 0.01458359 0.01382494 0.01353407] - -mean value: 0.012922096252441406 - -key: score_time -value: [0.00857997 0.01098967 0.01123166 0.01120615 0.01122999 0.01121783 - 0.01131988 0.01128626 0.01141262 0.01127839] - -mean value: 0.010975241661071777 - -key: test_mcc -value: [1. 0.81649658 0.65465367 0.40824829 0.33333333 0.81649658 - 0.65465367 0.5 0.2 0.65465367] - -mean value: 0.6038535797776581 - -key: train_mcc -value: [0.97801929 0.83553169 0.77919372 0.89087081 0.89442719 0.93541435 - 0.95650071 0.95650071 0.97801929 0.77919372] - -mean value: 0.8983671497421686 - -key: test_fscore -value: [1. 0.88888889 0.83333333 0.72727273 0.71428571 0.90909091 - 0.83333333 0.76923077 0.6 0.75 ] - -mean value: 0.8025435675435675 - -key: train_fscore -value: [0.98901099 0.90243902 0.89108911 0.94623656 0.94736842 0.96774194 - 0.97826087 0.97826087 0.98876404 0.86075949] - -mean value: 0.9449931315733553 - -key: test_precision -value: [1. 1. 0.71428571 0.66666667 0.55555556 0.83333333 - 0.71428571 0.625 0.6 1. ] - -mean value: 0.7709126984126984 - -key: train_precision -value: [0.97826087 1. 0.80357143 0.91666667 0.9 0.9375 - 0.95744681 0.95744681 1. 1. ] - -mean value: 0.945089258182459 - -key: test_recall -value: [1. 0.8 1. 0.8 1. 1. 1. 1. 0.6 0.6] - -mean value: 0.8799999999999999 - -key: train_recall -value: [1. 0.82222222 1. 0.97777778 1. 1. - 1. 1. 0.97777778 0.75555556] - -mean value: 0.9533333333333331 - -key: test_accuracy -value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8] - -mean value: 0.78 - -key: train_accuracy -value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667 - 0.97777778 0.97777778 0.98888889 0.87777778] - -mean value: 0.9455555555555556 - -key: test_roc_auc -value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8] - -mean value: 0.78 - -key: train_roc_auc -value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667 - 0.97777778 0.97777778 0.98888889 0.87777778] - -mean value: 0.9455555555555556 - -key: test_jcc -value: [1. 0.8 0.71428571 0.57142857 0.55555556 0.83333333 - 0.71428571 0.625 0.42857143 0.6 ] - -mean value: 0.6842460317460317 - -key: train_jcc -value: [0.97826087 0.82222222 0.80357143 0.89795918 0.9 0.9375 - 0.95744681 0.95744681 0.97777778 0.75555556] - -mean value: 0.8987740654386946 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.27 - -Accuracy on Blind test: 0.68 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00876498 0.01228714 0.01203871 0.01212764 0.01211023 0.01215172 - 0.01181269 0.01210976 0.01228261 0.01225233] - -mean value: 0.011793780326843261 - -key: score_time -value: [0.00829244 0.01176238 0.01127028 0.01124716 0.01124883 0.01142573 - 0.01126313 0.01129913 0.01135111 0.01130509] - -mean value: 0.011046528816223145 - -key: test_mcc -value: [0.81649658 0.33333333 0.81649658 0.65465367 0.33333333 0.65465367 - 0.33333333 0.21821789 0.5 0.6 ] - -mean value: 0.5260518393507398 - -key: train_mcc -value: [0.91111111 0.42919754 0.82548988 0.95555556 0.93541435 0.91473203 - 0.4108907 0.83553169 1. 1. ] - -mean value: 0.8217922862521331 - -key: test_fscore -value: [0.88888889 0.71428571 0.88888889 0.83333333 0.71428571 0.83333333 - 0.33333333 0.66666667 0.76923077 0.8 ] - -mean value: 0.7442246642246643 - -key: train_fscore -value: [0.95555556 0.74380165 0.90697674 0.97777778 0.96774194 0.95744681 - 0.44827586 0.91836735 1. 1. ] - -mean value: 0.8875943683414192 - -key: test_precision -value: [1. 0.55555556 1. 0.71428571 0.55555556 0.71428571 - 1. 0.57142857 0.625 0.8 ] - -mean value: 0.753611111111111 - -key: train_precision -value: [0.95555556 0.59210526 0.95121951 0.97777778 0.9375 0.91836735 - 1. 0.8490566 1. 1. ] - -mean value: 0.9181582059398711 - -key: test_recall -value: [0.8 1. 0.8 1. 1. 1. 0.2 0.8 1. 0.8] - -mean value: 0.8400000000000001 - -key: train_recall -value: [0.95555556 1. 0.86666667 0.97777778 1. 1. - 0.28888889 1. 1. 1. ] - -mean value: 0.9088888888888889 - -key: test_accuracy -value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8] - -mean value: 0.73 - -key: train_accuracy -value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556 - 0.64444444 0.91111111 1. 1. ] - -mean value: 0.8977777777777778 - -key: test_roc_auc -value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8] - -mean value: 0.7300000000000001 - -key: train_roc_auc -value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556 - 0.64444444 0.91111111 1. 1. ] - -mean value: 0.8977777777777778 - -key: test_jcc -value: [0.8 0.55555556 0.8 0.71428571 0.55555556 0.71428571 - 0.2 0.5 0.625 0.66666667] - -mean value: 0.6131349206349206 - -key: train_jcc -value: [0.91489362 0.59210526 0.82978723 0.95652174 0.9375 0.91836735 - 0.28888889 0.8490566 1. 1. ] - -mean value: 0.8287120692953408 - -key: TN -value: 31 - -mean value: 31.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.51 - -Accuracy on Blind test: 0.78 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.07988095 0.07883573 0.07912493 0.07934546 0.07975411 0.07876873 - 0.07892227 0.0793283 0.07942939 0.07979774] - -mean value: 0.07931876182556152 - -key: score_time -value: [0.01414919 0.01410151 0.01419544 0.01427197 0.01422954 0.01440668 - 0.01421189 0.01422381 0.01437879 0.01422191] - -mean value: 0.014239072799682617 - -key: test_mcc -value: [0.65465367 0.81649658 1. 0.81649658 1. 0.81649658 - 0.81649658 0.65465367 0.65465367 1. ] - -mean value: 0.8229947335834836 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.83333333 0.90909091 1. 0.90909091 1. 0.88888889 - 0.88888889 0.83333333 0.83333333 1. ] - -mean value: 0.9095959595959597 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.83333333 1. 0.83333333 1. 1. - 1. 0.71428571 0.71428571 1. ] - -mean value: 0.880952380952381 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] - -mean value: 0.96 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ] - -mean value: 0.9 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ] - -mean value: 0.9 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.71428571 0.83333333 1. 0.83333333 1. 0.8 - 0.8 0.71428571 0.71428571 1. ] - -mean value: 0.840952380952381 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02679038 0.02585912 0.02684498 0.03183603 0.04586864 0.03085876 - 0.03211308 0.03402162 0.03574443 0.03347778] - -mean value: 0.03234148025512695 - -key: score_time -value: [0.02189565 0.01761818 0.02150726 0.0278151 0.02133465 0.02162409 - 0.02341056 0.02291417 0.03275824 0.01854229] - -mean value: 0.022942018508911134 - -key: test_mcc -value: [0.81649658 0.81649658 1. 1. 1. 0.6 - 0.81649658 0.65465367 0.81649658 1. ] - -mean value: 0.8520639994418883 - -key: train_mcc -value: [0.97801929 1. 0.97801929 1. 1. 1. - 0.97801929 1. 1. 1. ] - -mean value: 0.9934057881530954 - -key: test_fscore -value: [0.88888889 0.90909091 1. 1. 1. 0.8 - 0.88888889 0.83333333 0.90909091 1. ] - -mean value: 0.9229292929292929 - -key: train_fscore -value: [0.98876404 1. 0.98876404 1. 1. 1. - 0.98901099 1. 1. 1. ] - -mean value: 0.9966539078898629 - -key: test_precision -value: [1. 0.83333333 1. 1. 1. 0.8 - 1. 0.71428571 0.83333333 1. ] - -mean value: 0.9180952380952382 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.97826087 1. 1. 1. ] - -mean value: 0.9978260869565216 - -key: test_recall -value: [0.8 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] - -mean value: 0.9400000000000001 - -key: train_recall -value: [0.97777778 1. 0.97777778 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_accuracy -value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ] - -mean value: 0.9199999999999999 - -key: train_accuracy -value: [0.98888889 1. 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9966666666666667 - -key: test_roc_auc -value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ] - -mean value: 0.9199999999999999 - -key: train_roc_auc -value: [0.98888889 1. 0.98888889 1. 1. 1. - 0.98888889 1. 1. 1. ] - -mean value: 0.9966666666666667 - -key: test_jcc -value: [0.8 0.83333333 1. 1. 1. 0.66666667 - 0.8 0.71428571 0.83333333 1. ] - -mean value: 0.8647619047619047 - -key: train_jcc -value: [0.97777778 1. 0.97777778 1. 1. 1. - 0.97826087 1. 1. 1. ] - -mean value: 0.9933816425120773 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 20 -Model_name: Gaussian Process -Model func: GaussianProcessClassifier(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01471233 0.01499677 0.01495695 0.01577234 0.01556015 0.01557922 - 0.01554871 0.01555872 0.01553726 0.01553893] - -mean value: 0.015376138687133788 - -key: score_time -value: [0.01128864 0.01108527 0.0116055 0.01157355 0.01152849 0.01179886 - 0.01158834 0.01157236 0.0115447 0.01676178] - -mean value: 0.012034749984741211 - -key: test_mcc -value: [0.81649658 0.40824829 1. 0.65465367 0.65465367 0.21821789 - 0.21821789 0.65465367 0.5 0.65465367] - -mean value: 0.5779795334695482 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.66666667 1. 0.83333333 0.83333333 0.5 - 0.66666667 0.83333333 0.76923077 0.75 ] - -mean value: 0.7761655011655011 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.75 1. 0.71428571 0.71428571 0.66666667 - 0.57142857 0.71428571 0.625 1. ] - -mean value: 0.7589285714285714 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.6 1. 1. 1. 0.4 0.8 1. 1. 0.6] - -mean value: 0.8400000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8] - -mean value: 0.77 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8] - -mean value: 0.77 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.5 1. 0.71428571 0.71428571 0.33333333 - 0.5 0.71428571 0.625 0.6 ] - -mean value: 0.653452380952381 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.52 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.21461725 0.18806386 0.1732049 0.16499853 0.17371368 0.16821599 - 0.14020443 0.19140482 0.18640518 0.16943955] - -mean value: 0.1770268201828003 - -key: score_time -value: [0.00959754 0.00923371 0.00887346 0.00884986 0.00876808 0.00874138 - 0.00899005 0.00879264 0.00887346 0.00937819] - -mean value: 0.009009838104248047 - -key: test_mcc -value: [1. 0.81649658 1. 0.81649658 1. 0.81649658 - 0.81649658 1. 0.65465367 1. ] - -mean value: 0.8920639994418881 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 1. 0.90909091 1. 0.88888889 - 0.88888889 1. 0.83333333 1. ] - -mean value: 0.942929292929293 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 0.83333333 1. 1. - 1. 1. 0.71428571 1. ] - -mean value: 0.9380952380952381 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] - -mean value: 0.96 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ] - -mean value: 0.9400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ] - -mean value: 0.9400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 1. 0.83333333 1. 0.8 - 0.8 1. 0.71428571 1. ] - -mean value: 0.8980952380952381 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 46 - -mean value: 46.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01019168 0.01433969 0.01385474 0.01470232 0.0138483 0.01425314 - 0.01388431 0.01397109 0.0143342 0.01450634] - -mean value: 0.013788580894470215 - -key: score_time -value: [0.01203132 0.01153278 0.0118506 0.01162958 0.01282692 0.01163149 - 0.01344275 0.01311135 0.01172233 0.01355076] - -mean value: 0.012332987785339356 - -key: test_mcc -value: [1. 0.65465367 0.65465367 1. 1. 0.5 - 0.65465367 0.65465367 1. 0.65465367] - -mean value: 0.7773268353539886 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -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/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. 0.75 0.75 1. 1. 0.57142857 - 0.75 0.75 1. 0.75 ] - -mean value: 0.8321428571428571 - -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. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6] - -mean value: 0.74 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8] - -mean value: 0.8700000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8] - -mean value: 0.8700000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6] - -mean value: 0.74 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 50 - -mean value: 50.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 0 - -mean value: 0.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.65 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02094769 0.01264119 0.01544523 0.02882671 0.03682876 0.02940774 - 0.0313282 0.03155279 0.05608463 0.03202128] - -mean value: 0.029508423805236817 - -key: score_time -value: [0.01188612 0.01171374 0.0116632 0.01729679 0.01832533 0.02263856 - 0.02219057 0.0126164 0.01564741 0.01932001] - -mean value: 0.01632981300354004 - -key: test_mcc -value: [0.81649658 0.6 1. 0.5 0.65465367 0.6 - 0.6 0.21821789 0. 0.81649658] - -mean value: 0.5805864722799422 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.97801929 0.97801929 1. ] - -mean value: 0.9956038587687303 - -key: test_fscore -value: [0.90909091 0.8 1. 0.76923077 0.83333333 0.8 - 0.8 0.66666667 0.54545455 0.88888889] - -mean value: 0.8012665112665113 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.98901099 0.98876404 1. ] - -mean value: 0.997777503395481 - -key: test_precision -value: [0.83333333 0.8 1. 0.625 0.71428571 0.8 - 0.8 0.57142857 0.5 1. ] - -mean value: 0.7644047619047619 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.97826087 1. 1. ] - -mean value: 0.9978260869565216 - -key: test_recall -value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8] - -mean value: 0.86 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.97777778 1. ] - -mean value: 0.9977777777777778 - -key: test_accuracy -value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9] - -mean value: 0.78 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.98888889 0.98888889 1. ] - -mean value: 0.9977777777777778 - -key: test_roc_auc -value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9] - -mean value: 0.78 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98888889 0.98888889 1. ] - -mean value: 0.9977777777777778 - -key: test_jcc -value: [0.83333333 0.66666667 1. 0.625 0.71428571 0.66666667 - 0.66666667 0.5 0.375 0.8 ] - -mean value: 0.6847619047619047 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.97826087 0.97777778 1. ] - -mean value: 0.9956038647342995 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.16 - -Accuracy on Blind test: 0.62 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.09823203 0.18534827 0.08709049 0.1154747 0.18197918 0.18414021 - 0.16416526 0.08809209 0.2873745 0.2193656 ] - -mean value: 0.1611262321472168 - -key: score_time -value: [0.02172661 0.01154947 0.01647282 0.02121782 0.02233553 0.02070236 - 0.01157141 0.01160717 0.02128386 0.01674223] - -mean value: 0.017520928382873537 - -key: test_mcc -value: [0.81649658 0.65465367 0.81649658 0.5 0.81649658 0.21821789 - 0.6 0.40824829 0. 0.65465367] - -mean value: 0.5485263264898987 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.86666667 0.97801929 1. ] - -mean value: 0.9844685960510319 - -key: test_fscore -value: [0.90909091 0.83333333 0.90909091 0.76923077 0.90909091 0.66666667 - 0.8 0.72727273 0.54545455 0.75 ] - -mean value: 0.7819230769230769 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.93333333 0.98876404 1. ] - -mean value: 0.9922097378277155 - -key: test_precision -value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.57142857 - 0.8 0.66666667 0.5 1. ] - -mean value: 0.7377380952380953 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.93333333 1. 1. ] - -mean value: 0.9933333333333334 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 0.6 0.6] - -mean value: 0.86 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 0.93333333 0.97777778 1. ] - -mean value: 0.9911111111111112 - -key: test_accuracy -value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8] - -mean value: 0.76 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.93333333 0.98888889 1. ] -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: 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 - ros_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: 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 - ros_CV['Resampling'] = rs_ros -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: 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 - ros_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: 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 - ros_BT['Resampling'] = rs_ros - -mean value: 0.9922222222222222 - -key: test_roc_auc -value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8] - -mean value: 0.76 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.93333333 0.98888889 1. ] - -mean value: 0.9922222222222222 - -key: test_jcc -value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.5 - 0.66666667 0.57142857 0.375 0.6 ] - -mean value: 0.6552380952380952 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.875 0.97777778 1. ] - -mean value: 0.9852777777777778 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.52 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02247024 0.02279019 0.0214591 0.02027655 0.02126312 0.02295542 - 0.02090645 0.02397966 0.02241564 0.02300167] - -mean value: 0.022151803970336913 - -key: score_time -value: [0.0113349 0.01142359 0.01140141 0.01136398 0.01136851 0.01148033 - 0.01136971 0.01144123 0.01137614 0.01134181] - -mean value: 0.011390161514282227 - -key: test_mcc -value: [0.4472136 1. 0. 0. 0. 1. - 0. 0. 0.40824829 1. ] - -mean value: 0.3855461885963821 - -key: train_mcc -value: [0.88527041 0.96225045 0.92307692 1. 0.92307692 0.88527041 - 0.96225045 0.92307692 0.92450142 0.96291111] - -mean value: 0.9351685024695351 - -key: test_fscore -value: [0.5 1. 0.66666667 0.4 0.57142857 1. - 0.4 0.57142857 0.66666667 1. ] - -mean value: 0.6776190476190476 - -key: train_fscore -value: [0.94117647 0.98039216 0.96153846 1. 0.96153846 0.94339623 - 0.98039216 0.96153846 0.96296296 0.98039216] - -mean value: 0.9673327515169913 - -key: test_precision -value: [1. 1. 0.5 0.5 0.5 1. 0.5 0.5 0.5 1. ] - -mean value: 0.7 - -key: train_precision -value: [0.96 1. 0.96153846 1. 0.96153846 0.92592593 - 1. 0.96153846 0.96296296 1. ] - -mean value: 0.9733504273504273 - -key: test_recall -value: [0.33333333 1. 1. 0.33333333 0.66666667 1. - 0.33333333 0.66666667 1. 1. ] - -mean value: 0.7333333333333333 - -key: train_recall -value: [0.92307692 0.96153846 0.96153846 1. 0.96153846 0.96153846 - 0.96153846 0.96153846 0.96296296 0.96153846] - -mean value: 0.9616809116809117 - -key: test_accuracy -value: [0.66666667 1. 0.5 0.5 0.5 1. - 0.5 0.5 0.6 1. ] - -mean value: 0.6766666666666665 - -key: train_accuracy -value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769 - 0.98076923 0.96153846 0.96226415 0.98113208] - -mean value: 0.9674165457184325 - -key: test_roc_auc -value: [0.66666667 1. 0.5 0.5 0.5 1. - 0.5 0.5 0.66666667 1. ] - -mean value: 0.6833333333333333 - -key: train_roc_auc -value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769 - 0.98076923 0.96153846 0.96225071 0.98076923] - -mean value: 0.9673789173789175 - -key: test_jcc -value: [0.33333333 1. 0.5 0.25 0.4 1. - 0.25 0.4 0.5 1. ] - -mean value: 0.5633333333333332 - -key: train_jcc -value: [0.88888889 0.96153846 0.92592593 1. 0.92592593 0.89285714 - 0.96153846 0.92592593 0.92857143 0.96153846] - -mean value: 0.9372710622710623 - -key: TN -value: 18 - -mean value: 18.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 21 - -mean value: 21.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.17 - -Accuracy on Blind test: 0.57 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.3368907 0.35755754 0.36199379 0.30242968 0.30465245 0.3230257 - 0.34169865 0.33659816 0.40250373 0.39995384] - -mean value: 0.3467304229736328 - -key: score_time -value: [0.01330423 0.01177645 0.01205063 0.01192522 0.01168323 0.01168942 - 0.01167512 0.01225448 0.01178122 0.01063108] - -mean value: 0.011877107620239257 - -key: test_mcc -value: [0.70710678 1. 0.70710678 0. 0. 1. - 0. 0.4472136 1. 1. ] - -mean value: 0.5861427157873053 - -key: train_mcc -value: [0.96225045 0.96225045 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9924500897298753 - -key: test_fscore -value: [0.8 1. 0.85714286 0.4 0.57142857 1. - 0.4 0.75 1. 1. ] - -mean value: 0.7778571428571428 - -key: train_fscore -value: [0.98039216 0.98039216 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.996078431372549 - -key: test_precision -value: [1. 1. 0.75 0.5 0.5 1. 0.5 0.6 1. 1. ] - -mean value: 0.7849999999999999 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 1. 0.33333333 0.66666667 1. - 0.33333333 1. 1. 1. ] - -mean value: 0.8 - -key: train_recall -value: [0.96153846 0.96153846 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9923076923076923 - -key: test_accuracy -value: [0.83333333 1. 0.83333333 0.5 0.5 1. - 0.5 0.66666667 1. 1. ] - -mean value: 0.7833333333333334 - -key: train_accuracy -value: [0.98076923 0.98076923 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9961538461538462 - -key: test_roc_auc -value: [0.83333333 1. 0.83333333 0.5 0.5 1. - 0.5 0.66666667 1. 1. ] - -mean value: 0.7833333333333334 - -key: train_roc_auc -value: [0.98076923 0.98076923 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9961538461538462 - -key: test_jcc -value: [0.66666667 1. 0.75 0.25 0.4 1. - 0.25 0.6 1. 1. ] - -mean value: 0.6916666666666667 - -key: train_jcc -value: [0.96153846 0.96153846 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9923076923076923 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 23 - -mean value: 23.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.62 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01113558 0.01096749 0.00920558 0.00855684 0.00815439 0.00800776 - 0.00789642 0.00879645 0.00837684 0.00784945] - -mean value: 0.008894681930541992 - -key: score_time -value: [0.01130128 0.01066971 0.00945187 0.0082593 0.00822663 0.00822568 - 0.00823283 0.00896049 0.00820088 0.00893879] - -mean value: 0.00904674530029297 - -key: test_mcc -value: [-0.33333333 0.70710678 0. -0.33333333 0. 0.4472136 - -0.4472136 -0.70710678 -0.16666667 0.40824829] - -mean value: -0.04250850428694703 - -key: train_mcc -value: [0.50336201 0.64676167 0.69230769 0.58080232 0.77151675 0.75878691 - 0.62279916 0.81312325 0.67348073 0.51261937] - -mean value: 0.6575559848126133 - -key: test_fscore -value: [0.33333333 0.85714286 0.66666667 0.33333333 0.66666667 0.75 - 0.5 0. 0.4 0.5 ] - -mean value: 0.5007142857142858 - -key: train_fscore -value: [0.76363636 0.83333333 0.84615385 0.8 0.88888889 0.88135593 - 0.82142857 0.89795918 0.81632653 0.77419355] - -mean value: 0.8323276198317204 - -key: test_precision -value: [0.33333333 0.75 0.5 0.33333333 0.5 0.6 - 0.4 0. 0.33333333 1. ] - -mean value: 0.475 - -key: train_precision -value: [0.72413793 0.73529412 0.84615385 0.75862069 0.85714286 0.78787879 - 0.76666667 0.95652174 0.90909091 0.66666667] - -mean value: 0.8008174211066882 - -key: test_recall -value: [0.33333333 1. 1. 0.33333333 1. 1. - 0.66666667 0. 0.5 0.33333333] - -mean value: 0.6166666666666666 - -key: train_recall -value: [0.80769231 0.96153846 0.84615385 0.84615385 0.92307692 1. - 0.88461538 0.84615385 0.74074074 0.92307692] - -mean value: 0.8779202279202278 - -key: test_accuracy -value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667 - 0.33333333 0.16666667 0.4 0.6 ] - -mean value: 0.4666666666666666 - -key: train_accuracy -value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462 - 0.80769231 0.90384615 0.83018868 0.73584906] - -mean value: 0.8219883889695211 - -key: test_roc_auc -value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667 - 0.33333333 0.16666667 0.41666667 0.66666667] - -mean value: 0.4750000000000001 - -key: train_roc_auc -value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462 - 0.80769231 0.90384615 0.83190883 0.73931624] - -mean value: 0.8225071225071225 - -key: test_jcc -value: [0.2 0.75 0.5 0.2 0.5 0.6 - 0.33333333 0. 0.25 0.33333333] - -mean value: 0.36666666666666664 - -key: train_jcc -value: [0.61764706 0.71428571 0.73333333 0.66666667 0.8 0.78787879 - 0.6969697 0.81481481 0.68965517 0.63157895] - -mean value: 0.7152830192554758 - -key: TN -value: 9 - -mean value: 9.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 18 - -mean value: 18.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.39 - -Accuracy on Blind test: 0.65 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00813365 0.00844741 0.00814033 0.00801945 0.00898266 0.00798416 - 0.00815678 0.00803804 0.00806212 0.00834966] - -mean value: 0.008231425285339355 - -key: score_time -value: [0.00833273 0.00839019 0.00825405 0.00831342 0.00851583 0.00821781 - 0.00835562 0.00828004 0.00859928 0.00834107] - -mean value: 0.008360004425048828 - -key: test_mcc -value: [ 0. 0.33333333 -0.33333333 0. 0.33333333 0.33333333 - -0.33333333 -0.70710678 -0.16666667 0.16666667] - -mean value: -0.03737734478532143 - -key: train_mcc -value: [0.57735027 0.73131034 0.61538462 0.65433031 0.84615385 0.70064905 - 0.58080232 0.6172134 0.58547009 0.54921597] - -mean value: 0.6457880196732791 - -key: test_fscore -value: [0.57142857 0.66666667 0.33333333 0.4 0.66666667 0.66666667 - 0.33333333 0. 0.4 0.66666667] - -mean value: 0.4704761904761905 - -key: train_fscore -value: [0.79245283 0.86792453 0.80769231 0.83018868 0.92307692 0.85714286 - 0.8 0.81481481 0.79245283 0.77777778] - -mean value: 0.8263523548429209 - -key: test_precision -value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 - 0.33333333 0. 0.33333333 0.66666667] - -mean value: 0.4666666666666666 - -key: train_precision -value: [0.77777778 0.85185185 0.80769231 0.81481481 0.92307692 0.8 - 0.75862069 0.78571429 0.80769231 0.75 ] - -mean value: 0.8077240958275441 - -key: test_recall -value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 0.66666667 - 0.33333333 0. 0.5 0.66666667] - -mean value: 0.4833333333333333 - -key: train_recall -value: [0.80769231 0.88461538 0.80769231 0.84615385 0.92307692 0.92307692 - 0.84615385 0.84615385 0.77777778 0.80769231] - -mean value: 0.847008547008547 - -key: test_accuracy -value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 - 0.33333333 0.16666667 0.4 0.6 ] - -mean value: 0.4833333333333333 - -key: train_accuracy -value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385 - 0.78846154 0.80769231 0.79245283 0.77358491] - -mean value: 0.8219883889695211 - -key: test_roc_auc -value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 - 0.33333333 0.16666667 0.41666667 0.58333333] - -mean value: 0.4833333333333333 - -key: train_roc_auc -value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385 - 0.78846154 0.80769231 0.79273504 0.77421652] - -mean value: 0.8220797720797719 - -key: test_jcc -value: [0.4 0.5 0.2 0.25 0.5 0.5 0.2 0. 0.25 0.5 ] - -mean value: 0.32999999999999996 - -key: train_jcc -value: [0.65625 0.76666667 0.67741935 0.70967742 0.85714286 0.75 - 0.66666667 0.6875 0.65625 0.63636364] - -mean value: 0.7063936601033376 - -key: TN -value: 14 - -mean value: 14.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 14 - -mean value: 14.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.02 - -Accuracy on Blind test: 0.45 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01033497 0.00774837 0.00769258 0.00766277 0.00772595 0.00759411 - 0.00768042 0.00764728 0.00764275 0.00769997] - -mean value: 0.007942914962768555 - -key: score_time -value: [0.01190901 0.00875068 0.00876427 0.0087657 0.00875902 0.0089035 - 0.00881863 0.00920081 0.00875378 0.00871611] - -mean value: 0.009134149551391602 - -key: test_mcc -value: [ 0.70710678 0.70710678 0. -0.4472136 0.4472136 0.4472136 - -0.4472136 0. 0.66666667 0.66666667] - -mean value: 0.27475468957064286 - -key: train_mcc -value: [0.34641016 0.34641016 0.54006172 0.65433031 0.50951017 0.54006172 - 0.65433031 0.57735027 0.43447293 0.50997151] - -mean value: 0.511290926764467 - -key: test_fscore -value: [0.8 0.8 0.57142857 0. 0.75 0.5 - 0.5 0.57142857 0.8 0.8 ] - -mean value: 0.6092857142857143 - -key: train_fscore -value: [0.66666667 0.66666667 0.76 0.82352941 0.72340426 0.76 - 0.82352941 0.78431373 0.71698113 0.75471698] - -mean value: 0.7479808250879637 - -key: test_precision -value: [1. 1. 0.5 0. 0.6 1. - 0.4 0.5 0.66666667 1. ] - -mean value: 0.6666666666666667 - -key: train_precision -value: [0.68 0.68 0.79166667 0.84 0.80952381 0.79166667 - 0.84 0.8 0.73076923 0.74074074] - -mean value: 0.7704367114367114 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0. 1. 0.33333333 - 0.66666667 0.66666667 1. 0.66666667] - -mean value: 0.6333333333333333 - -key: train_recall -value: [0.65384615 0.65384615 0.73076923 0.80769231 0.65384615 0.73076923 - 0.80769231 0.76923077 0.7037037 0.76923077] - -mean value: 0.728062678062678 - -key: test_accuracy -value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667 - 0.33333333 0.5 0.8 0.8 ] - -mean value: 0.6266666666666666 - -key: train_accuracy -value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077 - 0.82692308 0.78846154 0.71698113 0.75471698] - -mean value: 0.7548621190130624 - -key: test_roc_auc -value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667 - 0.33333333 0.5 0.83333333 0.83333333] - -mean value: 0.6333333333333333 - -key: train_roc_auc -value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077 - 0.82692308 0.78846154 0.71723647 0.75498575] - -mean value: 0.7549145299145299 - -key: test_jcc -value: [0.66666667 0.66666667 0.4 0. 0.6 0.33333333 - 0.33333333 0.4 0.66666667 0.66666667] - -mean value: 0.47333333333333344 - -key: train_jcc -value: [0.5 0.5 0.61290323 0.7 0.56666667 0.61290323 - 0.7 0.64516129 0.55882353 0.60606061] - -mean value: 0.6002518544074522 - -key: TN -value: 18 - -mean value: 18.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 18 - -mean value: 18.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.1 - -Accuracy on Blind test: 0.55 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00983238 0.00817156 0.00807023 0.00817275 0.00810385 0.00809503 - 0.00810146 0.00812173 0.00830746 0.00820637] - -mean value: 0.008318281173706055 - -key: score_time -value: [0.00890064 0.00827098 0.00825214 0.00835896 0.00818491 0.00825906 - 0.00822496 0.00825191 0.00823522 0.00826621] - -mean value: 0.008320498466491699 - -key: test_mcc -value: [-0.70710678 0.70710678 0. -0.4472136 -0.4472136 0.70710678 - -0.4472136 -0.4472136 0. 0.40824829] - -mean value: -0.06734993103494209 - -key: train_mcc -value: [0.58789635 0.77151675 0.80829038 0.84615385 0.84615385 0.84866842 - 0.80829038 0.77849894 0.71546507 0.81612228] - -mean value: 0.7827056268909882 - -key: test_fscore -value: [0.28571429 0.8 0.66666667 0. 0.5 0.8 - 0.5 0.5 0.57142857 0.5 ] - -mean value: 0.5123809523809524 - -key: train_fscore -value: [0.80701754 0.88 0.90566038 0.92307692 0.92307692 0.92592593 - 0.90566038 0.89285714 0.86666667 0.89795918] - -mean value: 0.8927901063853682 - -key: test_precision -value: [0.25 1. 0.5 0. 0.4 1. 0.4 0.4 0.4 1. ] - -mean value: 0.535 - -key: train_precision -value: [0.74193548 0.91666667 0.88888889 0.92307692 0.92307692 0.89285714 - 0.88888889 0.83333333 0.78787879 0.95652174] - -mean value: 0.8753124777668958 - -key: test_recall -value: [0.33333333 0.66666667 1. 0. 0.66666667 0.66666667 - 0.66666667 0.66666667 1. 0.33333333] - -mean value: 0.5999999999999999 - -key: train_recall -value: [0.88461538 0.84615385 0.92307692 0.92307692 0.92307692 0.96153846 - 0.92307692 0.96153846 0.96296296 0.84615385] - -mean value: 0.9155270655270658 - -key: test_accuracy -value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333 - 0.33333333 0.33333333 0.4 0.6 ] - -mean value: 0.4666666666666667 - -key: train_accuracy -value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692 - 0.90384615 0.88461538 0.8490566 0.90566038] - -mean value: 0.888933236574746 - -key: test_roc_auc -value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333 - 0.33333333 0.33333333 0.5 0.66666667] - -mean value: 0.4833333333333333 - -key: train_roc_auc -value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692 - 0.90384615 0.88461538 0.8468661 0.9045584 ] - -mean value: 0.8886039886039887 - -key: test_jcc -value: [0.16666667 0.66666667 0.5 0. 0.33333333 0.66666667 - 0.33333333 0.33333333 0.4 0.33333333] - -mean value: 0.37333333333333335 - -key: train_jcc -value: [0.67647059 0.78571429 0.82758621 0.85714286 0.85714286 0.86206897 - 0.82758621 0.80645161 0.76470588 0.81481481] - -mean value: 0.807968427761662 - -key: TN -value: 10 - -mean value: 10.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 17 - -mean value: 17.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.17 - -Accuracy on Blind test: 0.52 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.27118325 0.26555204 0.26822782 0.3543098 0.23144531 0.2879324 - 0.27575207 0.28139663 0.31252456 0.28410864] - -mean value: 0.2832432508468628 - -key: score_time -value: [0.01188612 0.01264691 0.01187563 0.0120225 0.01204729 0.01216674 - 0.01212144 0.01220417 0.01201892 0.0119679 ] - -mean value: 0.012095761299133301 - -key: test_mcc -value: [ 0.33333333 1. 0.4472136 -0.4472136 0. 1. - 0. -0.33333333 -0.16666667 1. ] - -mean value: 0.2833333333333333 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 1. 0.75 0. 0.57142857 1. - 0.4 0.33333333 0.4 1. ] - -mean value: 0.6121428571428572 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 1. 0.6 0. 0.5 1. - 0.5 0.33333333 0.33333333 1. ] - -mean value: 0.5933333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 1. 0. 0.66666667 1. - 0.33333333 0.33333333 0.5 1. ] - -mean value: 0.65 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.66666667 1. 0.66666667 0.33333333 0.5 1. - 0.5 0.33333333 0.4 1. ] - -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: [0.66666667 1. 0.66666667 0.33333333 0.5 1. - 0.5 0.33333333 0.41666667 1. ] - -mean value: 0.6416666666666667 - -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 1. 0.6 0. 0.4 1. 0.25 0.2 0.25 1. ] - -mean value: 0.52 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 18 - -mean value: 18.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 19 - -mean value: 19.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.18 - -Accuracy on Blind test: 0.6 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01426125 0.01220822 0.00889158 0.0087297 0.00895548 0.00863051 - 0.00851607 0.00860262 0.00862265 0.00863194] - -mean value: 0.009605002403259278 - -key: score_time -value: [0.01187658 0.0103178 0.00851917 0.00812912 0.00841165 0.00818539 - 0.00814438 0.00818205 0.0081892 0.0081985 ] - -mean value: 0.008815383911132813 - -key: test_mcc -value: [0.70710678 1. 0. 0.4472136 0. 0.70710678 - 1. 0.70710678 0.66666667 1. ] - -mean value: 0.6235200605726268 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286 - 1. 0.85714286 0.8 1. ] - -mean value: 0.7957142857142857 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.5 1. 0.5 0.75 - 1. 0.75 0.66666667 1. ] - -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: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. - 1. 1. 1. 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 - 1. 0.83333333 0.8 1. ] - -mean value: 0.7966666666666666 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 - 1. 0.83333333 0.83333333 1. ] - -mean value: 0.8 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 1. 0.4 0.33333333 0.4 0.75 - 1. 0.75 0.66666667 1. ] - -mean value: 0.6966666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.08186579 0.07851267 0.07875395 0.08117771 0.07774711 0.07864809 - 0.07936549 0.08071136 0.07908726 0.07877016] - -mean value: 0.07946395874023438 - -key: score_time -value: [0.01654387 0.01662087 0.01674056 0.01655841 0.01825023 0.01663804 - 0.0165503 0.01738572 0.01664352 0.01671124] - -mean value: 0.01686427593231201 - -key: test_mcc -value: [-0.33333333 0.70710678 0. 0. -0.70710678 0.70710678 - 0. -0.4472136 0.16666667 0.40824829] - -mean value: 0.050147480948378606 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.33333333 0.8 0.57142857 0.4 0.28571429 0.8 - 0.57142857 0.5 0.5 0.5 ] - -mean value: 0.5261904761904763 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.33333333 1. 0.5 0.5 0.25 1. - 0.5 0.4 0.5 1. ] - -mean value: 0.5983333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.33333333 0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 - 0.66666667 0.66666667 0.5 0.33333333] - -mean value: 0.5166666666666666 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.33333333 0.83333333 0.5 0.5 0.16666667 0.83333333 - 0.5 0.33333333 0.6 0.6 ] - -mean value: 0.5199999999999999 - -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 0.83333333 0.5 0.5 0.16666667 0.83333333 - 0.5 0.33333333 0.58333333 0.66666667] - -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: [0.2 0.66666667 0.4 0.25 0.16666667 0.66666667 - 0.4 0.33333333 0.33333333 0.33333333] - -mean value: 0.375 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 15 - -mean value: 15.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 15 - -mean value: 15.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.65 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00797677 0.00787926 0.00788188 0.00788665 0.00801349 0.0082798 - 0.00797129 0.0079236 0.00790405 0.00862122] - -mean value: 0.00803380012512207 - -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( -[0.0082202 0.00814605 0.00813627 0.00815105 0.00814056 0.008214 - 0.00821114 0.00814772 0.00879383 0.00855756] - -mean value: 0.00827183723449707 - -key: test_mcc -value: [ 0. 0.70710678 -0.4472136 -0.70710678 -0.70710678 -0.4472136 - 0. 0.4472136 0.66666667 0.66666667] - -mean value: 0.017901295664682747 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.4 0.8 0.5 0. 0.28571429 0. - 0.57142857 0.75 0.8 0.8 ] - -mean value: 0.49071428571428577 - -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. 0.4 0. 0.25 0. - 0.5 0.6 0.66666667 1. ] - -mean value: 0.4916666666666666 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.33333333 0.66666667 0.66666667 0. 0.33333333 0. - 0.66666667 1. 1. 0.66666667] - -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: [0.5 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333 - 0.5 0.66666667 0.8 0.8 ] - -mean value: 0.51 - -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 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333 - 0.5 0.66666667 0.83333333 0.83333333] - -mean value: 0.5166666666666666 - -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 0.66666667 0.33333333 0. 0.16666667 0. - 0.4 0.6 0.66666667 0.66666667] - -mean value: 0.375 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 14 - -mean value: 14.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 15 - -mean value: 15.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.57 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [0.98296475 0.98631954 0.98036337 0.98156142 0.97943497 0.98355842 - 0.98102999 0.97444534 0.98225522 0.97287917] - -mean value: 0.980481219291687 - -key: score_time -value: [0.0900774 0.09229064 0.09402585 0.09391332 0.09271097 0.0916388 - 0.09372568 0.09287024 0.09086537 0.08647847] - -mean value: 0.09185967445373536 - -key: test_mcc -value: [0. 1. 0. 0.4472136 0. 1. - 0. 0. 1. 0.66666667] - -mean value: 0.41138802621666243 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.4 1. 0.66666667 0.5 0.57142857 1. - 0.57142857 0.57142857 1. 0.8 ] - -mean value: 0.7080952380952381 - -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. 0.5 1. 0.5 1. 0.5 0.5 1. 1. ] - -mean value: 0.75 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.33333333 1. 1. 0.33333333 0.66666667 1. - 0.66666667 0.66666667 1. 0.66666667] - -mean value: 0.7333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.5 1. 0.5 0.66666667 0.5 1. - 0.5 0.5 1. 0.8 ] - -mean value: 0.6966666666666665 - -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 1. 0.5 0.66666667 0.5 1. - 0.5 0.5 1. 0.83333333] - -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: [0.25 1. 0.5 0.33333333 0.4 1. - 0.4 0.4 1. 0.66666667] - -mean value: 0.595 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 19 - -mean value: 19.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 21 - -mean value: 21.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.56 - -Accuracy on Blind test: 0.8 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.77408147 0.83756614 0.84048009 0.8518939 0.8126564 0.85278058 - 0.81466842 0.89787817 0.79662037 0.82353354] - -mean value: 0.8302159070968628 - -key: score_time -value: [0.18825769 0.19139767 0.17149496 0.18060875 0.18776417 0.12078071 - 0.18363857 0.19443631 0.18648338 0.21026301] - -mean value: 0.18151252269744872 - -key: test_mcc -value: [0. 1. 0. 0.4472136 0. 1. - 0. 0.70710678 0.66666667 0.66666667] - -mean value: 0.4487653710019838 - -key: train_mcc -value: [0.9258201 0.96225045 1. 0.96225045 1. 0.96225045 - 1. 0.96225045 0.92450142 1. ] - -mean value: 0.9699323318871482 - -key: test_fscore -value: [0.4 1. 0.66666667 0.5 0.57142857 1. - 0.57142857 0.85714286 0.8 0.8 ] - -mean value: 0.7166666666666666 - -key: train_fscore -value: [0.96 0.98039216 1. 0.98039216 1. 0.98113208 - 1. 0.98113208 0.96296296 1. ] - -mean value: 0.9846011427631851 - -key: test_precision -value: [0.5 1. 0.5 1. 0.5 1. - 0.5 0.75 0.66666667 1. ] - -mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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)) -0.7416666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 0.96296296 - 1. 0.96296296 0.96296296 1. ] - -mean value: 0.9888888888888889 - -key: test_recall -value: [0.33333333 1. 1. 0.33333333 0.66666667 1. - 0.66666667 1. 1. 0.66666667] - -mean value: 0.7666666666666667 - -key: train_recall -value: [0.92307692 0.96153846 1. 0.96153846 1. 1. - 1. 1. 0.96296296 1. ] - -mean value: 0.980911680911681 - -key: test_accuracy -value: [0.5 1. 0.5 0.66666667 0.5 1. - 0.5 0.83333333 0.8 0.8 ] - -mean value: 0.71 - -key: train_accuracy -value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923 - 1. 0.98076923 0.96226415 1. ] - -mean value: 0.9846879535558781 - -key: test_roc_auc -value: [0.5 1. 0.5 0.66666667 0.5 1. - 0.5 0.83333333 0.83333333 0.83333333] - -mean value: 0.7166666666666666 - -key: train_roc_auc -value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923 - 1. 0.98076923 0.96225071 1. ] - -mean value: 0.9846866096866098 - -key: test_jcc -value: [0.25 1. 0.5 0.33333333 0.4 1. - 0.4 0.75 0.66666667 0.66666667] - -mean value: 0.5966666666666667 - -key: train_jcc -value: [0.92307692 0.96153846 1. 0.96153846 1. 0.96296296 - 1. 0.96296296 0.92857143 1. ] - -mean value: 0.9700651200651201 - -key: TN -value: 19 - -mean value: 19.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 22 - -mean value: 22.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.51 - -Accuracy on Blind test: 0.78 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.06364822 0.03136325 0.02988315 0.02997828 0.030056 0.03207469 - 0.03320622 0.03187776 0.03167343 0.04203773] - -mean value: 0.035579872131347653 - -key: score_time -value: [0.01013684 0.01014662 0.01000309 0.01048088 0.01002479 0.01074505 - 0.00997162 0.01008677 0.01084256 0.01022935] - -mean value: 0.010266757011413575 - -key: test_mcc -value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 - 1. 1. 1. 1. ] - -mean value: 0.752809382453972 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 - 1. 1. 1. 1. ] - -mean value: 0.849047619047619 - -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 1. 0.66666667 0.75 - 1. 1. 1. 1. ] - -mean value: 0.9083333333333332 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. - 1. 1. 1. 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 - 1. 1. 1. 1. ] - -mean value: 0.775 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 1.0 - -Accuracy on Blind test: 1.0 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.01090717 0.04319477 0.03406215 0.0360713 0.03453875 0.03405213 - 0.04831815 0.01464033 0.01464176 0.01442671] - -mean value: 0.028485321998596193 - -key: score_time -value: [0.01095223 0.02088284 0.02107692 0.01997852 0.02296853 0.0319159 - 0.01285052 0.01170683 0.01175642 0.01163101] - -mean value: 0.01757197380065918 - -key: test_mcc -value: [ 0.70710678 1. 0. -0.33333333 0. 0.70710678 - 0.33333333 0.70710678 0. 0.61237244] - -mean value: 0.37336927792554375 - -key: train_mcc -value: [1. 0.84615385 1. 0.96225045 0.96225045 0.96225045 - 1. 1. 1. 1. ] - -mean value: 0.9732905192101976 - -key: test_fscore -value: [0.8 1. 0.4 0.33333333 0.57142857 0.8 - 0.66666667 0.8 0. 0.85714286] - -mean value: 0.6228571428571429 - -key: train_fscore -value: [1. 0.92307692 1. 0.98039216 0.98113208 0.98113208 - 1. 1. 1. 1. ] - -mean value: 0.9865733230883065 - -key: test_precision -value: [1. 1. 0.5 0.33333333 0.5 1. - 0.66666667 1. 0. 0.75 ] - -mean value: 0.675 - -key: train_precision -value: [1. 0.92307692 1. 1. 0.96296296 0.96296296 - 1. 1. 1. 1. ] - -mean value: 0.9849002849002849 - -key: test_recall -value: [0.66666667 1. 0.33333333 0.33333333 0.66666667 0.66666667 - 0.66666667 0.66666667 0. 1. ] - -mean value: 0.6 - -key: train_recall -value: [1. 0.92307692 1. 0.96153846 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9884615384615385 - -key: test_accuracy -value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333 - 0.66666667 0.83333333 0.6 0.8 ] - -mean value: 0.69 - -key: train_accuracy -value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923 - 1. 1. 1. 1. ] - -mean value: 0.9865384615384617 - -key: test_roc_auc -value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333 - 0.66666667 0.83333333 0.5 0.75 ] - -mean value: 0.675 - -key: train_roc_auc -value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923 - 1. 1. 1. 1. ] - -mean value: 0.9865384615384617 - -key: test_jcc -value: [0.66666667 1. 0.25 0.2 0.4 0.66666667 - 0.5 0.66666667 0. 0.75 ] - -mean value: 0.51 - -key: train_jcc -value: [1. 0.85714286 1. 0.96153846 0.96296296 0.96296296 - 1. 1. 1. 1. ] - -mean value: 0.9744607244607245 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 18 - -mean value: 18.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.24 - -Accuracy on Blind test: 0.6 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.0219574 0.00874662 0.00845957 0.00799561 0.00798368 0.00791121 - 0.00849724 0.00794101 0.00807071 0.00823379] - -mean value: 0.009579682350158691 - -key: score_time -value: [0.00983024 0.00915194 0.00822258 0.0083971 0.00818539 0.00826788 - 0.00891376 0.00871086 0.00815988 0.00857592] - -mean value: 0.008641552925109864 - -key: test_mcc -value: [-0.33333333 0.70710678 0. 0. -0.4472136 0. - -0.70710678 0.33333333 -0.61237244 0.40824829] - -mean value: -0.06513377407318895 - -key: train_mcc -value: [0.34848139 0.54006172 0.4233902 0.4259217 0.46709937 0.50336201 - 0.4259217 0.50037023 0.39888558 0.43447293] - -mean value: 0.44679668423593266 - -key: test_fscore -value: [0.33333333 0.8 0.66666667 0.4 0.5 0.4 - 0.28571429 0.66666667 0.33333333 0.5 ] - -mean value: 0.48857142857142855 - -key: train_fscore -value: [0.69090909 0.76 0.70588235 0.72727273 0.70833333 0.73469388 - 0.72727273 0.75471698 0.72413793 0.71698113] - -mean value: 0.7250200153522106 - -key: test_precision -value: [0.33333333 1. 0.5 0.5 0.4 0.5 - 0.25 0.66666667 0.25 1. ] - -mean value: 0.5399999999999999 - -key: train_precision -value: [0.65517241 0.79166667 0.72 0.68965517 0.77272727 0.7826087 - 0.68965517 0.74074074 0.67741935 0.7037037 ] - -mean value: 0.7223349192949957 - -key: test_recall -value: [0.33333333 0.66666667 1. 0.33333333 0.66666667 0.33333333 - 0.33333333 0.66666667 0.5 0.33333333] - -mean value: 0.5166666666666666 - -key: train_recall -value: [0.73076923 0.73076923 0.69230769 0.76923077 0.65384615 0.69230769 - 0.76923077 0.76923077 0.77777778 0.73076923] - -mean value: 0.7316239316239317 - -key: test_accuracy -value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5 - 0.16666667 0.66666667 0.2 0.6 ] - -mean value: 0.46333333333333326 - -key: train_accuracy -value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75 - 0.71153846 0.75 0.69811321 0.71698113] - -mean value: 0.722278664731495 - -key: test_roc_auc -value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5 - 0.16666667 0.66666667 0.25 0.66666667] - -mean value: 0.475 - -key: train_roc_auc -value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75 - 0.71153846 0.75 0.6965812 0.71723647] - -mean value: 0.7221509971509972 - -key: test_jcc -value: [0.2 0.66666667 0.5 0.25 0.33333333 0.25 - 0.16666667 0.5 0.2 0.33333333] - -mean value: 0.34 - -key: train_jcc -value: [0.52777778 0.61290323 0.54545455 0.57142857 0.5483871 0.58064516 - 0.57142857 0.60606061 0.56756757 0.55882353] - -mean value: 0.5690476653000371 - -key: TN -value: 12 - -mean value: 12.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 15 - -mean value: 15.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.57 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00844765 0.0117445 0.01213598 0.01206732 0.01215124 0.01197219 - 0.01199651 0.01190472 0.01303363 0.01203942] - -mean value: 0.01174931526184082 - -key: score_time -value: [0.00813985 0.01085377 0.0108459 0.01131415 0.01135468 0.01144958 - 0.01139641 0.01133323 0.01140904 0.01141429] - -mean value: 0.010951089859008788 - -key: test_mcc -value: [ 0.4472136 1. 0.4472136 0. -0.33333333 1. - -0.33333333 0.4472136 0. 1. ] - -mean value: 0.3674974119833207 - -key: train_mcc -value: [0.89056356 0.9258201 0.96225045 0.75878691 0.89056356 0.89056356 - 0.82305489 0.92307692 0.76178523 0.85164138] - -mean value: 0.8678106557240634 - -key: test_fscore -value: [0.5 1. 0.75 0.57142857 0.33333333 1. - 0.33333333 0.75 0.57142857 1. ] - -mean value: 0.6809523809523809 - -key: train_fscore -value: [0.93877551 0.96 0.98113208 0.88135593 0.93877551 0.93877551 - 0.9122807 0.96153846 0.8852459 0.92592593] - -mean value: 0.9323805529145449 - -key: test_precision -value: [1. 1. 0.6 0.5 0.33333333 1. - 0.33333333 0.6 0.4 1. ] - -mean value: 0.6766666666666667 - -key: train_precision -value: [1. 1. 0.96296296 0.78787879 1. 1. - 0.83870968 0.96153846 0.79411765 0.89285714] - -mean value: 0.9238064679715533 - -key: test_recall -value: [0.33333333 1. 1. 0.66666667 0.33333333 1. - 0.33333333 1. 1. 1. ] - -mean value: 0.7666666666666666 - -key: train_recall -value: [0.88461538 0.92307692 1. 1. 0.88461538 0.88461538 - 1. 0.96153846 1. 0.96153846] - -mean value: 0.95 - -key: test_accuracy -value: [0.66666667 1. 0.66666667 0.5 0.33333333 1. - 0.33333333 0.66666667 0.4 1. ] - -mean value: 0.6566666666666666 - -key: train_accuracy -value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769 - 0.90384615 0.96153846 0.86792453 0.9245283 ] - -mean value: 0.929245283018868 - -key: test_roc_auc -value: [0.66666667 1. 0.66666667 0.5 0.33333333 1. - 0.33333333 0.66666667 0.5 1. ] - -mean value: 0.6666666666666666 - -key: train_roc_auc -value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769 - 0.90384615 0.96153846 0.86538462 0.92521368] - -mean value: 0.929059829059829 - -key: test_jcc -value: [0.33333333 1. 0.6 0.4 0.2 1. - 0.2 0.6 0.4 1. ] - -mean value: 0.5733333333333334 - -key: train_jcc -value: [0.88461538 0.92307692 0.96296296 0.78787879 0.88461538 0.88461538 - 0.83870968 0.92592593 0.79411765 0.86206897] - -mean value: 0.8748587043686173 - -key: TN -value: 16 - -mean value: 16.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 22 - -mean value: 22.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.4 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00824594 0.00882649 0.01191044 0.01159239 0.01171517 0.01183748 - 0.01176429 0.011832 0.01183248 0.01164579] - -mean value: 0.011120247840881347 - -key: score_time -value: [0.00841475 0.00876427 0.01136231 0.01142454 0.01130295 0.0113647 - 0.01149154 0.01136804 0.01151252 0.01137519] - -mean value: 0.010838079452514648 - -key: test_mcc -value: [ 0.4472136 0.70710678 0.70710678 0. 0. 1. - 0. 0.70710678 -0.16666667 1. ] - -mean value: 0.4401867272392934 - -key: train_mcc -value: [0.28867513 0.89056356 1. 1. 1. 0.80829038 - 0.85634884 0.92307692 0.89227454 0.50219975] - -mean value: 0.8161429111522169 - -key: test_fscore -value: [0.75 0.8 0.8 0.4 0.57142857 1. - 0.4 0.85714286 0.4 1. ] - -mean value: 0.697857142857143 - -key: train_fscore -value: [0.7027027 0.93877551 1. 1. 1. 0.90196078 - 0.91666667 0.96153846 0.94736842 0.76470588] - -mean value: 0.9133718428831212 - -key: test_precision -value: [0.6 1. 1. 0.5 0.5 1. - 0.5 0.75 0.33333333 1. ] - -mean value: 0.7183333333333333 - -key: train_precision -value: [0.54166667 1. 1. 1. 1. 0.92 - 1. 0.96153846 0.9 0.61904762] - -mean value: 0.8942252747252747 - -key: test_recall -value: [1. 0.66666667 0.66666667 0.33333333 0.66666667 1. - 0.33333333 1. 0.5 1. ] - -mean value: 0.7166666666666666 - -key: train_recall -value: [1. 0.88461538 1. 1. 1. 0.88461538 - 0.84615385 0.96153846 1. 1. ] - -mean value: 0.9576923076923076 - -key: test_accuracy -value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1. - 0.5 0.83333333 0.4 1. ] - -mean value: 0.7066666666666668 - -key: train_accuracy -value: [0.57692308 0.94230769 1. 1. 1. 0.90384615 - 0.92307692 0.96153846 0.94339623 0.69811321] - -mean value: 0.8949201741654571 - -key: test_roc_auc -value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1. - 0.5 0.83333333 0.41666667 1. ] - -mean value: 0.7083333333333333 - -key: train_roc_auc -value: [0.57692308 0.94230769 1. 1. 1. 0.90384615 - 0.92307692 0.96153846 0.94230769 0.7037037 ] - -mean value: 0.8953703703703704 - -key: test_jcc -value: [0.6 0.66666667 0.66666667 0.25 0.4 1. - 0.25 0.75 0.25 1. ] - -mean value: 0.5833333333333333 - -key: train_jcc -value: [0.54166667 0.88461538 1. 1. 1. 0.82142857 - 0.84615385 0.92592593 0.9 0.61904762] - -mean value: 0.8538838013838014 - -key: TN -value: 20 - -mean value: 20.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 21 - -mean value: 21.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.06 - -Accuracy on Blind test: 0.52 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.06980157 0.07130361 0.07226229 0.06922984 0.06929612 0.07238531 - 0.0756197 0.07484674 0.07620382 0.07381821] - -mean value: 0.07247672080993653 - -key: score_time -value: [0.01429987 0.01539707 0.01418209 0.01416135 0.01432991 0.01474619 - 0.01519608 0.01451135 0.01416588 0.01511621] - -mean value: 0.014610600471496583 - -key: test_mcc -value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 - 1. 1. 1. 1. ] - -mean value: 0.752809382453972 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 - 1. 1. 1. 1. ] - -mean value: 0.849047619047619 - -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 1. 0.66666667 0.75 - 1. 1. 1. 1. ] - -mean value: 0.9083333333333332 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. - 1. 1. 1. 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 - 1. 1. 1. 1. ] - -mean value: 0.775 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 1.0 - -Accuracy on Blind test: 1.0 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02761078 0.0292685 0.03312254 0.02849674 0.03523755 0.0358634 - 0.02830291 0.02811313 0.03103304 0.03638864] - -mean value: 0.031343722343444826 - -key: score_time -value: [0.02042437 0.02199721 0.02151895 0.03123069 0.02501988 0.02050543 - 0.02155209 0.03574848 0.02209949 0.02571082] - -mean value: 0.024580740928649904 - -key: test_mcc -value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 - 1. 1. 1. 1. ] - -mean value: 0.752809382453972 - -key: train_mcc -value: [1. 0.96225045 1. 0.96225045 1. 1. - 1. 0.96225045 0.96296296 1. ] - -mean value: 0.9849714308911093 - -key: test_fscore -value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 - 1. 1. 1. 1. ] - -mean value: 0.849047619047619 - -key: train_fscore -value: [1. 0.98039216 1. 0.98039216 1. 1. - 1. 0.98039216 0.98113208 1. ] - -mean value: 0.9922308546059935 - -key: test_precision -value: [1. 1. 0.66666667 1. 0.66666667 0.75 - 1. 1. 1. 1. ] - -mean value: 0.9083333333333332 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. - 1. 1. 1. 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 0.96153846 1. 0.96153846 1. 1. - 1. 0.96153846 0.96296296 1. ] - -mean value: 0.9847578347578348 - -key: test_accuracy -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_accuracy -value: [1. 0.98076923 1. 0.98076923 1. 1. - 1. 0.98076923 0.98113208 1. ] - -mean value: 0.9923439767779391 - -key: test_roc_auc -value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8666666666666668 - -key: train_roc_auc -value: [1. 0.98076923 1. 0.98076923 1. 1. - 1. 0.98076923 0.98148148 1. ] - -mean value: 0.9923789173789175 - -key: test_jcc -value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 - 1. 1. 1. 1. ] - -mean value: 0.775 - -key: train_jcc -value: [1. 0.96153846 1. 0.96153846 1. 1. - 1. 0.96153846 0.96296296 1. ] - -mean value: 0.9847578347578348 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01054978 0.01045513 0.00980616 0.00982642 0.00995708 0.0100565 - 0.00980353 0.00985241 0.00990748 0.00987434] - -mean value: 0.010008883476257325 - -key: score_time -value: [0.00911832 0.00907159 0.00849557 0.00847793 0.00844884 0.00848508 - 0.00845075 0.00847316 0.00876307 0.00851512] - -mean value: 0.008629941940307617 - -key: test_mcc -value: [ 0.33333333 0.70710678 0. -0.4472136 -0.4472136 0.4472136 - 0. -0.4472136 0.40824829 0.40824829] - -mean value: 0.09625095044476913 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.8 0.57142857 0. 0.5 0.5 - 0.57142857 0.5 0.66666667 0.5 ] - -mean value: 0.5276190476190477 - -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/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -[0.66666667 1. 0.5 0. 0.4 1. - 0.5 0.4 0.5 1. ] - -mean value: 0.5966666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 0.66666667 0.66666667 0. 0.66666667 0.33333333 - 0.66666667 0.66666667 1. 0.33333333] - -mean value: 0.5666666666666667 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667 - 0.5 0.33333333 0.6 0.6 ] - -mean value: 0.5366666666666665 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667 - 0.5 0.33333333 0.66666667 0.66666667] - -mean value: 0.55 - -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 0.66666667 0.4 0. 0.33333333 0.33333333 - 0.4 0.33333333 0.5 0.33333333] - -mean value: 0.38 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 15 - -mean value: 15.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 16 - -mean value: 16.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.28 - -Accuracy on Blind test: 0.62 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.10271955 0.10487604 0.08293653 0.10259652 0.08842254 0.10237932 - 0.10384226 0.10312128 0.10364127 0.10579896] - -mean value: 0.10003342628479003 - -key: score_time -value: [0.00865936 0.00893712 0.00901771 0.0088377 0.00872445 0.0088408 - 0.0089922 0.00864983 0.0091002 0.00891113] - -mean value: 0.00886704921722412 - -key: test_mcc -value: [0.70710678 1. 0. 0.4472136 0. 0.70710678 - 1. 1. 1. 1. ] - -mean value: 0.6861427157873052 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286 - 1. 1. 1. 1. ] - -mean value: 0.8300000000000001 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.5 1. 0.5 0.75 1. 1. 1. 1. ] - -mean value: 0.875 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. - 1. 1. 1. 1. ] - -mean value: 0.8333333333333333 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8333333333333334 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 - 1. 1. 1. 1. ] - -mean value: 0.8333333333333334 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 1. 0.4 0.33333333 0.4 0.75 - 1. 1. 1. 1. ] - -mean value: 0.755 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.00854087 0.00976038 0.00851274 0.00856066 0.00975871 0.00889325 - 0.00963783 0.00880289 0.00930786 0.01002502] - -mean value: 0.009180021286010743 - -key: score_time -value: [0.00873137 0.00865483 0.0095017 0.00902653 0.00855947 0.00916696 - 0.00919914 0.00919485 0.00843978 0.00921845] - -mean value: 0.008969306945800781 - -key: test_mcc -value: [ 0.4472136 0.4472136 -0.70710678 0. -0.33333333 -0.4472136 - -0.4472136 0.4472136 -0.40824829 -0.61237244] - -mean value: -0.16138472451795804 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.75 0.5 0. 0.57142857 0.33333333 0.5 - 0.5 0.5 0. 0. ] - -mean value: 0.3654761904761905 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.6 1. 0. 0.5 0.33333333 0.4 - 0.4 1. 0. 0. ] - -mean value: 0.42333333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.33333333 0. 0.66666667 0.33333333 0.66666667 - 0.66666667 0.33333333 0. 0. ] - -mean value: 0.4 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333 - 0.33333333 0.66666667 0.4 0.2 ] - -mean value: 0.42666666666666664 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333 - 0.33333333 0.66666667 0.33333333 0.25 ] - -mean value: 0.425 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.6 0.33333333 0. 0.4 0.2 0.33333333 - 0.33333333 0.33333333 0. 0. ] - -mean value: 0.2533333333333333 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 13 - -mean value: 13.0 - -key: FP -value: 17 - -mean 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/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: 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 - rus_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: 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 - rus_CV['Resampling'] = rs_rus -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: 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 - rus_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: 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 - rus_BT['Resampling'] = rs_rus -17.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 12 - -mean value: 12.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.5 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01034141 0.0121088 0.01195264 0.01200914 0.02302814 0.01293015 - 0.0122416 0.01224279 0.01262784 0.01239896] - -mean value: 0.01318814754486084 - -key: score_time -value: [0.01122975 0.01138377 0.01147366 0.01134562 0.02079654 0.01142693 - 0.01140499 0.01142883 0.01149654 0.01142836] - -mean value: 0.012341499328613281 - -key: test_mcc -value: [0. 1. 1. 0. 0. 1. 0. - 0.4472136 1. 1. ] - -mean value: 0.5447213595499958 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.96225045 1. 1. ] - -mean value: 0.9962250448649377 - -key: test_fscore -value: [0.4 1. 1. 0.4 0.57142857 1. - 0.4 0.75 1. 1. ] - -mean value: 0.7521428571428571 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.98113208 1. 1. ] - -mean value: 0.9981132075471699 - -key: test_precision -value: [0.5 1. 1. 0.5 0.5 1. 0.5 0.6 1. 1. ] - -mean value: 0.76 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.96296296 1. 1. ] - -mean value: 0.9962962962962962 - -key: test_recall -value: [0.33333333 1. 1. 0.33333333 0.66666667 1. - 0.33333333 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: [0.5 1. 1. 0.5 0.5 1. - 0.5 0.66666667 1. 1. ] - -mean value: 0.7666666666666666 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.98076923 1. 1. ] - -mean value: 0.998076923076923 - -key: test_roc_auc -value: [0.5 1. 1. 0.5 0.5 1. - 0.5 0.66666667 1. 1. ] - -mean value: 0.7666666666666667 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98076923 1. 1. ] - -mean value: 0.998076923076923 - -key: test_jcc -value: [0.25 1. 1. 0.25 0.4 1. 0.25 0.6 1. 1. ] - -mean value: 0.675 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.96296296 1. 1. ] - -mean value: 0.9962962962962962 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 22 - -mean value: 22.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.57 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.07401347 0.07836699 0.078619 0.07845759 0.07878184 0.07833385 - 0.07831335 0.07814956 0.07846451 0.07824159] - -mean value: 0.07797417640686036 - -key: score_time -value: [0.01146913 0.01154041 0.01154804 0.01152658 0.01153302 0.01152396 - 0.0114727 0.01152277 0.01153612 0.01147652] - -mean value: 0.011514925956726074 - -key: test_mcc -value: [0.33333333 1. 1. 0. 0. 1. - 0. 0.4472136 0.66666667 1. ] - -mean value: 0.5447213595499958 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.96225045 1. 1. ] - -mean value: 0.9962250448649377 - -key: test_fscore -value: [0.66666667 1. 1. 0.4 0.57142857 1. - 0.4 0.75 0.8 1. ] - -mean value: 0.7588095238095238 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.98113208 1. 1. ] - -mean value: 0.9981132075471699 - -key: test_precision -value: [0.66666667 1. 1. 0.5 0.5 1. - 0.5 0.6 0.66666667 1. ] - -mean value: 0.7433333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.96296296 1. 1. ] - -mean value: 0.9962962962962962 - -key: test_recall -value: [0.66666667 1. 1. 0.33333333 0.66666667 1. - 0.33333333 1. 1. 1. ] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.66666667 1. 1. 0.5 0.5 1. - 0.5 0.66666667 0.8 1. ] - -mean value: 0.7633333333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.98076923 1. 1. ] - -mean value: 0.998076923076923 - -key: test_roc_auc -value: [0.66666667 1. 1. 0.5 0.5 1. - 0.5 0.66666667 0.83333333 1. ] - -mean value: 0.7666666666666667 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98076923 1. 1. ] - -mean value: 0.998076923076923 - -key: test_jcc -value: [0.5 1. 1. 0.25 0.4 1. - 0.25 0.6 0.66666667 1. ] - -mean value: 0.6666666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.96296296 1. 1. ] - -mean value: 0.9962962962962962 - -key: TN -value: 21 - -mean value: 21.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 23 - -mean value: 23.0 - -key: trainingY_neg -value: 29 - -mean value: 29.0 - -key: trainingY_pos -value: 29 - -mean value: 29.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.18 - -Accuracy on Blind test: 0.6 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02776575 0.02869368 0.02271485 0.02522039 0.02290487 0.02502799 - 0.02245021 0.05590749 0.02390695 0.03170252] - -mean value: 0.028629469871520995 - -key: score_time -value: [0.01155543 0.01159883 0.01160884 0.01166868 0.01157451 0.01157188 - 0.01154232 0.01169586 0.01163626 0.01184702] - -mean value: 0.011629962921142578 - -key: test_mcc -value: [1. 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658 - 0.40824829 0.81649658 0.65465367 0. ] - -mean value: 0.6638195626298699 - -key: train_mcc -value: [0.93541435 0.93541435 0.93541435 0.95650071 0.95555556 0.91201231 - 0.88910845 0.93541435 0.95555556 0.95555556] - -mean value: 0.9365945526249924 - -key: test_fscore -value: [1. 0.83333333 0.88888889 0.83333333 0.88888889 0.90909091 - 0.72727273 0.90909091 0.83333333 0.54545455] - -mean value: 0.8368686868686869 - -key: train_fscore -value: [0.96774194 0.96774194 0.96774194 0.97826087 0.97777778 0.95652174 - 0.94382022 0.96774194 0.97777778 0.97777778] - -mean value: 0.9682903908683571 - -key: test_precision -value: [1. 0.71428571 1. 0.71428571 1. 0.83333333 - 0.66666667 0.83333333 0.71428571 0.5 ] - -mean value: 0.7976190476190477 - -key: train_precision -value: [0.9375 0.9375 0.9375 0.95744681 0.97777778 0.93617021 - 0.95454545 0.9375 0.97777778 0.97777778] - -mean value: 0.9531495809155384 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 0.8 1. 1. 0.6] - -mean value: 0.9 - -key: train_recall -value: [1. 1. 1. 1. 0.97777778 0.97777778 - 0.93333333 1. 0.97777778 0.97777778] - -mean value: 0.9844444444444445 - -key: test_accuracy -value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5] - -mean value: 0.82 - -key: train_accuracy -value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556 - 0.94444444 0.96666667 0.97777778 0.97777778] - -mean value: 0.9677777777777777 - -key: test_roc_auc -value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5] - -mean value: 0.82 - -key: train_roc_auc -value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556 - 0.94444444 0.96666667 0.97777778 0.97777778] - -mean value: 0.9677777777777778 - -key: test_jcc -value: [1. 0.71428571 0.8 0.71428571 0.8 0.83333333 - 0.57142857 0.83333333 0.71428571 0.375 ] - -mean value: 0.7355952380952381 - -key: train_jcc -value: [0.9375 0.9375 0.9375 0.95744681 0.95652174 0.91666667 - 0.89361702 0.9375 0.95652174 0.95652174] - -mean value: 0.9387295713845207 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.72 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.56633496 0.55252409 0.53593993 0.68272257 0.54451585 0.53557205 - 0.54859138 0.74416137 0.54470611 0.56081605] - -mean value: 0.5815884351730347 - -key: score_time -value: [0.01307201 0.01323223 0.01380658 0.01179194 0.01315022 0.01324105 - 0.01309943 0.0132587 0.01182222 0.01328754] - -mean value: 0.01297619342803955 - -key: test_mcc -value: [1. 0.81649658 0.6 0.65465367 0.81649658 0.65465367 - 1. 1. 0.40824829 0.21821789] - -mean value: 0.7168766683971262 - -key: train_mcc -value: [0.97801929 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978019293843652 - -key: test_fscore -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[1. 0.90909091 0.8 0.83333333 0.88888889 0.83333333 - 1. 1. 0.72727273 0.66666667] - -mean value: 0.8658585858585859 - -key: train_fscore -value: [0.98901099 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9989010989010989 - -key: test_precision -value: [1. 0.83333333 0.8 0.71428571 1. 0.71428571 - 1. 1. 0.66666667 0.57142857] - -mean value: 0.8300000000000001 - -key: train_precision -value: [0.97826087 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978260869565216 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 0.8] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6] - -mean value: 0.85 - -key: train_accuracy -value: [0.98888889 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6] - -mean value: 0.85 - -key: train_roc_auc -value: [0.98888889 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_jcc -value: [1. 0.83333333 0.66666667 0.71428571 0.8 0.71428571 - 1. 1. 0.57142857 0.5 ] - -mean value: 0.78 - -key: train_jcc -value: [0.97826087 1. 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978260869565216 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.68 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01261568 0.01144147 0.00931716 0.00832486 0.00855613 0.00928593 - 0.00807047 0.00803423 0.00799537 0.00809693] - -mean value: 0.009173822402954102 - -key: score_time -value: [0.01160741 0.00956869 0.00901723 0.00961471 0.00909519 0.00840259 - 0.00830054 0.00824618 0.00833321 0.00828719] - -mean value: 0.009047293663024902 - -key: test_mcc -value: [ 0.5 0. -0.33333333 0.5 0. 0.40824829 - -0.33333333 0.40824829 0. 0. ] - -mean value: 0.11498299142610595 - -key: train_mcc -value: [0.45226702 0.53931937 0.53931937 0.57906602 0.43808583 0.69509522 - 0.48001536 0.56568542 0.51066218 0.68957028] - -mean value: 0.5489086080136931 - -key: test_fscore -value: [0.76923077 0.66666667 0.57142857 0.76923077 0.54545455 0.72727273 - 0.57142857 0.72727273 0.54545455 0.54545455] - -mean value: 0.6438894438894438 - -key: train_fscore -value: [0.75675676 0.78899083 0.78899083 0.79569892 0.74509804 0.85416667 - 0.76635514 0.8 0.77669903 0.84782609] - -mean value: 0.792058229501609 - -key: test_precision -value: [0.625 0.5 0.44444444 0.625 0.5 0.66666667 - 0.44444444 0.66666667 0.5 0.5 ] - -mean value: 0.5472222222222223 - -key: train_precision -value: [0.63636364 0.671875 0.671875 0.77083333 0.66666667 0.80392157 - 0.66129032 0.7 0.68965517 0.82978723] - -mean value: 0.7102267934028078 - -key: test_recall -value: [1. 1. 0.8 1. 0.6 0.8 0.8 0.8 0.6 0.6] - -mean value: 0.7999999999999999 - -key: train_recall -value: [0.93333333 0.95555556 0.95555556 0.82222222 0.84444444 0.91111111 - 0.91111111 0.93333333 0.88888889 0.86666667] - -mean value: 0.9022222222222223 - -key: test_accuracy -value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5] - -mean value: 0.5599999999999999 - -key: train_accuracy -value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444 - 0.72222222 0.76666667 0.74444444 0.84444444] - -mean value: 0.7611111111111111 - -key: test_roc_auc -value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5] - -mean value: 0.5599999999999999 - -key: train_roc_auc -value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444 - 0.72222222 0.76666667 0.74444444 0.84444444] - -mean value: 0.7611111111111111 - -key: test_jcc -value: [0.625 0.5 0.4 0.625 0.375 0.57142857 - 0.4 0.57142857 0.375 0.375 ] - -mean value: 0.4817857142857143 - -key: train_jcc -value: [0.60869565 0.65151515 0.65151515 0.66071429 0.59375 0.74545455 - 0.62121212 0.66666667 0.63492063 0.73584906] - -mean value: 0.6570293265776243 - -key: TN -value: 16 - -mean value: 16.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 34 - -mean value: 34.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.39 - -Accuracy on Blind test: 0.65 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.0094583 0.00919294 0.00911546 0.00833702 0.00844932 0.00869441 - 0.00868297 0.00884938 0.00957847 0.00917697] - -mean value: 0.008953523635864259 - -key: score_time -value: [0.00851822 0.00911808 0.01154637 0.009516 0.00891685 0.00901747 - 0.0085547 0.00905323 0.00843143 0.00888371] - -mean value: 0.009155607223510743 - -key: test_mcc -value: [0.81649658 0.65465367 0.2 0.2 0.5 0.40824829 - 0.65465367 0.2 0. 0. ] - -mean value: 0.3634052212807543 - -key: train_mcc -value: [0.49897013 0.58137767 0.53346507 0.55555556 0.57792049 0.53346507 - 0.53346507 0.51111111 0.68888889 0.80178373] - -mean value: 0.5816002788156436 - -key: test_fscore -value: [0.88888889 0.83333333 0.6 0.6 0.57142857 0.66666667 - 0.83333333 0.6 0.28571429 0.44444444] - -mean value: 0.6323809523809524 - -key: train_fscore -value: [0.76767677 0.8 0.76923077 0.77777778 0.79120879 0.76404494 - 0.76404494 0.75555556 0.84444444 0.89655172] - -mean value: 0.7930535717672486 - -key: test_precision -value: [1. 0.71428571 0.6 0.6 1. 0.75 - 0.71428571 0.6 0.5 0.5 ] - -mean value: 0.6978571428571428 - -key: train_precision -value: [0.7037037 0.76 0.76086957 0.77777778 0.7826087 0.77272727 - 0.77272727 0.75555556 0.84444444 0.92857143] - -mean value: 0.7858985716377022 - -key: test_recall -value: [0.8 1. 0.6 0.6 0.4 0.6 1. 0.6 0.2 0.4] - -mean value: 0.62 - -key: train_recall -value: [0.84444444 0.84444444 0.77777778 0.77777778 0.8 0.75555556 - 0.75555556 0.75555556 0.84444444 0.86666667] - -mean value: 0.8022222222222222 - -key: test_accuracy -value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5] - -mean value: 0.67 - -key: train_accuracy -value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667 - 0.76666667 0.75555556 0.84444444 0.9 ] - -mean value: 0.7899999999999999 - -key: test_roc_auc -value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5] - -mean value: 0.67 - -key: train_roc_auc -value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667 - 0.76666667 0.75555556 0.84444444 0.9 ] - -mean value: 0.7899999999999999 - -key: test_jcc -value: [0.8 0.71428571 0.42857143 0.42857143 0.4 0.5 - 0.71428571 0.42857143 0.16666667 0.28571429] - -mean value: 0.4866666666666667 - -key: train_jcc -value: [0.62295082 0.66666667 0.625 0.63636364 0.65454545 0.61818182 - 0.61818182 0.60714286 0.73076923 0.8125 ] - -mean value: 0.6592302301523614 - -key: TN -value: 36 - -mean value: 36.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 31 - -mean value: 31.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.06 - -Accuracy on Blind test: 0.5 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01051545 0.00867343 0.0085032 0.00867558 0.00853562 0.00811458 - 0.00812817 0.00796604 0.0099206 0.00903177] - -mean value: 0.008806443214416504 - -key: score_time -value: [0.01103592 0.00978136 0.00917578 0.00924969 0.01515198 0.01001072 - 0.01416135 0.00911379 0.01416039 0.01609468] - -mean value: 0.011793565750122071 - -key: test_mcc -value: [ 0.81649658 0.65465367 0.5 0. -0.2 0.2 - 0.5 0.40824829 0.40824829 -0.21821789] - -mean value: 0.3069428942327437 - -key: train_mcc -value: [0.47863442 0.49897013 0.56056066 0.52421865 0.49103499 0.47087096 - 0.47863442 0.53990552 0.54433105 0.56056066] - -mean value: 0.5147721476862103 - -key: test_fscore -value: [0.90909091 0.83333333 0.76923077 0.61538462 0.4 0.6 - 0.76923077 0.72727273 0.72727273 0.5 ] - -mean value: 0.6850815850815851 - -key: train_fscore -value: [0.76 0.76767677 0.79166667 0.78 0.76923077 0.75 - 0.76 0.78350515 0.78787879 0.79166667] - -mean value: 0.7741624812758834 - -key: test_precision -value: [0.83333333 0.71428571 0.625 0.5 0.4 0.6 - 0.625 0.66666667 0.66666667 0.42857143] - -mean value: 0.6059523809523809 - -key: train_precision -value: [0.69090909 0.7037037 0.74509804 0.70909091 0.6779661 0.70588235 - 0.69090909 0.73076923 0.72222222 0.74509804] - -mean value: 0.7121648780671712 - -key: test_recall -value: [1. 1. 1. 0.8 0.4 0.6 1. 0.8 0.8 0.6] - -mean value: 0.7999999999999999 - -key: train_recall -value: [0.84444444 0.84444444 0.84444444 0.86666667 0.88888889 0.8 - 0.84444444 0.84444444 0.86666667 0.84444444] - -mean value: 0.848888888888889 - -key: test_accuracy -value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4] - -mean value: 0.6400000000000001 - -key: train_accuracy -value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333 - 0.73333333 0.76666667 0.76666667 0.77777778] - -mean value: 0.7522222222222222 - -key: test_roc_auc -value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4] - -mean value: 0.6400000000000001 - -key: train_roc_auc -value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333 - 0.73333333 0.76666667 0.76666667 0.77777778] - -mean value: 0.7522222222222222 - -key: test_jcc -value: [0.83333333 0.71428571 0.625 0.44444444 0.25 0.42857143 - 0.625 0.57142857 0.57142857 0.33333333] - -mean value: 0.5396825396825397 - -key: train_jcc -value: [0.61290323 0.62295082 0.65517241 0.63934426 0.625 0.6 - 0.61290323 0.6440678 0.65 0.65517241] - -mean value: 0.6317514157776494 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.07 - -Accuracy on Blind test: 0.48 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00882292 0.00860882 0.00860405 0.00861382 0.00862885 0.00865412 - 0.00877261 0.00858068 0.00863194 0.00862861] - -mean value: 0.008654642105102538 - -key: score_time -value: [0.00841212 0.0083425 0.00839615 0.00844121 0.00848246 0.00840759 - 0.00846624 0.00836325 0.00846195 0.00850344] - -mean value: 0.008427691459655762 - -key: test_mcc -value: [0.5 0.81649658 0.65465367 0.40824829 0.5 0.65465367 - 0.40824829 0.6 0.40824829 0.2 ] - -mean value: 0.515054879373527 - -key: train_mcc -value: [0.84465303 0.8001976 0.75724019 0.78086881 0.80498447 0.75724019 - 0.73624773 0.75724019 0.79036782 0.80498447] - -mean value: 0.7834024490981437 - -key: test_fscore -value: [0.57142857 0.90909091 0.75 0.66666667 0.57142857 0.75 - 0.72727273 0.8 0.66666667 0.6 ] - -mean value: 0.7012554112554111 - -key: train_fscore -value: [0.92134831 0.8988764 0.87356322 0.88372093 0.89411765 0.87356322 - 0.86046512 0.87356322 0.87804878 0.89411765] - -mean value: 0.8851384495390617 - -key: test_precision -value: [1. 0.83333333 1. 0.75 1. 1. - 0.66666667 0.8 0.75 0.6 ] - -mean value: 0.8400000000000001 - -key: train_precision -value: [0.93181818 0.90909091 0.9047619 0.92682927 0.95 0.9047619 - 0.90243902 0.9047619 0.97297297 0.95 ] - -mean value: 0.9257436070850705 - -key: test_recall -value: [0.4 1. 0.6 0.6 0.4 0.6 0.8 0.8 0.6 0.6] - -mean value: 0.6399999999999999 - -key: train_recall -value: [0.91111111 0.88888889 0.84444444 0.84444444 0.84444444 0.84444444 - 0.82222222 0.84444444 0.8 0.84444444] - -mean value: 0.8488888888888889 - -key: test_accuracy -value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6] - -mean value: 0.74 - -key: train_accuracy -value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778 - 0.86666667 0.87777778 0.88888889 0.9 ] - -mean value: 0.89 - -key: test_roc_auc -value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6] - -mean value: 0.74 - -key: train_roc_auc -value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778 - 0.86666667 0.87777778 0.88888889 0.9 ] - -mean value: 0.89 - -key: test_jcc -value: [0.4 0.83333333 0.6 0.5 0.4 0.6 - 0.57142857 0.66666667 0.5 0.42857143] - -mean value: 0.55 - -key: train_jcc -value: [0.85416667 0.81632653 0.7755102 0.79166667 0.80851064 0.7755102 - 0.75510204 0.7755102 0.7826087 0.80851064] - -mean value: 0.7943422489254721 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 32 - -mean value: 32.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.04 - -Accuracy on Blind test: 0.62 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.46989155 0.45708156 0.47301435 0.51050806 0.4922812 0.58298516 - 0.6829381 0.55199766 0.33458424 0.63565779] - -mean value: 0.5190939664840698 - -key: score_time -value: [0.01193571 0.0119791 0.01196957 0.01409888 0.01202154 0.01195478 - 0.01197529 0.01233053 0.01202965 0.01204038] - -mean value: 0.012233543395996093 - -key: test_mcc -value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 - 0.81649658 1. 0.65465367 0.21821789] - -mean value: 0.7069825734923353 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 - 0.90909091 1. 0.83333333 0.66666667] - -mean value: 0.8597979797979798 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 - 0.83333333 1. 0.71428571 0.57142857] - -mean value: 0.8061904761904763 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.8] - -mean value: 0.9400000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6] - -mean value: 0.8400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6] - -mean value: 0.8400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 - 0.83333333 1. 0.71428571 0.5 ] - -mean value: 0.7657142857142858 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.6 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01784778 0.01463175 0.00978041 0.00983381 0.00975132 0.00979137 - 0.01004624 0.00982881 0.00951791 0.00948429] - -mean value: 0.011051368713378907 - -key: score_time -value: [0.01210833 0.01203465 0.0088129 0.00876093 0.00856209 0.00864077 - 0.00851083 0.00841141 0.00842595 0.00868106] - -mean value: 0.009294891357421875 - -key: test_mcc -value: [1. 1. 0.81649658 1. 0.6 0.65465367 - 0.81649658 0.6 0.6 0.81649658] - -mean value: 0.7904143413491156 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.88888889 1. 0.8 0.83333333 - 0.90909091 0.8 0.8 0.90909091] - -mean value: 0.894040404040404 - -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. 1. 0.8 0.71428571 - 0.83333333 0.8 0.8 0.83333333] - -mean value: 0.878095238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 1. ] - -mean value: 0.9200000000000002 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9] - -mean value: 0.89 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9] - -mean value: 0.89 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.8 1. 0.66666667 0.71428571 - 0.83333333 0.66666667 0.66666667 0.83333333] - -mean value: 0.818095238095238 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.08306623 0.08243489 0.08078671 0.08000588 0.08049583 0.08101439 - 0.08041883 0.08083487 0.07945704 0.0797565 ] - -mean value: 0.08082711696624756 - -key: score_time -value: [0.01788282 0.01666999 0.017452 0.01670957 0.01666594 0.01666021 - 0.01663876 0.01678586 0.01667166 0.01662517] - -mean value: 0.01687619686126709 - -key: test_mcc -value: [0.5 0.81649658 0.40824829 0.65465367 0.65465367 0.81649658 - 0.33333333 0.6 0.40824829 0.2 ] - -mean value: 0.5392130417532466 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.57142857 0.90909091 0.72727273 0.83333333 0.75 0.88888889 - 0.71428571 0.8 0.66666667 0.6 ] - -mean value: 0.7460966810966811 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 0.66666667 0.71428571 1. 1. - 0.55555556 0.8 0.75 0.6 ] - -mean value: 0.7919841269841269 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.4 1. 0.8 1. 0.6 0.8 1. 0.8 0.6 0.6] - -mean value: 0.76 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6] - -mean value: 0.75 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6] - -mean value: 0.75 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.4 0.83333333 0.57142857 0.71428571 0.6 0.8 - 0.55555556 0.66666667 0.5 0.42857143] - -mean value: 0.606984126984127 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 38 - -mean value: 38.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.78 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.00919604 0.00881147 0.00983024 0.00988555 0.00845003 0.00822234 - 0.00817466 0.00906372 0.0087781 0.00858617] - -mean value: 0.008899831771850586 - -key: score_time -value: [0.00908685 0.00836706 0.00952148 0.01046968 0.00823426 0.00821829 - 0.00818181 0.00913 0.00822067 0.00824046] - -mean value: 0.008767056465148925 - -key: test_mcc -value: [-0.21821789 0.81649658 0.40824829 0.65465367 0.6 0.81649658 - 0.81649658 0.6 0.2 0.40824829] - -mean value: 0.5102422104182889 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.90909091 0.72727273 0.83333333 0.8 0.90909091 - 0.90909091 0.8 0.6 0.66666667] - -mean value: 0.7654545454545454 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.42857143 0.83333333 0.66666667 0.71428571 0.8 0.83333333 - 0.83333333 0.8 0.6 0.75 ] - -mean value: 0.7259523809523809 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.6 1. 0.8 1. 0.8 1. 1. 0.8 0.6 0.6] - -mean value: 0.82 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7] - -mean value: 0.75 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7] - -mean value: 0.75 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.33333333 0.83333333 0.57142857 0.71428571 0.66666667 0.83333333 - 0.83333333 0.66666667 0.42857143 0.5 ] - -mean value: 0.6380952380952382 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.56 - -Accuracy on Blind test: 0.8 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.12863898 1.05638361 1.04797721 1.22669673 1.21202326 1.01976037 - 1.10352778 1.02443147 1.04925394 1.07379055] - -mean value: 1.094248390197754 - -key: score_time -value: [0.10170126 0.09575319 0.09421945 0.10475135 0.0917902 0.09088731 - 0.0953505 0.08893514 0.09012246 0.09573841] - -mean value: 0.0949249267578125 - -key: test_mcc -value: [0.65465367 0.81649658 0.6 0.81649658 0.65465367 1. - 0.81649658 0.81649658 0.6 0.2 ] - -mean value: 0.6975293665126858 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -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( -[0.75 0.90909091 0.8 0.90909091 0.75 1. - 0.90909091 0.88888889 0.8 0.6 ] - -mean value: 0.8316161616161617 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 0.8 0.83333333 1. 1. - 0.83333333 1. 0.8 0.6 ] - -mean value: 0.8700000000000001 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.6 1. 0.8 1. 0.6 1. 1. 0.8 0.8 0.6] - -mean value: 0.8200000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6] - -mean value: 0.8400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6] - -mean value: 0.8400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.6 0.83333333 0.66666667 0.83333333 0.6 1. - 0.83333333 0.8 0.66666667 0.42857143] - -mean value: 0.7261904761904763 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.8 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.83751202 0.89611864 0.86344433 0.82031369 0.8383913 0.87373209 - 0.85556459 0.87743616 0.84090114 0.83468747] - -mean value: 0.8538101434707641 - -key: score_time -value: [0.1183486 0.17216015 0.1454649 0.1703856 0.16838622 0.20413017 - 0.18524504 0.20958233 0.19292188 0.14789653] - -mean value: 0.17145214080810547 - -key: test_mcc -value: [1. 0.81649658 0.81649658 0.81649658 0.81649658 1. - 0.81649658 0.6 0.6 0.2 ] - -mean value: 0.748248290463863 - -key: train_mcc -value: [0.97801929 1. 1. 1. 0.95555556 0.95650071 - 0.95555556 0.95555556 0.93356387 0.93356387] - -mean value: 0.9668314417898023 - -key: test_fscore -value: [1. 0.90909091 0.88888889 0.90909091 0.88888889 1. - 0.90909091 0.8 0.8 0.6 ] - -mean value: 0.8705050505050507 - -key: train_fscore -value: [0.98876404 1. 1. 1. 0.97777778 0.97727273 - 0.97777778 0.97777778 0.96629213 0.96629213] - -mean value: 0.9831954375212801 - -key: test_precision -value: [1. 0.83333333 1. 0.83333333 1. 1. - 0.83333333 0.8 0.8 0.6 ] - -mean value: 0.8700000000000001 - -key: train_precision -value: [1. 1. 1. 1. 0.97777778 1. - 0.97777778 0.97777778 0.97727273 0.97727273] - -mean value: 0.9887878787878787 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6] - -mean value: 0.8800000000000001 - -key: train_recall -value: [0.97777778 1. 1. 1. 0.97777778 0.95555556 - 0.97777778 0.97777778 0.95555556 0.95555556] - -mean value: 0.9777777777777779 - -key: test_accuracy -value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6] - -mean value: 0.8700000000000001 - -key: train_accuracy -value: [0.98888889 1. 1. 1. 0.97777778 0.97777778 - 0.97777778 0.97777778 0.96666667 0.96666667] - -mean value: 0.9833333333333334 - -key: test_roc_auc -value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6] - -mean value: 0.8700000000000001 - -key: train_roc_auc -value: [0.98888889 1. 1. 1. 0.97777778 0.97777778 - 0.97777778 0.97777778 0.96666667 0.96666667] - -mean value: 0.9833333333333334 - -key: test_jcc -value: [1. 0.83333333 0.8 0.83333333 0.8 1. - 0.83333333 0.66666667 0.66666667 0.42857143] - -mean value: 0.7861904761904762 - -key: train_jcc -value: [0.97777778 1. 1. 1. 0.95652174 0.95555556 - 0.95652174 0.95652174 0.93478261 0.93478261] - -mean value: 0.9672463768115943 - -key: TN -value: 43 - -mean value: 43.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.85 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.10435414 0.03400135 0.04029894 0.03453851 0.03732896 0.03765655 - 0.0378046 0.0363245 0.03529716 0.03576374] - -mean value: 0.0433368444442749 - -key: score_time -value: [0.01012611 0.00994301 0.01033235 0.01006889 0.01035452 0.01092505 - 0.01093435 0.01010299 0.01055551 0.01002288] - -mean value: 0.010336565971374511 - -key: test_mcc -value: [1. 0.81649658 0.81649658 0.81649658 1. 0.81649658 - 0.81649658 0.81649658 1. 1. ] - -mean value: 0.8898979485566356 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 0.88888889 0.90909091 1. 0.90909091 - 0.90909091 0.90909091 1. 1. ] - -mean value: 0.9434343434343434 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 0.83333333 1. 0.83333333 - 0.83333333 0.83333333 1. 1. ] - -mean value: 0.9166666666666667 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ] - -mean value: 0.9800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ] - -mean value: 0.9400000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ] - -mean value: 0.9400000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 0.8 0.83333333 1. 0.83333333 - 0.83333333 0.83333333 1. 1. ] - -mean value: 0.8966666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 45 - -mean value: 45.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.0220542 0.03752089 0.05885172 0.03988481 0.04153132 0.04049897 - 0.04043698 0.04112816 0.04124546 0.03902507] - -mean value: 0.04021775722503662 - -key: score_time -value: [0.01765323 0.01775956 0.02387667 0.0141654 0.0216701 0.02373743 - 0.0230279 0.02206993 0.0226841 0.01399088] - -mean value: 0.02006351947784424 - -key: test_mcc -value: [0.40824829 0.33333333 0.40824829 0.6 0.6 0.6 - 0.81649658 0.40824829 0.21821789 0.40824829] - -mean value: 0.48010409663525044 - -key: train_mcc -value: [1. 1. 1. 1. 0.97801929 1. - 1. 1. 1. 1. ] - -mean value: 0.9978019293843652 - -key: test_fscore -value: [0.66666667 0.71428571 0.72727273 0.8 0.8 0.8 - 0.90909091 0.72727273 0.66666667 0.66666667] - -mean value: 0.7477922077922079 - -key: train_fscore -value: [1. 1. 1. 1. 0.98876404 1. - 1. 1. 1. 1. ] - -mean value: 0.9988764044943821 - -key: test_precision -value: [0.75 0.55555556 0.66666667 0.8 0.8 0.8 - 0.83333333 0.66666667 0.57142857 0.75 ] - -mean value: 0.7193650793650793 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.6 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8 0.6] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 1. 1. 0.97777778 1. - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_accuracy -value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7] - -mean value: 0.73 - -key: train_accuracy -value: [1. 1. 1. 1. 0.98888889 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7] - -mean value: 0.73 - -key: train_roc_auc -value: [1. 1. 1. 1. 0.98888889 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_jcc -value: [0.5 0.55555556 0.57142857 0.66666667 0.66666667 0.66666667 - 0.83333333 0.57142857 0.5 0.5 ] - -mean value: 0.6031746031746031 - -key: train_jcc -value: [1. 1. 1. 1. 0.97777778 1. - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.5 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.0215857 0.00902247 0.00941372 0.00821018 0.00910091 0.00885034 - 0.00864315 0.00913239 0.00845718 0.00905919] - -mean value: 0.010147523880004884 - -key: score_time -value: [0.01546955 0.00928783 0.00828052 0.00871253 0.00915241 0.00861788 - 0.00836587 0.00900817 0.00822067 0.00899863] - -mean value: 0.00941140651702881 - -key: test_mcc -value: [ 1. 0.2 0.21821789 0. 0.40824829 0.21821789 - 0.21821789 -0.2 -0.6 -0.40824829] - -mean value: 0.10546536707079768 - -key: train_mcc -value: [0.51161666 0.4260261 0.35564338 0.42263985 0.4260261 0.44992127 - 0.4472136 0.40249224 0.42222222 0.4454354 ] - -mean value: 0.4309236824763807 - -key: test_fscore -value: [1. 0.6 0.5 0.54545455 0.72727273 0.5 - 0.66666667 0.4 0.2 0.36363636] - -mean value: 0.5503030303030303 - -key: train_fscore -value: [0.75 0.69047619 0.6741573 0.70454545 0.69047619 0.69879518 - 0.70588235 0.68235294 0.71111111 0.71264368] - -mean value: 0.7020440402981192 - -key: test_precision -value: [1. 0.6 0.66666667 0.5 0.66666667 0.66666667 - 0.57142857 0.4 0.2 0.33333333] - -mean value: 0.5604761904761905 - -key: train_precision -value: [0.76744186 0.74358974 0.68181818 0.72093023 0.74358974 0.76315789 - 0.75 0.725 0.71111111 0.73809524] - -mean value: 0.7344734005964116 - -key: test_recall -value: [1. 0.6 0.4 0.6 0.8 0.4 0.8 0.4 0.2 0.4] - -mean value: 0.56 - -key: train_recall -value: [0.73333333 0.64444444 0.66666667 0.68888889 0.64444444 0.64444444 - 0.66666667 0.64444444 0.71111111 0.68888889] - -mean value: 0.6733333333333335 - -key: test_accuracy -value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3] - -mean value: 0.55 - -key: train_accuracy -value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222 - 0.72222222 0.7 0.71111111 0.72222222] - -mean value: 0.7144444444444444 - -key: test_roc_auc -value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3] - -mean value: 0.55 - -key: train_roc_auc -value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222 - 0.72222222 0.7 0.71111111 0.72222222] - -mean value: 0.7144444444444444 - -key: test_jcc -value: [1. 0.42857143 0.33333333 0.375 0.57142857 0.33333333 - 0.5 0.25 0.11111111 0.22222222] - -mean value: 0.4125 - -key: train_jcc -value: [0.6 0.52727273 0.50847458 0.54385965 0.52727273 0.53703704 - 0.54545455 0.51785714 0.55172414 0.55357143] - -mean value: 0.5412523971790637 - -key: TN -value: 27 - -mean value: 27.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.14 - -Accuracy on Blind test: 0.48 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01105547 0.01381016 0.01291037 0.01365805 0.01394439 0.01359439 - 0.01429915 0.01413941 0.01459932 0.01359248] - -mean value: 0.013560318946838379 - -key: score_time -value: [0.00817347 0.0110867 0.01157665 0.01226854 0.01200342 0.01189685 - 0.01202917 0.01192331 0.01188517 0.01133728] - -mean value: 0.01141805648803711 - -key: test_mcc -value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 - 0.33333333 0.81649658 0.65465367 0. ] - -mean value: 0.6184941178020693 - -key: train_mcc -value: [0.97801929 0.93541435 0.97801929 0.89442719 0.97801929 0.97801929 - 0.79772404 0.97801929 1. 0.97801929] - -mean value: 0.9495681335972777 - -key: test_fscore -value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 - 0.71428571 0.90909091 0.83333333 0.54545455] - -mean value: 0.8191053391053391 - -key: train_fscore -value: [0.98876404 0.96774194 0.98901099 0.94736842 0.98876404 0.98901099 - 0.9 0.98901099 1. 0.98901099] - -mean value: 0.9748682402468098 - -key: test_precision -value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 - 0.55555556 0.83333333 0.71428571 0.5 ] - -mean value: 0.7546031746031747 - -key: train_precision -value: [1. 0.9375 0.97826087 0.9 1. 0.97826087 - 0.81818182 0.97826087 1. 0.97826087] - -mean value: 0.9568725296442688 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.6] - -mean value: 0.9199999999999999 - -key: train_recall -value: [0.97777778 1. 1. 1. 0.97777778 1. - 1. 1. 1. 1. ] - -mean value: 0.9955555555555555 - -key: test_accuracy -value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5] - -mean value: 0.79 - -key: train_accuracy -value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889 - 0.88888889 0.98888889 1. 0.98888889] - -mean value: 0.9733333333333334 - -key: test_roc_auc -value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5] - -mean value: 0.79 - -key: train_roc_auc -value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889 - 0.88888889 0.98888889 1. 0.98888889] - -mean value: 0.9733333333333333 - -key: test_jcc -value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 - 0.55555556 0.83333333 0.71428571 0.375 ] - -mean value: 0.7087698412698413 - -key: train_jcc -value: [0.97777778 0.9375 0.97826087 0.9 0.97777778 0.97826087 - 0.81818182 0.97826087 1. 0.97826087] - -mean value: 0.9524280851998244 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.31 - -Accuracy on Blind test: 0.65 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00869608 0.01205468 0.0119195 0.01242995 0.01249123 0.01260424 - 0.0124619 0.01284695 0.01233792 0.01319003] - -mean value: 0.01210324764251709 - -key: score_time -value: [0.00816989 0.01123571 0.0111959 0.01133227 0.0114491 0.01152349 - 0.01132083 0.01165223 0.01228309 0.01179123] - -mean value: 0.01119537353515625 - -key: test_mcc -value: [1. 0.5 0.81649658 0.65465367 0.65465367 0.65465367 - 1. 0.33333333 0.40824829 0.5 ] - -mean value: 0.6522039216848854 - -key: train_mcc -value: [0.95650071 0.91201231 1. 1. 0.93541435 1. - 0.91201231 0.39223227 0.82962978 0.72486118] - -mean value: 0.8662662908352459 - -key: test_fscore -value: [1. 0.76923077 0.88888889 0.83333333 0.75 0.83333333 - 1. 0.33333333 0.72727273 0.76923077] - -mean value: 0.7904623154623154 - -key: train_fscore -value: [0.97826087 0.95652174 1. 1. 0.96551724 1. - 0.95454545 0.42105263 0.9047619 0.86538462] - -mean value: 0.9046044456345884 - -key: test_precision -value: [1. 0.625 1. 0.71428571 1. 0.71428571 - 1. 1. 0.66666667 0.625 ] - -mean value: 0.8345238095238094 - -key: train_precision -value: [0.95744681 0.93617021 1. 1. 1. 1. - 0.97674419 1. 0.97435897 0.76271186] - -mean value: 0.9607432046088862 - -key: test_recall -value: [1. 1. 0.8 1. 0.6 1. 1. 0.2 0.8 1. ] - -mean value: 0.8399999999999999 - -key: train_recall -value: [1. 0.97777778 1. 1. 0.93333333 1. - 0.93333333 0.26666667 0.84444444 1. ] - -mean value: 0.8955555555555555 - -key: test_accuracy -value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7] - -mean value: 0.8 - -key: train_accuracy -value: [0.97777778 0.95555556 1. 1. 0.96666667 1. - 0.95555556 0.63333333 0.91111111 0.84444444] - -mean value: 0.9244444444444445 - -key: test_roc_auc -value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7] - -mean value: 0.8 - -key: train_roc_auc -value: [0.97777778 0.95555556 1. 1. 0.96666667 1. - 0.95555556 0.63333333 0.91111111 0.84444444] - -mean value: 0.9244444444444445 - -key: test_jcc -value: [1. 0.625 0.8 0.71428571 0.6 0.71428571 - 1. 0.2 0.57142857 0.625 ] - -mean value: 0.6849999999999999 - -key: train_jcc -value: [0.95744681 0.91666667 1. 1. 0.93333333 1. - 0.91304348 0.26666667 0.82608696 0.76271186] - -mean value: 0.8575955774366694 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.65 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.0937829 0.08539081 0.08131576 0.08085942 0.081738 0.07984424 - 0.08620453 0.08208632 0.08311081 0.0840354 ] - -mean value: 0.08383681774139404 - -key: score_time -value: [0.01697683 0.01498771 0.01476073 0.0150857 0.01448298 0.01486278 - 0.01463366 0.01593375 0.0157733 0.01465559] - -mean value: 0.015215301513671875 - -key: test_mcc -value: [1. 0.65465367 0.81649658 0.81649658 0.81649658 0.65465367 - 1. 0.81649658 0.6 1. ] - -mean value: 0.8175293665126858 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.83333333 0.88888889 0.90909091 0.90909091 0.83333333 - 1. 0.90909091 0.8 1. ] - -mean value: 0.9082828282828282 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.71428571 1. 0.83333333 0.83333333 0.71428571 - 1. 0.83333333 0.8 1. ] - -mean value: 0.8728571428571428 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 1. 1. 1. 1. 0.8 1. ] - -mean value: 0.96 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ] - -mean value: 0.9 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ] - -mean value: 0.9 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.71428571 0.8 0.83333333 0.83333333 0.71428571 - 1. 0.83333333 0.66666667 1. ] - -mean value: 0.8395238095238096 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 2 - -mean value: 2.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02795291 0.029001 0.03056097 0.02775407 0.02784705 0.02628183 - 0.02716231 0.02904415 0.03186536 0.02968788] - -mean value: 0.028715753555297853 - -key: score_time -value: [0.01913691 0.01803756 0.01878023 0.01808381 0.01785254 0.02040362 - 0.02372098 0.02432561 0.02418256 0.02507067] - -mean value: 0.02095944881439209 - -key: test_mcc -value: [1. 0.81649658 0.81649658 1. 0.81649658 0.81649658 - 0.81649658 0.81649658 0.81649658 1. ] - -mean value: 0.8715476066494082 - -key: train_mcc -value: [1. 1. 1. 0.97801929 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978019293843652 - -key: test_fscore -value: [1. 0.90909091 0.88888889 1. 0.88888889 0.90909091 - 0.90909091 0.90909091 0.88888889 1. ] - -mean value: 0.9303030303030303 - -key: train_fscore -value: [1. 1. 1. 0.98876404 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9988764044943821 - -key: test_precision -value: [1. 0.83333333 1. 1. 1. 0.83333333 - 0.83333333 0.83333333 1. 1. ] - -mean value: 0.9333333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 1. ] - -mean value: 0.9400000000000001 - -key: train_recall -value: [1. 1. 1. 0.97777778 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: test_accuracy -value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ] - -mean value: 0.93 - -key: train_accuracy -value: [1. 1. 1. 0.98888889 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ] - -mean value: 0.93 - -key: train_roc_auc -value: [1. 1. 1. 0.98888889 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9988888888888889 - -key: test_jcc -value: [1. 0.83333333 0.8 1. 0.8 0.83333333 - 0.83333333 0.83333333 0.8 1. ] - -mean value: 0.8733333333333334 - -key: train_jcc -value: [1. 1. 1. 0.97777778 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9977777777777778 - -key: TN -value: 46 - -mean value: 46.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/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: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.01426291 0.01899099 0.06119943 0.03039908 0.03261042 0.03212762 - 0.01616931 0.01589775 0.01599836 0.0489819 ] - -mean value: 0.028663778305053712 - -key: score_time -value: [0.01212502 0.01236367 0.01209068 0.01212645 0.01205349 0.01278925 - 0.01178861 0.01174092 0.01172686 0.01259255] - -mean value: 0.01213974952697754 - -key: test_mcc -value: [0.21821789 0.81649658 0.40824829 0.65465367 0.65465367 1. - 0.5 0.6 0.2 0.21821789] - -mean value: 0.5270487993279529 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.90909091 0.72727273 0.83333333 0.75 1. - 0.76923077 0.8 0.6 0.66666667] - -mean value: 0.7555594405594406 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.83333333 0.66666667 0.71428571 1. 1. - 0.625 0.8 0.6 0.57142857] - -mean value: 0.7477380952380951 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.4 1. 0.8 1. 0.6 1. 1. 0.8 0.6 0.8] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6] - -mean value: 0.7499999999999999 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6] - -mean value: 0.75 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.33333333 0.83333333 0.57142857 0.71428571 0.6 1. - 0.625 0.66666667 0.42857143 0.5 ] - -mean value: 0.6272619047619048 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 40 - -mean value: 40.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.52 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.19596338 0.1739645 0.1458776 0.17693067 0.1970973 0.17478251 - 0.19787335 0.19073057 0.1710403 0.17166138] - -mean value: 0.17959215641021728 - -key: score_time -value: [0.00906062 0.0088172 0.00977397 0.00960827 0.00882506 0.00957179 - 0.00906038 0.00920892 0.00884843 0.00902319] - -mean value: 0.009179782867431641 - -key: test_mcc -value: [1. 0.81649658 0.81649658 0.81649658 1. 0.65465367 - 0.81649658 0.81649658 1. 1. ] - -mean value: 0.8737136575346607 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.90909091 0.88888889 0.90909091 1. 0.83333333 - 0.90909091 0.90909091 1. 1. ] - -mean value: 0.9358585858585858 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 0.83333333 1. 0.71428571 - 0.83333333 0.83333333 1. 1. ] - -mean value: 0.9047619047619048 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ] - -mean value: 0.9800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ] - -mean value: 0.93 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ] - -mean value: 0.93 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.83333333 0.8 0.83333333 1. 0.71428571 - 0.83333333 0.83333333 1. 1. ] - -mean value: 0.8847619047619049 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 1 - -mean value: 1.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.98 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01014853 0.01743078 0.01398611 0.01395059 0.0140667 0.01543117 - 0.01630807 0.03985023 0.01398015 0.01428962] - -mean value: 0.016944193840026857 - -key: score_time -value: [0.01163626 0.01192975 0.01302242 0.01280975 0.01169062 0.01553082 - 0.01562738 0.0132699 0.01170063 0.01315784] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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)) - -mean value: 0.013037538528442383 - -key: test_mcc -value: [0.5 1. 0.81649658 0.81649658 0.65465367 0.81649658 - 1. 0.65465367 0.5 0.65465367] - -mean value: 0.7413450754907109 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.57142857 1. 0.88888889 0.88888889 0.75 0.88888889 - 1. 0.75 0.57142857 0.75 ] - -mean value: 0.805952380952381 - -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. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.4 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6] - -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: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8] - -mean value: 0.85 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8] - -mean value: 0.85 - -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 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6] - -mean value: 0.7 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 50 - -mean value: 50.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 0 - -mean value: 0.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.65 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('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: [0.02866507 0.03145432 0.03152037 0.03154778 0.03143167 0.03134251 - 0.03164673 0.03147626 0.03186297 0.03150988] - -mean value: 0.03124575614929199 - -key: score_time -value: [0.01959229 0.0194881 0.0223341 0.01981282 0.01142001 0.01322937 - 0.01942945 0.02004266 0.02273679 0.02062583] - -mean value: 0.018871140480041505 - -key: test_mcc -value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 - 0.81649658 0.81649658 0.6 0. ] - -mean value: 0.661345075490711 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 0.97801929 1. 1. 0.97801929] - -mean value: 0.9956038587687303 - -key: test_fscore -value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 - 0.90909091 0.88888889 0.8 0.54545455] - -mean value: 0.8332323232323233 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 0.98901099 1. 1. 0.98901099] - -mean value: 0.9978021978021978 - -key: test_precision -value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 - 0.83333333 1. 0.8 0.5 ] - -mean value: 0.8076190476190476 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.97826087 1. 1. 0.97826087] - -mean value: 0.9956521739130434 - -key: test_recall -value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6] - -mean value: 0.8800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5] - -mean value: 0.82 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 0.98888889 1. 1. 0.98888889] - -mean value: 0.9977777777777778 - -key: test_roc_auc -value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5] - -mean value: 0.82 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 0.98888889 1. 1. 0.98888889] - -mean value: 0.9977777777777778 - -key: test_jcc -value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 - 0.83333333 0.8 0.66666667 0.375 ] - -mean value: 0.728452380952381 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 0.97826087 1. 1. 0.97826087] - -mean value: 0.9956521739130434 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: 0.16 - -Accuracy on Blind test: 0.62 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=167)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.18429089 0.17889333 0.2217567 0.20420456 0.27858353 0.26535463 - 0.194134 0.18192577 0.20066214 0.18395448] - -mean value: 0.20937600135803222 - -key: score_time -value: [0.02141452 0.0196619 0.02247691 0.02075529 0.01194358 0.02149749 - 0.02219009 0.02246118 0.0197928 0.0219295 ] - -mean value: 0.020412325859069824 - -key: test_mcc -value: [1. 0.81649658 0.6 0.81649658 1. 0.21821789 - 0.81649658 0.6 0.6 0. ] - -mean value: 0.6467707633019171 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.97801929] - -mean value: 0.9978019293843652 - -key: test_fscore -value: [1. 0.90909091 0.8 0.90909091 1. 0.66666667 - 0.90909091 0.8 0.8 0.54545455] - -mean value: 0.833939393939394 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.98901099] - -mean value: 0.9989010989010989 - -key: test_precision -value: [1. 0.83333333 0.8 0.83333333 1. 0.57142857 - 0.83333333 0.8 0.8 0.5 ] - -mean value: 0.7971428571428572 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.97826087] - -mean value: 0.9978260869565216 - -key: test_recall -value: [1. 1. 0.8 1. 1. 0.8 1. 0.8 0.8 0.6] - -mean value: 0.8800000000000001 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5] -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: 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 - rouC_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: 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 - rouC_CV['Resampling'] = rs_rouC -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: 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 - rouC_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: 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 - rouC_BT['Resampling'] = rs_rouC - -mean value: 0.82 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.98888889] - -mean value: 0.9988888888888889 - -key: test_roc_auc -value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5] - -mean value: 0.82 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.98888889] - -mean value: 0.9988888888888889 - -key: test_jcc -value: [1. 0.83333333 0.66666667 0.83333333 1. 0.5 - 0.83333333 0.66666667 0.66666667 0.375 ] - -mean value: 0.7375 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.97826087] - -mean value: 0.9978260869565216 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 44 - -mean value: 44.0 - -key: trainingY_neg -value: 50 - -mean value: 50.0 - -key: trainingY_pos -value: 50 - -mean value: 50.0 - -key: blindY_neg -value: 26 - -mean value: 26.0 - -key: blindY_pos -value: 14 - -mean value: 14.0 - -MCC on Blind test: -0.03 - -Accuracy on Blind test: 0.52 - -PASS: sorting df by score that is mapped onto the order I want - -Concatenating dfs with different resampling methods [WF]: 70/30 -No. of dfs combining: 10 -The sampling methods are: - Resampling -Logistic Regression none -Logistic Regression smnc -Logistic Regression ros -Logistic Regression rus -Logistic Regression rouC - -PASS: 10 dfs successfully combined -nrows in combined_df_wf: 240 -ncols in combined_df_wf: 9 - -Concatenating dfs with different resampling methods: 70/30 -No. of dfs combining: 5 -The sampling methods are: - Resampling training_size -0 none 79 -24 smnc 100 -48 ros 100 -72 rus 58 -96 rouC 100 - -PASS: 5 dfs successfully combined -nrows in combined_df: 120 -ncols in combined_df: 32 - -File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_7030.csv - -File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_ext_7030.csv diff --git a/scripts/ml/log_katg_7030.txt b/scripts/ml/log_katg_7030.txt deleted file mode 100644 index f99e56d..0000000 --- a/scripts/ml/log_katg_7030.txt +++ /dev/null @@ -1,25943 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -/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/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -1.22.4 -1.4.1 - -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: 817 - -PASS: my_features_df and aa_df successfully combined -nrows: 817 -ncols: 269 -count of NULL values before imputation - -or_mychisq 244 -log10_or_mychisq 244 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 174 - -PASS: ML data with input features, training and test generated... - -Total no. of input features: 174 ---------No. of numerical features: 168 ---------No. of categorical features: 6 - -Total no. of evolutionary features: 3 - -Total no. of stability features: 28 ---------Common stabilty cols: 5 ---------Foldx cols: 23 - -Total no. of affinity features: 5 ---------Common affinity cols: 3 ---------Gene specific affinity cols: 2 - -Total no. of residue level features: 132 ---------AA index cols: 123 ---------Residue Prop cols: 3 ---------AA change Prop cols: 6 - -Total no. of genomic features: 6 ---------MAF+OR cols: 2 ---------Lineage cols: 4 ---------Other cols: 0 - -------------------------------------------------------------- -Successfully split data: ALL features -actual values: training set -imputed values: blind test set - -Total data size: 467 - -Train data size: (312, 174) -y_train numbers: Counter({1: 206, 0: 106}) - -Test data size: (155, 174) -y_test_numbers: Counter({1: 103, 0: 52}) - -y_train ratio: 0.5145631067961165 -y_test ratio: 0.5048543689320388 -------------------------------------------------------------- - -index: 0 -ind: 1 - -Mask count check: True - -index: 1 -ind: 2 - -Mask count check: True -Original Data - Counter({1: 206, 0: 106}) Data dim: (312, 174) -Simple Random OverSampling - Counter({1: 206, 0: 206}) -(412, 174) -Simple Random UnderSampling - Counter({0: 106, 1: 106}) -(212, 174) -Simple Combined Over and UnderSampling - Counter({0: 206, 1: 206}) -(412, 174) - -SMOTE_NC OverSampling - Counter({1: 206, 0: 206}) -(412, 174) - -##################################################################### - -Running ML analysis: feature groups -Gene name: katG -Drug name: isoniazid - -Output directory: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/ - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -key: fit_time -value: [0.03077984 0.03297734 0.03392029 0.03392172 0.03496599 0.03346992 - 0.03359389 0.03486037 0.03373337 0.03370595] - -mean value: 0.03359286785125733 - -key: score_time -value: [0.0124476 0.01199484 0.01336312 0.01245666 0.0124948 0.01370454 - 0.01242924 0.01259065 0.012465 0.01205707] - -mean value: 0.012600350379943847 - -key: test_mcc -value: [0.79772404 0.78959188 0.77484502 0.77484502 0.77484502 0.70992957 - 0.78625916 0.64203411 0.79524277 0.79524277] - -mean value: 0.7640559345359147 - -key: train_mcc -value: [0.86371491 0.89599932 0.8564766 0.87250517 0.88150779 0.86449998 - 0.87995543 0.85567237 0.85594873 0.90448686] - -mean value: 0.8730767160189149 - -key: test_fscore -value: [0.93333333 0.93023256 0.93023256 0.93023256 0.93023256 0.91304348 - 0.92682927 0.87804878 0.93023256 0.93023256] - -mean value: 0.9232650209211899 - -key: train_fscore -value: [0.95514512 0.96551724 0.95238095 0.95744681 0.96062992 0.95466667 - 0.96042216 0.95263158 0.95287958 0.96842105] - -mean value: 0.9580141085250089 - -key: test_precision -value: [0.875 0.90909091 0.90909091 0.90909091 0.90909091 0.84 - 0.9047619 0.85714286 0.86956522 0.86956522] - -mean value: 0.8852398833051007 - -key: train_precision -value: [0.93298969 0.94791667 0.93264249 0.94240838 0.93367347 0.94210526 - 0.94300518 0.93298969 0.92857143 0.94845361] - -mean value: 0.93847558628316 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 1. - 0.95 0.9 1. 1. ] - -mean value: 0.9659523809523808 - -key: train_recall -value: [0.97837838 0.98378378 0.97297297 0.97297297 0.98918919 0.96756757 - 0.97849462 0.97311828 0.97849462 0.98924731] - -mean value: 0.9784219703574543 - -key: test_accuracy -value: [0.90625 0.90625 0.90322581 0.90322581 0.90322581 0.87096774 - 0.90322581 0.83870968 0.90322581 0.90322581] - -mean value: 0.8941532258064514 - -key: train_accuracy -value: [0.93928571 0.95357143 0.93594306 0.9430605 0.94661922 0.93950178 - 0.94661922 0.93594306 0.93594306 0.95729537] - -mean value: 0.9433782409761058 - -key: test_roc_auc -value: [0.86363636 0.88528139 0.87619048 0.87619048 0.87619048 0.8 - 0.88409091 0.81363636 0.86363636 0.86363636] - -mean value: 0.8602489177489178 - -key: train_roc_auc -value: [0.92076814 0.93926031 0.91877815 0.92919482 0.92688626 0.92649212 - 0.93135257 0.91813809 0.9155631 0.94199208] - -mean value: 0.9268425641260956 - -key: test_jcc -value: [0.875 0.86956522 0.86956522 0.86956522 0.86956522 0.84 - 0.86363636 0.7826087 0.86956522 0.86956522] - -mean value: 0.8578636363636363 - -key: train_jcc -value: [0.91414141 0.93333333 0.90909091 0.91836735 0.92424242 0.91326531 - 0.92385787 0.90954774 0.91 0.93877551] - -mean value: 0.9194621850787158 - -key: TN -value: 80 - -mean value: 80.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 199 - -mean value: 199.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.91 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.70210862 0.69636774 0.83434844 0.7121985 0.71469593 0.83674455 - 0.72348166 0.69391394 0.79157877 0.70643449] - -mean value: 0.7411872625350953 - -key: score_time -value: [0.01284766 0.01272511 0.01273108 0.01270127 0.0127151 0.01370049 - 0.0127027 0.01273274 0.01376271 0.01266599] - -mean value: 0.012928485870361328 - -key: test_mcc -value: [1. 0.93154098 0.86831345 0.69695062 1. 0.78625916 - 0.65635466 0.73603286 0.78625916 0.86243936] - -mean value: 0.8324150242970061 - -key: train_mcc -value: [0.98411246 1. 1. 0.98422269 0.99210029 1. - 1. 1. 1. 0.98414076] - -mean value: 0.9944576204129685 - -key: test_fscore -value: [1. 0.97674419 0.95 0.90909091 1. 0.92682927 - 0.87179487 0.89473684 0.92682927 0.95238095] - -mean value: 0.9408406298003875 - -key: train_fscore -value: [0.99462366 1. 1. 0.99462366 0.99730458 1. - 1. 1. 1. 0.99465241] - -mean value: 0.9981204300455312 - -key: test_precision -value: [1. 0.95454545 1. 0.86956522 1. 0.95 - 0.89473684 0.94444444 0.9047619 0.90909091] - -mean value: 0.9427144772339279 - -key: train_precision -value: [0.98930481 1. 1. 0.98930481 0.99462366 1. - 1. 1. 1. 0.9893617 ] - -mean value: 0.9962594983710087 - -key: test_recall -value: [1. 1. 0.9047619 0.95238095 1. 0.9047619 - 0.85 0.85 0.95 1. ] - -mean value: 0.9411904761904761 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.96875 0.93548387 0.87096774 1. 0.90322581 - 0.83870968 0.87096774 0.90322581 0.93548387] - -mean value: 0.9226814516129032 - -key: train_accuracy -value: [0.99285714 1. 1. 0.99288256 0.99644128 1. - 1. 1. 1. 0.99288256] - -mean value: 0.9975063548551093 - -key: test_roc_auc -value: [1. 0.95454545 0.95238095 0.82619048 1. 0.90238095 - 0.83409091 0.87954545 0.88409091 0.90909091] - -mean value: 0.9142316017316017 - -key: train_roc_auc -value: [0.98947368 1. 1. 0.98958333 0.99479167 1. - 1. 1. 1. 0.98947368] - -mean value: 0.9963322368421054 - -key: test_jcc -value: [1. 0.95454545 0.9047619 0.83333333 1. 0.86363636 - 0.77272727 0.80952381 0.86363636 0.90909091] - -mean value: 0.8911255411255411 - -key: train_jcc -value: [0.98930481 1. 1. 0.98930481 0.99462366 1. - 1. 1. 1. 0.9893617 ] - -mean value: 0.9962594983710087 - -key: TN -value: 92 - -mean value: 92.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 194 - -mean value: 194.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.0132184 0.01286697 0.00963426 0.00930786 0.00908041 0.00924706 - 0.00901842 0.00894332 0.00898361 0.00899673] - -mean value: 0.009929704666137695 - -key: score_time -value: [0.01198387 0.01038861 0.00893116 0.00865841 0.00870872 0.00867224 - 0.00869894 0.00875521 0.00867367 0.00875235] - -mean value: 0.009222316741943359 - -key: test_mcc -value: [0.47306844 0.39072951 0.69695062 0.20935895 0.7047619 0.36059915 - 0.29545455 0.27532188 0.48992888 0.40572206] - -mean value: 0.4301895937784511 - -key: train_mcc -value: [0.41534408 0.48082512 0.4751815 0.49567937 0.41755413 0.46123131 - 0.46864391 0.47740752 0.47255597 0.47217054] - -mean value: 0.46365934504124107 - -key: test_fscore -value: [0.8 0.7804878 0.90909091 0.73170732 0.9047619 0.82608696 - 0.75 0.7027027 0.84444444 0.81818182] - -mean value: 0.8067463857654736 - -key: train_fscore -value: [0.80952381 0.79300292 0.82849604 0.832 0.79891304 0.82228117 - 0.81940701 0.81643836 0.82352941 0.82939633] - -mean value: 0.8172988079253738 - -key: test_precision -value: [0.84210526 0.8 0.86956522 0.75 0.9047619 0.76 - 0.75 0.76470588 0.76 0.75 ] - -mean value: 0.7951138267664045 - -key: train_precision -value: [0.79274611 0.86075949 0.80927835 0.82105263 0.80327869 0.80729167 - 0.82162162 0.83240223 0.81914894 0.81025641] - -mean value: 0.8177836147631308 - -key: test_recall -value: [0.76190476 0.76190476 0.95238095 0.71428571 0.9047619 0.9047619 - 0.75 0.65 0.95 0.9 ] - -mean value: 0.825 - -key: train_recall -value: [0.82702703 0.73513514 0.84864865 0.84324324 0.79459459 0.83783784 - 0.8172043 0.80107527 0.82795699 0.84946237] - -mean value: 0.8182185411217671 - -key: test_accuracy -value: [0.75 0.71875 0.87096774 0.64516129 0.87096774 0.74193548 - 0.67741935 0.64516129 0.77419355 0.74193548] - -mean value: 0.7436491935483871 - -key: train_accuracy -value: [0.74285714 0.74642857 0.76868327 0.77580071 0.7366548 0.76156584 - 0.76156584 0.76156584 0.76512456 0.76868327] - -mean value: 0.7588929842399593 - -key: test_roc_auc -value: [0.74458874 0.6991342 0.82619048 0.60714286 0.85238095 0.65238095 - 0.64772727 0.64318182 0.70227273 0.67727273] - -mean value: 0.7052272727272728 - -key: train_roc_auc -value: [0.7029872 0.75177809 0.73161599 0.74453829 0.7097973 0.72621059 - 0.73491794 0.7426429 0.73503113 0.72999434] - -mean value: 0.7309513758240413 - -key: test_jcc -value: [0.66666667 0.64 0.83333333 0.57692308 0.82608696 0.7037037 - 0.6 0.54166667 0.73076923 0.69230769] - -mean value: 0.681145732689211 - -key: train_jcc -value: [0.68 0.65700483 0.70720721 0.71232877 0.66515837 0.6981982 - 0.69406393 0.68981481 0.7 0.70852018] - -mean value: 0.6912296295614943 - -key: TN -value: 62 - -mean value: 62.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 44 - -mean value: 44.0 - -key: TP -value: 170 - -mean value: 170.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.7 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00950885 0.00936079 0.01019502 0.00956726 0.01026011 0.00957608 - 0.00969887 0.00971508 0.00942612 0.01114821] - -mean value: 0.009845638275146484 - -key: score_time -value: [0.00886631 0.00886059 0.00883245 0.00917625 0.00903559 0.00905657 - 0.00917363 0.00900912 0.00908494 0.01070547] - -mean value: 0.009180092811584472 - -key: test_mcc -value: [ 0.4133805 0.15803489 0.01471225 0.35192842 0.45253757 0.09967105 - -0.07802347 0.14863011 0.22469871 0.14863011] - -mean value: 0.19342001287335014 - -key: train_mcc -value: [0.38595876 0.40659146 0.40169892 0.42341022 0.35612129 0.33100274 - 0.36505417 0.35193106 0.33525911 0.35908657] - -mean value: 0.3716114301409626 - -key: test_fscore -value: [0.83333333 0.76595745 0.69767442 0.83333333 0.84444444 0.8 - 0.68181818 0.75555556 0.7826087 0.75555556] - -mean value: 0.775028096510574 - -key: train_fscore -value: [0.83018868 0.82758621 0.82640587 0.82793017 0.80604534 0.81339713 - 0.82380952 0.81372549 0.81622912 0.81751825] - -mean value: 0.8202835777038959 - -key: test_precision -value: [0.74074074 0.69230769 0.68181818 0.74074074 0.79166667 0.68965517 - 0.625 0.68 0.69230769 0.68 ] - -mean value: 0.7014236886995507 - -key: train_precision -value: [0.73640167 0.760181 0.75446429 0.76851852 0.75471698 0.72961373 - 0.73931624 0.74774775 0.73390558 0.74666667] - -mean value: 0.7471532421515537 - -key: test_recall -value: [0.95238095 0.85714286 0.71428571 0.95238095 0.9047619 0.95238095 - 0.75 0.85 0.9 0.85 ] - -mean value: 0.8683333333333334 - -key: train_recall -value: [0.95135135 0.90810811 0.91351351 0.8972973 0.86486486 0.91891892 - 0.93010753 0.89247312 0.91935484 0.90322581] - -mean value: 0.9099215344376634 - -key: test_accuracy -value: [0.75 0.65625 0.58064516 0.74193548 0.77419355 0.67741935 - 0.5483871 0.64516129 0.67741935 0.64516129] - -mean value: 0.6696572580645161 - -key: train_accuracy -value: [0.74285714 0.75 0.74733096 0.7544484 0.72597865 0.72241993 - 0.7366548 0.72953737 0.72597865 0.73309609] - -mean value: 0.7368301982714794 - -key: test_roc_auc -value: [0.65800866 0.56493506 0.50714286 0.62619048 0.70238095 0.52619048 - 0.46590909 0.56136364 0.58636364 0.56136364] - -mean value: 0.5759848484848484 - -key: train_roc_auc -value: [0.64409673 0.67510669 0.67029842 0.68823198 0.6615991 0.63133446 - 0.64400113 0.65149972 0.63336163 0.6516129 ] - -mean value: 0.6551142759907616 - -key: test_jcc -value: [0.71428571 0.62068966 0.53571429 0.71428571 0.73076923 0.66666667 - 0.51724138 0.60714286 0.64285714 0.60714286] - -mean value: 0.6356795503347227 - -key: train_jcc -value: [0.70967742 0.70588235 0.70416667 0.70638298 0.67510549 0.68548387 - 0.70040486 0.68595041 0.68951613 0.69135802] - -mean value: 0.6953928199132248 - -key: TN -value: 30 - -mean value: 30.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 76 - -mean value: 76.0 - -key: TP -value: 179 - -mean value: 179.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.31 - -Accuracy on Blind test: 0.7 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00896025 0.01062131 0.00921178 0.01032758 0.0118413 0.01038861 - 0.00920725 0.01012421 0.00997972 0.01029325] - -mean value: 0.010095524787902831 - -key: score_time -value: [0.04918337 0.01206517 0.01528883 0.01191854 0.01781154 0.01341414 - 0.01296878 0.01386476 0.01706791 0.01706243] - -mean value: 0.018064546585083007 - -key: test_mcc -value: [ 0.13261933 0.12434118 0.01471225 0.1667388 0.44786837 0.12245134 - 0.11978324 -0.02874798 0.41684569 0.17892962] - -mean value: 0.16955418600116953 - -key: train_mcc -value: [0.48779683 0.58110662 0.51362016 0.50738455 0.48727637 0.52197598 - 0.46868404 0.52014954 0.50198868 0.52763661] - -mean value: 0.5117619381388325 - -key: test_fscore -value: [0.72727273 0.7755102 0.69767442 0.74418605 0.85106383 0.75555556 - 0.71428571 0.66666667 0.80952381 0.74418605] - -mean value: 0.7485925018801247 - -key: train_fscore -value: [0.84020619 0.86821705 0.85063291 0.84615385 0.84343434 0.85353535 - 0.83756345 0.85279188 0.84848485 0.85642317] - -mean value: 0.8497443046584138 - -key: test_precision -value: [0.69565217 0.67857143 0.68181818 0.72727273 0.76923077 0.70833333 - 0.68181818 0.63636364 0.77272727 0.69565217] - -mean value: 0.7047439878961617 - -key: train_precision -value: [0.80295567 0.83168317 0.8 0.80487805 0.79146919 0.80094787 - 0.79326923 0.80769231 0.8 0.8056872 ] - -mean value: 0.8038582685986333 - -key: test_recall -value: [0.76190476 0.9047619 0.71428571 0.76190476 0.95238095 0.80952381 - 0.75 0.7 0.85 0.8 ] - -mean value: 0.8004761904761905 - -key: train_recall -value: [0.88108108 0.90810811 0.90810811 0.89189189 0.9027027 0.91351351 - 0.88709677 0.90322581 0.90322581 0.91397849] - -mean value: 0.9012932287125835 - -key: test_accuracy -value: [0.625 0.65625 0.58064516 0.64516129 0.77419355 0.64516129 - 0.61290323 0.5483871 0.74193548 0.64516129] - -mean value: 0.6474798387096775 - -key: train_accuracy -value: [0.77857143 0.81785714 0.79003559 0.78647687 0.77935943 0.79359431 - 0.77224199 0.79359431 0.78647687 0.79715302] - -mean value: 0.7895360955770209 - -key: test_roc_auc -value: [0.56277056 0.54329004 0.50714286 0.58095238 0.67619048 0.5547619 - 0.55681818 0.48636364 0.69772727 0.58181818] - -mean value: 0.5747835497835497 - -key: train_roc_auc -value: [0.73001422 0.77510669 0.73530405 0.73761261 0.72218468 0.73800676 - 0.7172326 0.74108659 0.73056027 0.74119977] - -mean value: 0.7368308248826076 - -key: test_jcc -value: [0.57142857 0.63333333 0.53571429 0.59259259 0.74074074 0.60714286 - 0.55555556 0.5 0.68 0.59259259] - -mean value: 0.6009100529100528 - -key: train_jcc -value: [0.72444444 0.76712329 0.74008811 0.73333333 0.72925764 0.74449339 - 0.72052402 0.74336283 0.73684211 0.74889868] - -mean value: 0.7388367838170675 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 41 - -mean value: 41.0 - -key: FN -value: 69 - -mean value: 69.0 - -key: TP -value: 165 - -mean value: 165.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.29 - -Accuracy on Blind test: 0.7 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01540208 0.01376987 0.01407671 0.01378703 0.0139339 0.01437092 - 0.01446509 0.01371384 0.01400924 0.01391745] - -mean value: 0.014144611358642579 - -key: score_time -value: [0.01087761 0.0103426 0.0101378 0.01008773 0.01020432 0.01038265 - 0.01006603 0.01003528 0.01035857 0.01014781] - -mean value: 0.010264039039611816 - -key: test_mcc -value: [0.57196944 0.49517597 0.55777335 0.47079191 0.53526436 0.26024784 - 0.4870862 0.31927949 0.44136741 0.56697057] - -mean value: 0.4705926550629849 - -key: train_mcc -value: [0.61185172 0.67361652 0.61915671 0.63587521 0.61345251 0.62176784 - 0.60608952 0.6620156 0.60390614 0.58699634] - -mean value: 0.6234728084389831 - -key: test_fscore -value: [0.86956522 0.85106383 0.875 0.8372093 0.86956522 0.80851064 - 0.8372093 0.8 0.83333333 0.86363636] - -mean value: 0.8445093204488575 - -key: train_fscore -value: [0.88235294 0.89876543 0.88279302 0.88721805 0.8817734 0.88395062 - 0.88135593 0.8960396 0.88077859 0.87651332] - -mean value: 0.8851540894305963 - -key: test_precision -value: [0.8 0.76923077 0.77777778 0.81818182 0.8 0.73076923 - 0.7826087 0.72 0.71428571 0.79166667] - -mean value: 0.7704520672564151 - -key: train_precision -value: [0.80717489 0.82727273 0.81944444 0.8271028 0.80995475 0.81363636 - 0.80176211 0.83027523 0.80444444 0.79735683] - -mean value: 0.8138424594648971 - -key: test_recall -value: [0.95238095 0.95238095 1. 0.85714286 0.95238095 0.9047619 - 0.9 0.9 1. 0.95 ] - -mean value: 0.9369047619047619 - -key: train_recall -value: [0.97297297 0.98378378 0.95675676 0.95675676 0.96756757 0.96756757 - 0.97849462 0.97311828 0.97311828 0.97311828] - -mean value: 0.9703254867770996 - -key: test_accuracy -value: [0.8125 0.78125 0.80645161 0.77419355 0.80645161 0.70967742 - 0.77419355 0.70967742 0.74193548 0.80645161] - -mean value: 0.7722782258064516 - -key: train_accuracy -value: [0.82857143 0.85357143 0.83274021 0.83985765 0.82918149 0.83274021 - 0.82562278 0.85053381 0.82562278 0.81850534] - -mean value: 0.8336947127605491 - -key: test_roc_auc -value: [0.74891775 0.7034632 0.7 0.72857143 0.72619048 0.60238095 - 0.72272727 0.63181818 0.63636364 0.74772727] - -mean value: 0.6948160173160174 - -key: train_roc_auc -value: [0.7601707 0.79189189 0.77525338 0.78567005 0.76503378 0.77024212 - 0.75240521 0.7918223 0.75498019 0.74445388] - -mean value: 0.7691923486517078 - -key: test_jcc -value: [0.76923077 0.74074074 0.77777778 0.72 0.76923077 0.67857143 - 0.72 0.66666667 0.71428571 0.76 ] - -mean value: 0.7316503866503867 - -key: train_jcc -value: [0.78947368 0.8161435 0.79017857 0.7972973 0.78854626 0.7920354 - 0.78787879 0.81165919 0.78695652 0.78017241] - -mean value: 0.7940341620667072 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 58 - -mean value: 58.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.81 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.16458178 1.28447604 1.16362858 1.27272463 1.20178652 1.13651323 - 1.22490501 1.13492298 1.25703454 1.13588333] - -mean value: 1.197645664215088 - -key: score_time -value: [0.01479721 0.01402593 0.01684952 0.01273608 0.01289916 0.01262927 - 0.01277757 0.01286125 0.01281571 0.01280928] - -mean value: 0.013520097732543946 - -key: test_mcc -value: [0.79772404 0.6457766 0.85238095 0.53924646 1. 0.61758068 - 0.71818182 0.65635466 0.93048421 0.79524277] - -mean value: 0.7552972189336914 - -key: train_mcc -value: [0.99204533 0.99204533 1. 0.99210029 1. 0.99210029 - 1. 1. 1. 0.99205967] - -mean value: 0.9960350909981239 - -key: test_fscore -value: [0.93333333 0.88888889 0.95238095 0.86363636 1. 0.88888889 - 0.9 0.87179487 0.97560976 0.93023256] - -mean value: 0.9204765613160395 - -key: train_fscore -value: [0.99730458 0.99730458 1. 0.99730458 1. 0.99730458 - 1. 1. 1. 0.99731903] - -mean value: 0.9986537363693516 - -key: test_precision -value: [0.875 0.83333333 0.95238095 0.82608696 1. 0.83333333 - 0.9 0.89473684 0.95238095 0.86956522] - -mean value: 0.8936817587446878 - -key: train_precision -value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366 - 1. 1. 1. 0.99465241] - -mean value: 0.9973147030073026 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.9047619 1. 0.95238095 - 0.9 0.85 1. 1. ] - -mean value: 0.9511904761904763 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.90625 0.84375 0.93548387 0.80645161 1. 0.83870968 - 0.87096774 0.83870968 0.96774194 0.90322581] - -mean value: 0.8911290322580644 - -key: train_accuracy -value: [0.99642857 0.99642857 1. 0.99644128 1. 0.99644128 - 1. 1. 1. 0.99644128] - -mean value: 0.9982180986273512 - -key: test_roc_auc -value: [0.86363636 0.79437229 0.92619048 0.75238095 1. 0.77619048 - 0.85909091 0.83409091 0.95454545 0.86363636] - -mean value: 0.8624134199134199 - -key: train_roc_auc -value: [0.99473684 0.99473684 1. 0.99479167 1. 0.99479167 - 1. 1. 1. 0.99473684] - -mean value: 0.9973793859649123 - -key: test_jcc -value: [0.875 0.8 0.90909091 0.76 1. 0.8 - 0.81818182 0.77272727 0.95238095 0.86956522] - -mean value: 0.8556946169772257 - -key: train_jcc -value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366 - 1. 1. 1. 0.99465241] - -mean value: 0.9973147030073026 - -key: TN -value: 82 - -mean value: 82.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 196 - -mean value: 196.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02068901 0.02152038 0.01389027 0.01603794 0.01353812 0.01289558 - 0.015836 0.01678038 0.01749015 0.01312494] - -mean value: 0.01618027687072754 - -key: score_time -value: [0.01225066 0.00925589 0.00909591 0.00866771 0.00862551 0.00864029 - 0.00862956 0.00868511 0.00961447 0.00894046] - -mean value: 0.009240555763244628 - -key: test_mcc -value: [0.87496729 0.93435318 0.93048421 0.78625916 1. 0.86831345 - 0.93048421 0.79476958 0.85909091 0.72821908] - -mean value: 0.8706941085318691 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95 0.97560976 0.97560976 0.92682927 1. 0.95 - 0.97560976 0.92307692 0.95 0.90909091] - -mean value: 0.9535826368753197 - -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. 0.95 1. 1. - 0.95238095 0.94736842 0.95 0.83333333] - -mean value: 0.9633082706766917 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.9047619 0.95238095 0.95238095 0.9047619 1. 0.9047619 - 1. 0.9 0.95 1. ] - -mean value: 0.9469047619047618 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.96875 0.96774194 0.90322581 1. 0.93548387 - 0.96774194 0.90322581 0.93548387 0.87096774] - -mean value: 0.9390120967741936 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 0.97619048 0.97619048 0.90238095 1. 0.95238095 - 0.95454545 0.90454545 0.92954545 0.81818182] - -mean value: 0.936634199134199 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.9047619 0.95238095 0.95238095 0.86363636 1. 0.9047619 - 0.95238095 0.85714286 0.9047619 0.83333333] - -mean value: 0.9125541125541126 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 195 - -mean value: 195.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.10506797 0.1029923 0.10362291 0.10310459 0.10460377 0.10338831 - 0.10361099 0.10267019 0.105057 0.10298061] - -mean value: 0.10370986461639405 - -key: score_time -value: [0.01752758 0.01787734 0.01751494 0.01744723 0.01748729 0.01749158 - 0.01751399 0.01741982 0.01745582 0.01747465] - -mean value: 0.017521023750305176 - -key: test_mcc -value: [0.78959188 0.57163505 0.61758068 0.36059915 0.47079191 0.36059915 - 0.56537691 0.49780905 0.5913124 0.57727273] - -mean value: 0.5402568904601063 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93023256 0.86363636 0.88888889 0.82608696 0.8372093 0.82608696 - 0.85714286 0.82926829 0.86956522 0.85 ] - -mean value: 0.8578117393250937 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.90909091 0.82608696 0.83333333 0.76 0.81818182 0.76 - 0.81818182 0.80952381 0.76923077 0.85 ] - -mean value: 0.8153629414064196 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 0.9047619 0.95238095 0.9047619 0.85714286 0.9047619 - 0.9 0.85 1. 0.85 ] - -mean value: 0.9076190476190475 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.90625 0.8125 0.83870968 0.74193548 0.77419355 0.74193548 - 0.80645161 0.77419355 0.80645161 0.80645161] - -mean value: 0.8009072580645162 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.88528139 0.77056277 0.77619048 0.65238095 0.72857143 0.65238095 - 0.76818182 0.74318182 0.72727273 0.78863636] - -mean value: 0.7492640692640691 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.86956522 0.76 0.8 0.7037037 0.72 0.7037037 - 0.75 0.70833333 0.76923077 0.73913043] - -mean value: 0.7523667162145423 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 187 - -mean value: 187.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.53 - -Accuracy on Blind test: 0.79 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00934625 0.00909376 0.00920677 0.00956249 0.00934076 0.00916028 - 0.00926018 0.00925374 0.00932503 0.00914741] - -mean value: 0.00926966667175293 - -key: score_time -value: [0.00877452 0.00852442 0.00861669 0.00886846 0.0085597 0.00864863 - 0.00862837 0.00879931 0.00861359 0.00859666] - -mean value: 0.008663034439086914 - -key: test_mcc -value: [ 0.52663543 0.50569367 -0.20763488 -0.09157015 0.62281846 0.28749445 - 0.51793973 0.3261463 0.24110987 0.15454545] - -mean value: 0.2883178338157986 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.82926829 0.8372093 0.66666667 0.68181818 0.88372093 0.8 - 0.82051282 0.73684211 0.71794872 0.7 ] - -mean value: 0.7673987017450612 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85 0.81818182 0.625 0.65217391 0.86363636 0.75 - 0.84210526 0.77777778 0.73684211 0.7 ] - -mean value: 0.761571724106049 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.80952381 0.85714286 0.71428571 0.71428571 0.9047619 0.85714286 - 0.8 0.7 0.7 0.7 ] - -mean value: 0.7757142857142857 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.78125 0.78125 0.51612903 0.5483871 0.83870968 0.70967742 - 0.77419355 0.67741935 0.64516129 0.61290323] - -mean value: 0.6885080645161291 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.76839827 0.74675325 0.40714286 0.45714286 0.80238095 0.62857143 - 0.76363636 0.66818182 0.62272727 0.57727273] - -mean value: 0.6442207792207792 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.70833333 0.72 0.5 0.51724138 0.79166667 0.66666667 - 0.69565217 0.58333333 0.56 0.53846154] - -mean value: 0.6281355091684926 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 55 - -mean value: 55.0 - -key: FP -value: 46 - -mean value: 46.0 - -key: FN -value: 51 - -mean value: 51.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.29 - -Accuracy on Blind test: 0.68 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -[1.42102885 1.41763186 1.43122268 1.39822125 1.40961742 1.39854026 - 1.42449594 1.49090695 1.44167662 1.44908071] - -mean value: 1.4282422542572022 - -key: score_time -value: [0.094944 0.09802151 0.15033007 0.09156108 0.09057498 0.09051704 - 0.09883428 0.09939861 0.09202886 0.09227395] - -mean value: 0.09984843730926514 - -key: test_mcc -value: [0.93154098 0.71797362 0.92687157 0.69695062 0.85238095 0.77484502 - 0.78625916 0.71390814 0.86243936 0.66057826] - -mean value: 0.7923747679070615 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97674419 0.90909091 0.97674419 0.90909091 0.95238095 0.93023256 - 0.92682927 0.9047619 0.95238095 0.88888889] - -mean value: 0.9327144715119757 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95454545 0.86956522 0.95454545 0.86956522 0.95238095 0.90909091 - 0.9047619 0.86363636 0.90909091 0.8 ] - -mean value: 0.8987182382834555 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095 - 0.95 0.95 1. 1. ] - -mean value: 0.9709523809523809 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.96875 0.875 0.96774194 0.87096774 0.93548387 0.90322581 - 0.90322581 0.87096774 0.93548387 0.83870968] - -mean value: 0.9069556451612902 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95454545 0.83982684 0.95 0.82619048 0.92619048 0.87619048 - 0.88409091 0.83863636 0.90909091 0.77272727] - -mean value: 0.8777489177489176 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95454545 0.83333333 0.95454545 0.83333333 0.90909091 0.86956522 - 0.86363636 0.82608696 0.90909091 0.8 ] - -mean value: 0.8753227931488802 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 83 - -mean value: 83.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.93 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.89417028 0.92537498 0.97537518 0.91410828 0.87176323 0.92093062 - 0.91085744 0.92615676 0.87906098 0.91220188] - -mean value: 0.912999963760376 - -key: score_time -value: [0.21468759 0.18967795 0.22059464 0.181077 0.24509573 0.19168997 - 0.2172718 0.18608809 0.19562244 0.18895364] - -mean value: 0.20307588577270508 - -key: test_mcc -value: [0.8643122 0.6457766 0.78262379 0.61758068 0.77484502 0.61758068 - 0.78625916 0.71390814 0.66057826 0.66057826] - -mean value: 0.7124042779002867 - -key: train_mcc -value: [0.95258202 0.94474539 0.94513672 0.95291644 0.93737406 0.94513672 - 0.95266247 0.96836384 0.95266247 0.95266247] - -mean value: 0.9504242616765979 - -key: test_fscore -value: [0.95454545 0.88888889 0.93333333 0.88888889 0.93023256 0.88888889 - 0.92682927 0.9047619 0.88888889 0.88888889] - -mean value: 0.9094146963517356 - -key: train_fscore -value: [0.98404255 0.98143236 0.98143236 0.98404255 0.97883598 0.98143236 - 0.98412698 0.9893617 0.98412698 0.98412698] - -mean value: 0.9832960821955685 - -key: test_precision -value: [0.91304348 0.83333333 0.875 0.83333333 0.90909091 0.83333333 - 0.9047619 0.86363636 0.8 0.8 ] - -mean value: 0.8565532655750048 - -key: train_precision -value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167 - 0.96875 0.97894737 0.96875 0.96875 ] - -mean value: 0.9671544366088091 - -key: test_recall -value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095 - 0.95 0.95 1. 1. ] - -mean value: 0.9709523809523809 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.84375 0.90322581 0.83870968 0.90322581 0.83870968 - 0.90322581 0.87096774 0.83870968 0.83870968] - -mean value: 0.8716733870967742 - -key: train_accuracy -value: [0.97857143 0.975 0.97508897 0.97864769 0.97153025 0.97508897 - 0.97864769 0.98576512 0.97864769 0.97864769] - -mean value: 0.9775635485510931 - -key: test_roc_auc -value: [0.90909091 0.79437229 0.85 0.77619048 0.87619048 0.77619048 - 0.88409091 0.83863636 0.77272727 0.77272727] - -mean value: 0.8250216450216449 - -key: train_roc_auc -value: [0.96842105 0.96315789 0.96354167 0.96875 0.95833333 0.96354167 - 0.96842105 0.97894737 0.96842105 0.96842105] - -mean value: 0.9669956140350877 - -key: test_jcc -value: [0.91304348 0.8 0.875 0.8 0.86956522 0.8 - 0.86363636 0.82608696 0.8 0.8 ] - -mean value: 0.8347332015810277 - -key: train_jcc -value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167 - 0.96875 0.97894737 0.96875 0.96875 ] - -mean value: 0.9671544366088091 - -key: TN -value: 72 - -mean value: 72.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 34 - -mean value: 34.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 13 -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=None, 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/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 -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.13504362 0.05386043 0.17046118 0.04772305 0.05267906 0.05315638 - 0.05455399 0.05298352 0.05491114 0.05289721] - -mean value: 0.07282695770263672 - -key: score_time -value: [0.01084018 0.01215386 0.01093411 0.01053691 0.01060319 0.0104351 - 0.01052928 0.01054263 0.0104084 0.01039553] - -mean value: 0.010737919807434082 - -key: test_mcc -value: [1. 1. 1. 0.85238095 0.93048421 0.93048421 - 0.79476958 0.93048421 0.85909091 0.72821908] - -mean value: 0.902591315825731 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.95238095 0.97560976 0.97560976 - 0.92307692 0.97560976 0.95 0.90909091] - -mean value: 0.9661378052841467 - -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. 0.95238095 1. 1. - 0.94736842 0.95238095 0.95 0.83333333] - -mean value: 0.963546365914787 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95238095 0.95238095 0.95238095 - 0.9 1. 0.95 1. ] - -mean value: 0.9707142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.93548387 0.96774194 0.96774194 - 0.90322581 0.96774194 0.93548387 0.87096774] - -mean value: 0.9548387096774194 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.92619048 0.97619048 0.97619048 - 0.90454545 0.95454545 0.92954545 0.81818182] - -mean value: 0.9485389610389611 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.90909091 0.95238095 0.95238095 - 0.85714286 0.95238095 0.9047619 0.83333333] - -mean value: 0.9361471861471863 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03230834 0.04915643 0.02894878 0.03100872 0.06014895 0.04913616 - 0.02832913 0.02888393 0.03995156 0.07339001] - -mean value: 0.04212620258331299 - -key: score_time -value: [0.02405763 0.01220226 0.01222754 0.01217437 0.02302003 0.01225495 - 0.01228237 0.01220059 0.02282333 0.01247096] - -mean value: 0.015571403503417968 - -key: test_mcc -value: [0.8643122 0.74100101 0.64203411 0.55714286 0.78625916 0.55714286 - 0.68174942 0.599404 0.79524277 0.56697057] - -mean value: 0.6791258953421403 - -key: train_mcc -value: [0.96809668 0.96809668 0.96831892 0.97623798 0.96057359 0.97636634 - 0.96815373 0.96819468 0.97611544 0.97611544] - -mean value: 0.9706269497086195 - -key: test_fscore -value: [0.95454545 0.9 0.87804878 0.85714286 0.92682927 0.85714286 - 0.86486486 0.84210526 0.93023256 0.86363636] - -mean value: 0.8874548267410315 - -key: train_fscore -value: [0.98924731 0.98924731 0.98924731 0.99191375 0.98644986 0.9919571 - 0.98930481 0.98924731 0.9919571 0.9919571 ] - -mean value: 0.9900528984948347 - -key: test_precision -value: [0.91304348 0.94736842 0.9 0.85714286 0.95 0.85714286 - 0.94117647 0.88888889 0.86956522 0.79166667] - -mean value: 0.891599485713431 - -key: train_precision -value: [0.98395722 0.98395722 0.98395722 0.98924731 0.98913043 0.98404255 - 0.98404255 0.98924731 0.98930481 0.98930481] - -mean value: 0.9866191448243959 - -key: test_recall -value: [1. 0.85714286 0.85714286 0.85714286 0.9047619 0.85714286 - 0.8 0.8 1. 0.95 ] - -mean value: 0.8883333333333333 - -key: train_recall -value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98378378 1. - 0.99462366 0.98924731 0.99462366 0.99462366] - -mean value: 0.9935280441732054 - -key: test_accuracy -value: [0.9375 0.875 0.83870968 0.80645161 0.90322581 0.80645161 - 0.83870968 0.80645161 0.90322581 0.80645161] - -mean value: 0.8522177419354838 - -key: train_accuracy -value: [0.98571429 0.98571429 0.98576512 0.98932384 0.98220641 0.98932384 - 0.98576512 0.98576512 0.98932384 0.98932384] - -mean value: 0.9868225724453483 - -key: test_roc_auc -value: [0.90909091 0.88311688 0.82857143 0.77857143 0.90238095 0.77857143 - 0.85454545 0.80909091 0.86363636 0.74772727] - -mean value: 0.8355303030303031 - -key: train_roc_auc -value: [0.98150782 0.98150782 0.9816723 0.98688063 0.98147523 0.984375 - 0.98152235 0.98409734 0.98678551 0.98678551] - -mean value: 0.983660951911164 - -key: test_jcc -value: [0.91304348 0.81818182 0.7826087 0.75 0.86363636 0.75 - 0.76190476 0.72727273 0.86956522 0.76 ] - -mean value: 0.7996213062300018 - -key: train_jcc -value: [0.9787234 0.9787234 0.9787234 0.98395722 0.97326203 0.98404255 - 0.97883598 0.9787234 0.98404255 0.98404255] - -mean value: 0.9803076506768622 - -key: TN -value: 83 - -mean value: 83.0 - -key: FP -value: 23 - -mean value: 23.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.92 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.0126524 0.0095458 0.00931358 0.0091176 0.00899625 0.00920296 - 0.00903535 0.00899458 0.00897455 0.00904608] - -mean value: 0.0094879150390625 - -key: score_time -value: [0.02371144 0.00912809 0.00880551 0.00858068 0.00853944 0.00854635 - 0.00848079 0.00854325 0.00861645 0.00864959] - -mean value: 0.010160160064697266 - -key: test_mcc -value: [0.33910216 0.13261933 0.53526436 0.06513389 0.49780905 0.35192842 - 0.33300791 0.01363636 0.40800555 0.33300791] - -mean value: 0.30095149600575316 - -key: train_mcc -value: [0.31603146 0.3455391 0.3767181 0.39130863 0.34815112 0.3313273 - 0.29256975 0.35900377 0.37052503 0.33144271] - -mean value: 0.3462616963153834 - -key: test_fscore -value: [0.75 0.72727273 0.86956522 0.68292683 0.82926829 0.83333333 - 0.79069767 0.65 0.82608696 0.79069767] - -mean value: 0.7749848705307533 - -key: train_fscore -value: [0.78974359 0.79586563 0.81683168 0.80927835 0.79691517 0.79177378 - 0.77720207 0.79792746 0.80512821 0.79695431] - -mean value: 0.7977620256045503 - -key: test_precision -value: [0.78947368 0.69565217 0.8 0.7 0.85 0.74074074 - 0.73913043 0.65 0.73076923 0.73913043] - -mean value: 0.7434896699198758 - -key: train_precision -value: [0.75121951 0.76237624 0.75342466 0.77339901 0.75980392 0.75490196 - 0.75 0.77 0.76960784 0.75480769] - -mean value: 0.7599540839929344 - -key: test_recall -value: [0.71428571 0.76190476 0.95238095 0.66666667 0.80952381 0.95238095 - 0.85 0.65 0.95 0.85 ] - -mean value: 0.8157142857142858 - -key: train_recall -value: [0.83243243 0.83243243 0.89189189 0.84864865 0.83783784 0.83243243 - 0.80645161 0.82795699 0.84408602 0.84408602] - -mean value: 0.8398256320836965 - -key: test_accuracy -value: [0.6875 0.625 0.80645161 0.58064516 0.77419355 0.74193548 - 0.70967742 0.5483871 0.74193548 0.70967742] - -mean value: 0.6925403225806452 - -key: train_accuracy -value: [0.70714286 0.71785714 0.7366548 0.7366548 0.71886121 0.71174377 - 0.69395018 0.72241993 0.72953737 0.71530249] - -mean value: 0.7190124555160142 - -key: test_roc_auc -value: [0.67532468 0.56277056 0.72619048 0.53333333 0.7547619 0.62619048 - 0.65227273 0.50681818 0.65681818 0.65227273] - -mean value: 0.6346753246753247 - -key: train_roc_auc -value: [0.64779516 0.66358464 0.66469595 0.68474099 0.66371059 0.65579955 - 0.64006791 0.67187323 0.67467459 0.65362196] - -mean value: 0.6620564563927254 - -key: test_jcc -value: [0.6 0.57142857 0.76923077 0.51851852 0.70833333 0.71428571 - 0.65384615 0.48148148 0.7037037 0.65384615] - -mean value: 0.63746743996744 - -key: train_jcc -value: [0.65254237 0.66094421 0.69037657 0.67965368 0.66239316 0.65531915 - 0.63559322 0.6637931 0.67381974 0.66244726] - -mean value: 0.6636882462571104 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 38 - -mean value: 38.0 - -key: FN -value: 58 - -mean value: 58.0 - -key: TP -value: 168 - -mean value: 168.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.71 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01832533 0.01673365 0.02225184 0.02035999 0.02189851 0.02132702 - 0.01896715 0.02250695 0.01972532 0.02366352] - -mean value: 0.020575928688049316 - -key: score_time -value: [0.01003265 0.01122832 0.01144624 0.01167583 0.0117116 0.01166582 - 0.01175475 0.01174879 0.01169896 0.01169562] - -mean value: 0.011465859413146973 - -key: test_mcc -value: [1. 0.79772404 0.93048421 0.77484502 1. 0.85238095 - 0.6310315 0.79524277 0.93048421 0.85909091] - -mean value: 0.8571283609822942 - -key: train_mcc -value: [0.96009907 0.93692544 0.98422269 0.96835586 0.97635661 0.97623798 - 0.88905141 0.97624243 0.96819468 0.96847885] - -mean value: 0.9604165025930188 - -key: test_fscore -value: [1. 0.93333333 0.97560976 0.93023256 1. 0.95238095 - 0.83333333 0.93023256 0.97560976 0.95 ] - -mean value: 0.948073224752181 - -key: train_fscore -value: [0.98652291 0.97883598 0.99462366 0.98918919 0.99186992 0.99191375 - 0.95821727 0.992 0.98924731 0.98918919] - -mean value: 0.9861609171532406 - -key: test_precision -value: [1. 0.875 1. 0.90909091 1. 0.95238095 - 0.9375 0.86956522 0.95238095 0.95 ] - -mean value: 0.9445918031244117 - -key: train_precision -value: [0.98387097 0.95854922 0.98930481 0.98918919 0.99456522 0.98924731 - 0.99421965 0.98412698 0.98924731 0.99456522] - -mean value: 0.9866885888307972 - -key: test_recall -value: [1. 1. 0.95238095 0.95238095 1. 0.95238095 - 0.75 1. 1. 0.95 ] - -mean value: 0.9557142857142857 - -key: train_recall -value: [0.98918919 1. 1. 0.98918919 0.98918919 0.99459459 - 0.92473118 1. 0.98924731 0.98387097] - -mean value: 0.9860011624527754 - -key: test_accuracy -value: [1. 0.90625 0.96774194 0.90322581 1. 0.93548387 - 0.80645161 0.90322581 0.96774194 0.93548387] - -mean value: 0.9325604838709678 - -key: train_accuracy -value: [0.98214286 0.97142857 0.99288256 0.98576512 0.98932384 0.98932384 - 0.94661922 0.98932384 0.98576512 0.98576512] - -mean value: 0.9818340111845449 - -key: test_roc_auc -value: [1. 0.86363636 0.97619048 0.87619048 1. 0.92619048 - 0.82954545 0.86363636 0.95454545 0.92954545] - -mean value: 0.921948051948052 - -key: train_roc_auc -value: [0.97880512 0.95789474 0.98958333 0.98417793 0.98938626 0.98688063 - 0.95710243 0.98421053 0.98409734 0.98667233] - -mean value: 0.979881063682528 - -key: test_jcc -value: [1. 0.875 0.95238095 0.86956522 1. 0.90909091 - 0.71428571 0.86956522 0.95238095 0.9047619 ] - -mean value: 0.9047030867683041 - -key: train_jcc -value: [0.97340426 0.95854922 0.98930481 0.97860963 0.98387097 0.98395722 - 0.9197861 0.98412698 0.9787234 0.97860963] - -mean value: 0.972894221392046 - -key: TN -value: 94 - -mean value: 94.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.64 - -Accuracy on Blind test: 0.8 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01601458 0.01648951 0.01605535 0.01551676 0.01533937 0.01700306 - 0.01540875 0.01559615 0.01406908 0.01540995] - -mean value: 0.015690255165100097 - -key: score_time -value: [0.01169801 0.01167488 0.01167607 0.01165557 0.011724 0.01172376 - 0.01167727 0.01172972 0.01162696 0.01171517] - -mean value: 0.011690139770507812 - -key: test_mcc -value: [0.6798418 0.8643122 0.81199794 0.47434165 0.93048421 0.69695062 - 0.599404 0.58316015 0.35410712 0.79476958] - -mean value: 0.6789369269903351 - -key: train_mcc -value: [0.73826562 0.96830875 0.9213983 0.42421821 0.90645458 0.9218965 - 0.94451335 0.94577703 0.52778998 0.86687445] - -mean value: 0.8165496770553766 - -key: test_fscore -value: [0.83333333 0.95454545 0.92307692 0.85714286 0.97560976 0.90909091 - 0.84210526 0.8 0.81632653 0.92307692] - -mean value: 0.88343079501341 - -key: train_fscore -value: [0.87951807 0.98930481 0.97282609 0.83710407 0.96721311 0.97368421 - 0.98113208 0.98092643 0.86111111 0.94972067] - -mean value: 0.9392540657250088 - -key: test_precision -value: [1. 0.91304348 1. 0.75 1. 0.86956522 - 0.88888889 0.93333333 0.68965517 0.94736842] - -mean value: 0.8991854511340822 - -key: train_precision -value: [0.99319728 0.97883598 0.97814208 0.71984436 0.97790055 0.94871795 - 0.98378378 0.99447514 0.75609756 0.98837209] - -mean value: 0.9319366769335261 - -key: test_recall -value: [0.71428571 1. 0.85714286 1. 0.95238095 0.95238095 - 0.8 0.7 1. 0.9 ] - -mean value: 0.8876190476190476 - -key: train_recall -value: [0.78918919 1. 0.96756757 1. 0.95675676 1. - 0.97849462 0.96774194 1. 0.91397849] - -mean value: 0.9573728567276956 - -key: test_accuracy -value: [0.8125 0.9375 0.90322581 0.77419355 0.96774194 0.87096774 - 0.80645161 0.77419355 0.70967742 0.90322581] - -mean value: 0.8459677419354839 - -key: train_accuracy -value: [0.85714286 0.98571429 0.96441281 0.74377224 0.95729537 0.96441281 - 0.97508897 0.97508897 0.78647687 0.93594306] - -mean value: 0.9145348246059989 - -key: test_roc_auc -value: [0.85714286 0.90909091 0.92857143 0.65 0.97619048 0.82619048 - 0.80909091 0.80454545 0.59090909 0.90454545] - -mean value: 0.8256277056277057 - -key: train_roc_auc -value: [0.88933144 0.97894737 0.96295045 0.625 0.95754505 0.94791667 - 0.97345784 0.97860781 0.68421053 0.94646293] - -mean value: 0.8944430073112162 - -key: test_jcc -value: [0.71428571 0.91304348 0.85714286 0.75 0.95238095 0.83333333 - 0.72727273 0.66666667 0.68965517 0.85714286] - -mean value: 0.7960923758899772 - -key: train_jcc -value: [0.78494624 0.97883598 0.94708995 0.71984436 0.93650794 0.94871795 - 0.96296296 0.96256684 0.75609756 0.90425532] - -mean value: 0.89018250936949 - -key: TN -value: 81 - -mean value: 81.0 - -key: FP -value: 23 - -mean value: 23.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.14809275 0.13004065 0.13075185 0.13108635 0.1305635 0.1305747 - 0.12959647 0.13134789 0.1296742 0.1307199 ] - -mean value: 0.13224482536315918 - -key: score_time -value: [0.01494169 0.01492929 0.01494741 0.01512766 0.01498914 0.01503229 - 0.01501012 0.01628828 0.01516294 0.01494336] - -mean value: 0.015137219429016113 - -key: test_mcc -value: [1. 1. 0.93048421 0.92687157 1. 0.86831345 - 0.93048421 0.85909091 0.93048421 0.72821908] - -mean value: 0.9173947646273607 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.97560976 0.97674419 1. 0.95 - 0.97560976 0.95 0.97560976 0.90909091] - -mean value: 0.9712664363430102 - -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. 0.95454545 1. 1. - 0.95238095 0.95 0.95238095 0.83333333] - -mean value: 0.9642640692640694 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95238095 1. 1. 0.9047619 - 1. 0.95 1. 1. ] - -mean value: 0.9807142857142856 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.96774194 0.96774194 1. 0.93548387 - 0.96774194 0.93548387 0.96774194 0.87096774] - -mean value: 0.9612903225806452 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.97619048 0.95 1. 0.95238095 - 0.95454545 0.92954545 0.95454545 0.81818182] - -mean value: 0.9535389610389611 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.95238095 0.95454545 1. 0.9047619 - 0.95238095 0.9047619 0.95238095 0.83333333] - -mean value: 0.9454545454545455 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 202 - -mean value: 202.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.04230762 0.04330516 0.04764128 0.036551 0.05268097 0.04338908 - 0.04194522 0.05751896 0.04093218 0.03783798] - -mean value: 0.04441094398498535 - -key: score_time -value: [0.02528048 0.02137256 0.01939631 0.0182085 0.03135848 0.02629328 - 0.02537656 0.02307057 0.02730846 0.0283308 ] - -mean value: 0.024599599838256835 - -key: test_mcc -value: [0.93435318 1. 1. 0.92687157 0.93048421 0.93048421 - 0.93048421 0.79476958 0.85909091 0.72821908] - -mean value: 0.9034756961158659 - -key: train_mcc -value: [0.9920858 0.98411246 0.99213963 1. 1. 0.99210029 - 1. 1. 0.99205967 0.99205967] - -mean value: 0.9944557518725569 - -key: test_fscore -value: [0.97560976 1. 1. 0.97674419 0.97560976 0.97560976 - 0.97560976 0.92307692 0.95 0.90909091] - -mean value: 0.9661351042604587 - -key: train_fscore -value: [0.99728997 0.99462366 0.99728997 1. 1. 0.99730458 - 1. 1. 0.99731903 0.99731903] - -mean value: 0.9981146253628772 - -key: test_precision -value: [1. 1. 1. 0.95454545 1. 1. - 0.95238095 0.94736842 0.95 0.83333333] - -mean value: 0.9637628161312373 - -key: train_precision -value: [1. 0.98930481 1. 1. 1. 0.99462366 - 1. 1. 0.99465241 0.99465241] - -mean value: 0.9973233281582428 - -key: test_recall -value: [0.95238095 1. 1. 1. 0.95238095 0.95238095 - 1. 0.9 0.95 1. ] - -mean value: 0.9707142857142858 - -key: train_recall -value: [0.99459459 1. 0.99459459 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.998918918918919 - -key: test_accuracy -value: [0.96875 1. 1. 0.96774194 0.96774194 0.96774194 - 0.96774194 0.90322581 0.93548387 0.87096774] - -mean value: 0.9549395161290322 - -key: train_accuracy -value: [0.99642857 0.99285714 0.99644128 1. 1. 0.99644128 - 1. 1. 0.99644128 0.99644128] - -mean value: 0.9975050838840873 - -key: test_roc_auc -value: [0.97619048 1. 1. 0.95 0.97619048 0.97619048 - 0.95454545 0.90454545 0.92954545 0.81818182] - -mean value: 0.9485389610389611 - -key: train_roc_auc -value: [0.9972973 0.98947368 0.9972973 1. 1. 0.99479167 - 1. 1. 0.99473684 0.99473684] - -mean value: 0.9968333629682314 - -key: test_jcc -value: [0.95238095 1. 1. 0.95454545 0.95238095 0.95238095 - 0.95238095 0.85714286 0.9047619 0.83333333] - -mean value: 0.9359307359307361 - -key: train_jcc -value: [0.99459459 0.98930481 0.99459459 1. 1. 0.99462366 - 1. 1. 0.99465241 0.99465241] - -mean value: 0.9962422470771616 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.93 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.06104779 0.05568504 0.03996634 0.04052663 0.08026958 0.09496188 - 0.0754683 0.08375978 0.0394938 0.04188108] - -mean value: 0.06130602359771729 - -key: score_time -value: [0.01644206 0.01353455 0.01360631 0.01330638 0.02197981 0.01357412 - 0.02960944 0.01343012 0.01336837 0.02463293] - -mean value: 0.017348408699035645 - -key: test_mcc -value: [0.15803489 0.4133805 0.12245134 0.01126872 0.55777335 0.18593394 - 0.43636364 0.02485134 0.40800555 0.33300791] - -mean value: 0.26510711743317406 - -key: train_mcc -value: [0.88063607 0.92018324 0.85676692 0.84883567 0.87460224 0.88093695 - 0.8816558 0.88870395 0.88870395 0.87386742] - -mean value: 0.8794892204012807 - -key: test_fscore -value: [0.76595745 0.83333333 0.75555556 0.73913043 0.875 0.7826087 - 0.8 0.69767442 0.82608696 0.79069767] - -mean value: 0.7866044515677177 - -key: train_fscore -value: [0.96062992 0.97340426 0.95263158 0.95013123 0.95833333 0.96042216 - 0.96103896 0.96335079 0.96335079 0.95854922] - -mean value: 0.9601842240561401 - -key: test_precision -value: [0.69230769 0.74074074 0.70833333 0.68 0.77777778 0.72 - 0.8 0.65217391 0.73076923 0.73913043] - -mean value: 0.7241233122754862 - -key: train_precision -value: [0.93367347 0.95811518 0.92820513 0.92346939 0.92462312 0.93814433 - 0.92964824 0.93877551 0.93877551 0.925 ] - -mean value: 0.933842987568305 - -key: test_recall -value: [0.85714286 0.95238095 0.80952381 0.80952381 1. 0.85714286 - 0.8 0.75 0.95 0.85 ] - -mean value: 0.8635714285714287 - -key: train_recall -value: [0.98918919 0.98918919 0.97837838 0.97837838 0.99459459 0.98378378 - 0.99462366 0.98924731 0.98924731 0.99462366] - -mean value: 0.9881255448997385 - -key: test_accuracy -value: [0.65625 0.75 0.64516129 0.61290323 0.80645161 0.67741935 - 0.74193548 0.58064516 0.74193548 0.70967742] - -mean value: 0.6922379032258065 - -key: train_accuracy -value: [0.94642857 0.96428571 0.93594306 0.93238434 0.9430605 0.94661922 - 0.94661922 0.95017794 0.95017794 0.9430605 ] - -mean value: 0.945875699034062 - -key: test_roc_auc -value: [0.56493506 0.65800866 0.5547619 0.5047619 0.7 0.57857143 - 0.71818182 0.51136364 0.65681818 0.65227273] - -mean value: 0.6099675324675324 - -key: train_roc_auc -value: [0.92617354 0.95248933 0.91627252 0.91106419 0.9191723 0.92939189 - 0.92362762 0.93146576 0.93146576 0.91836446] - -mean value: 0.92594873736215 - -key: test_jcc -value: [0.62068966 0.71428571 0.60714286 0.5862069 0.77777778 0.64285714 - 0.66666667 0.53571429 0.7037037 0.65384615] - -mean value: 0.650889085371844 - -key: train_jcc -value: [0.92424242 0.94818653 0.90954774 0.905 0.92 0.92385787 - 0.925 0.92929293 0.92929293 0.92039801] - -mean value: 0.9234818427989714 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 68 - -mean value: 68.0 - -key: TP -value: 178 - -mean value: 178.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -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/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.28 - -Accuracy on Blind test: 0.7 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.46115923 0.44185972 0.44269156 0.44909668 0.44474816 0.43808627 - 0.43469429 0.43570971 0.43906617 0.4296422 ] - -mean value: 0.44167540073394773 - -key: score_time -value: [0.0092442 0.00915384 0.00920796 0.00919032 0.00921798 0.00926757 - 0.00912905 0.00921392 0.00900984 0.00932336] - -mean value: 0.009195804595947266 - -key: test_mcc -value: [1. 1. 1. 0.85238095 1. 0.93048421 - 0.93048421 0.93048421 0.85909091 0.72821908] - -mean value: 0.9231143573921694 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.95238095 1. 0.97560976 - 0.97560976 0.97560976 0.95 0.90909091] - -mean value: 0.9738301129764544 - -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. 0.95238095 1. 1. - 0.95238095 0.95238095 0.95 0.83333333] - -mean value: 0.964047619047619 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95238095 1. 0.95238095 - 1. 1. 0.95 1. ] - -mean value: 0.9854761904761904 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.93548387 1. 0.96774194 - 0.96774194 0.96774194 0.93548387 0.87096774] - -mean value: 0.964516129032258 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.92619048 1. 0.97619048 - 0.95454545 0.95454545 0.92954545 0.81818182] - -mean value: 0.9559199134199134 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.90909091 1. 0.95238095 - 0.95238095 0.95238095 0.9047619 0.83333333] - -mean value: 0.9504329004329005 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 203 - -mean value: 203.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.0233779 0.02502084 0.02472115 0.03441906 0.02718186 0.03686452 - 0.0269208 0.0285058 0.02501702 0.0264864 ] - -mean value: 0.02785153388977051 - -key: score_time -value: [0.01227784 0.01208687 0.01251578 0.01264119 0.01323295 0.0203855 - 0.01288342 0.01299381 0.01248217 0.01287174] - -mean value: 0.013437128067016602 - -key: test_mcc -value: [ 0.21867346 0.0849412 -0.26560636 0.09967105 -0.05976143 0.00752923 - 0.01363636 -0.23927198 0.14863011 0.22469871] - -mean value: 0.023314035374443605 - -key: train_mcc -value: [0.34354378 0.33200663 0.37383194 0.35226764 0.35226764 0.34110438 - 0.34382047 0.39766525 0.32040778 0.35507261] - -mean value: 0.3511988107242974 - -key: test_fscore -value: [0.8 0.78431373 0.70833333 0.8 0.75 0.7755102 - 0.65 0.63636364 0.75555556 0.7826087 ] - -mean value: 0.7442685150476528 - -key: train_fscore -value: [0.82405345 0.82222222 0.82774049 0.82405345 0.82405345 0.82222222 - 0.8248337 0.83408072 0.82119205 0.82666667] - -mean value: 0.8251118432979977 - -key: test_precision -value: [0.68965517 0.66666667 0.62962963 0.68965517 0.66666667 0.67857143 - 0.65 0.58333333 0.68 0.69230769] - -mean value: 0.6626485762003004 - -key: train_precision -value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321 - 0.70188679 0.71538462 0.69662921 0.70454545] - -mean value: 0.7023052088462121 - -key: test_recall -value: [0.95238095 0.95238095 0.80952381 0.95238095 0.85714286 0.9047619 - 0.65 0.7 0.85 0.9 ] - -mean value: 0.8528571428571429 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6875 0.65625 0.5483871 0.67741935 0.61290323 0.64516129 - 0.5483871 0.48387097 0.64516129 0.67741935] - -mean value: 0.6182459677419355 - -key: train_accuracy -value: [0.71785714 0.71428571 0.72597865 0.71886121 0.71886121 0.71530249 - 0.71886121 0.7366548 0.71174377 0.72241993] - -mean value: 0.7200826131164211 - -key: test_roc_auc -value: [0.56709957 0.52164502 0.4047619 0.52619048 0.47857143 0.50238095 - 0.50681818 0.39545455 0.56136364 0.58636364] - -mean value: 0.505064935064935 - -key: train_roc_auc -value: [0.58421053 0.57894737 0.59895833 0.58854167 0.58854167 0.58333333 - 0.58421053 0.61052632 0.57368421 0.58947368] - -mean value: 0.5880427631578947 - -key: test_jcc -value: [0.66666667 0.64516129 0.5483871 0.66666667 0.6 0.63333333 - 0.48148148 0.46666667 0.60714286 0.64285714] - -mean value: 0.5958363201911588 - -key: train_jcc -value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321 - 0.70188679 0.71538462 0.69662921 0.70454545] - -mean value: 0.7023052088462121 - -key: TN -value: 17 - -mean value: 17.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 89 - -mean value: 89.0 - -key: TP -value: 176 - -mean value: 176.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.17 - -Accuracy on Blind test: 0.68 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02277946 0.03577924 0.03494143 0.03556728 0.03561592 0.03502131 - 0.03542185 0.03545427 0.03555131 0.03549528] - -mean value: 0.03416273593902588 - -key: score_time -value: [0.02342391 0.02337527 0.02280545 0.02236032 0.02176046 0.02052379 - 0.02406287 0.0236938 0.0235455 0.02287507] - -mean value: 0.0228426456451416 - -key: test_mcc -value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095 - 0.85909091 0.79476958 0.93048421 0.72821908] - -mean value: 0.8653851428550935 - -key: train_mcc -value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998 - 0.96017122 0.96815373 0.96021134 0.96021134] - -mean value: 0.9554819935656209 - -key: test_fscore -value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095 - 0.95 0.92307692 0.97560976 0.90909091] - -mean value: 0.9547240158215766 - -key: train_fscore -value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722 - 0.98659517 0.98930481 0.98666667 0.98666667] - -mean value: 0.9849993906126601 - -key: test_precision -value: [1. 0.95238095 1. 0.86956522 1. 0.95238095 - 0.95 0.94736842 0.95238095 0.83333333] - -mean value: 0.9457409828920126 - -key: train_precision -value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497 - 0.98395722 0.98404255 0.97883598 0.97883598] - -mean value: 0.9781994809529229 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 - 0.95 0.9 1. 1. ] - -mean value: 0.9659523809523808 - -key: train_recall -value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459 - 0.98924731 0.99462366 0.99462366 0.99462366] - -mean value: 0.9919064225515838 - -key: test_accuracy -value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387 - 0.93548387 0.90322581 0.96774194 0.87096774] - -mean value: 0.9389112903225808 - -key: train_accuracy -value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769 - 0.98220641 0.98576512 0.98220641 0.98220641] - -mean value: 0.9800546517539402 - -key: test_roc_auc -value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048 - 0.92954545 0.90454545 0.95454545 0.81818182] - -mean value: 0.9266125541125542 - -key: train_roc_auc -value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563 - 0.97883418 0.98152235 0.9762592 0.9762592 ] - -mean value: 0.9744729481178972 - -key: test_jcc -value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091 - 0.9047619 0.85714286 0.95238095 0.83333333] - -mean value: 0.9151515151515153 - -key: train_jcc -value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105 - 0.97354497 0.97883598 0.97368421 0.97368421] - -mean value: 0.9704511278195488 - -key: TN -value: 94 - -mean value: 94.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 199 - -mean value: 199.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.23733401 0.24284005 0.25065804 0.2886591 0.24323916 0.2424953 - 0.43217683 0.28001761 0.36137295 0.34959149] - -mean value: 0.292838454246521 - -key: score_time -value: [0.0211041 0.02119303 0.02349997 0.01739216 0.02394438 0.02036405 - 0.02439165 0.0242722 0.02366924 0.02450585] - -mean value: 0.022433662414550783 - -key: test_mcc -value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095 - 0.85909091 0.79476958 0.93048421 0.66057826] - -mean value: 0.8586210606372333 - -key: train_mcc -value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998 - 0.96017122 0.96815373 0.96021134 0.97611544] - -mean value: 0.9570724042956436 - -key: test_fscore -value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095 - 0.95 0.92307692 0.97560976 0.88888889] - -mean value: 0.9527038138013747 - -key: train_fscore -value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722 - 0.98659517 0.98930481 0.98666667 0.9919571 ] - -mean value: 0.9855284344017574 - -key: test_precision -value: [1. 0.95238095 1. 0.86956522 1. 0.95238095 - 0.95 0.94736842 0.95238095 0.8 ] - -mean value: 0.9424076495586793 - -key: train_precision -value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497 - 0.98395722 0.98404255 0.97883598 0.98930481] - -mean value: 0.9792463643527473 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 - 0.95 0.9 1. 1. ] - -mean value: 0.9659523809523808 - -key: train_recall -value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459 - 0.98924731 0.99462366 0.99462366 0.99462366] - -mean value: 0.9919064225515838 - -key: test_accuracy -value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387 - 0.93548387 0.90322581 0.96774194 0.83870968] - -mean value: 0.9356854838709678 - -key: train_accuracy -value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769 - 0.98220641 0.98576512 0.98220641 0.98932384] - -mean value: 0.9807663955261822 - -key: test_roc_auc -value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048 - 0.92954545 0.90454545 0.95454545 0.77272727] - -mean value: 0.9220670995670996 - -key: train_roc_auc -value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563 - 0.97883418 0.98152235 0.9762592 0.98678551] - -mean value: 0.9755255796968445 - -key: test_jcc -value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091 - 0.9047619 0.85714286 0.95238095 0.8 ] - -mean value: 0.9118181818181819 - -key: train_jcc -value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105 - 0.97354497 0.97883598 0.97368421 0.98404255] - -mean value: 0.9714869620860662 - -key: TN -value: 93 - -mean value: 93.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 199 - -mean value: 199.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: 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 - baseline_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: 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 - baseline_CV['Resampling'] = rs_none -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: 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 - baseline_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: 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 - baseline_BT['Resampling'] = rs_none -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03308296 0.03610563 0.03306627 0.03525186 0.03636479 0.03501225 - 0.03478789 0.0367043 0.03446531 0.03514433] - -mean value: 0.03499855995178223 - -key: score_time -value: [0.01234603 0.01233053 0.01335764 0.01235819 0.01245904 0.01250768 - 0.01237798 0.01265287 0.01228142 0.01239014] - -mean value: 0.01250615119934082 - -key: test_mcc -value: [0.95346259 0.90889326 0.85441771 0.76500781 0.85441771 0.8547619 - 0.80817439 0.70714286 0.90238095 0.85441771] - -mean value: 0.846307688531032 - -key: train_mcc -value: [0.94066423 0.95136525 0.93025158 0.94614468 0.9355233 0.95160448 - 0.956873 0.940826 0.940826 0.94071491] - -mean value: 0.9434793423451845 - -key: test_fscore -value: [0.97674419 0.95 0.93023256 0.87179487 0.93023256 0.92682927 - 0.89473684 0.85 0.95 0.92307692] - -mean value: 0.9203647207595322 - -key: train_fscore -value: [0.97002725 0.97560976 0.96438356 0.97282609 0.96721311 0.97547684 - 0.97849462 0.9701897 0.9701897 0.9703504 ] - -mean value: 0.9714761038408097 - -key: test_precision -value: [0.95454545 1. 0.90909091 0.94444444 0.90909091 0.95 - 0.94444444 0.85 0.95 0.94736842] - -mean value: 0.9358984582668792 - -key: train_precision -value: [0.97802198 0.97826087 0.97777778 0.97814208 0.97790055 0.98351648 - 0.97849462 0.97814208 0.97814208 0.97297297] - -mean value: 0.978137148750473 - -key: test_recall -value: [1. 0.9047619 0.95238095 0.80952381 0.95238095 0.9047619 - 0.85 0.85 0.95 0.9 ] - -mean value: 0.9073809523809523 - -key: train_recall -value: [0.96216216 0.97297297 0.95135135 0.96756757 0.95675676 0.96756757 - 0.97849462 0.96236559 0.96236559 0.96774194] - -mean value: 0.9649346120313862 - -key: test_accuracy -value: [0.97619048 0.95238095 0.92682927 0.87804878 0.92682927 0.92682927 - 0.90243902 0.85365854 0.95121951 0.92682927] - -mean value: 0.9221254355400698 - -key: train_accuracy -value: [0.97027027 0.97567568 0.96495957 0.97304582 0.96765499 0.97574124 - 0.97843666 0.9703504 0.9703504 0.9703504 ] - -mean value: 0.9716835433816566 - -key: test_roc_auc -value: [0.97619048 0.95238095 0.92619048 0.8797619 0.92619048 0.92738095 - 0.90119048 0.85357143 0.95119048 0.92619048] - -mean value: 0.9220238095238095 - -key: train_roc_auc -value: [0.97027027 0.97567568 0.96492299 0.9730311 0.96762569 0.97571927 - 0.9784365 0.97037198 0.97037198 0.97035745] - -mean value: 0.9716782911944202 - -key: test_jcc -value: [0.95454545 0.9047619 0.86956522 0.77272727 0.86956522 0.86363636 - 0.80952381 0.73913043 0.9047619 0.85714286] - -mean value: 0.8545360436664785 - -key: train_jcc -value: [0.94179894 0.95238095 0.93121693 0.94708995 0.93650794 0.95212766 - 0.95789474 0.94210526 0.94210526 0.94240838] - -mean value: 0.9445636008690423 - -key: TN -value: 193 - -mean value: 193.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 187 - -mean value: 187.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.88 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.76303077 0.8300209 0.73767257 0.85287404 0.74666619 0.72793317 - 0.82305479 0.78377914 0.7454052 0.87989378] - -mean value: 0.789033055305481 - -key: score_time -value: [0.01280284 0.01379657 0.01262498 0.01349235 0.01266551 0.01262546 - 0.01262879 0.01256895 0.01259089 0.0126822 ] - -mean value: 0.01284785270690918 - -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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.9047619 0.9047619 0.90692382 0.90238095 0.95227002 0.90692382 - 0.95227002 0.7565654 1. 0.95238095] - -mean value: 0.9139238793157022 - -key: train_mcc -value: [1. 0.99460913 1. 1. 0.99462366 1. - 0.9946235 0.9946235 0.98927544 1. ] - -mean value: 0.9967755223131768 - -key: test_fscore -value: [0.95238095 0.95238095 0.95 0.95238095 0.97674419 0.95 - 0.97435897 0.87179487 1. 0.97560976] - -mean value: 0.9555650645440774 - -key: train_fscore -value: [1. 0.99730458 1. 1. 0.99730458 1. - 0.99731903 0.99731903 0.99465241 1. ] - -mean value: 0.998389964054269 - -key: test_precision -value: [0.95238095 0.95238095 1. 0.95238095 0.95454545 1. - 1. 0.89473684 1. 0.95238095] - -mean value: 0.9658806106174527 - -key: train_precision -value: [1. 0.99462366 1. 1. 0.99462366 1. - 0.99465241 0.99465241 0.9893617 1. ] - -mean value: 0.9967913826789842 - -key: test_recall -value: [0.95238095 0.95238095 0.9047619 0.95238095 1. 0.9047619 - 0.95 0.85 1. 1. ] - -mean value: 0.9466666666666667 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951 - 0.97560976 0.87804878 1. 0.97560976] - -mean value: 0.9563298490127756 - -key: train_accuracy -value: [1. 0.9972973 1. 1. 0.99730458 1. - 0.99730458 0.99730458 0.99460916 1. ] - -mean value: 0.998382020834851 - -key: test_roc_auc -value: [0.95238095 0.95238095 0.95238095 0.95119048 0.975 0.95238095 - 0.975 0.87738095 1. 0.97619048] - -mean value: 0.9564285714285713 - -key: train_roc_auc -value: [1. 0.9972973 1. 1. 0.99731183 1. - 0.9972973 0.9972973 0.99459459 1. ] - -mean value: 0.9983798314443476 - -key: test_jcc -value: [0.90909091 0.90909091 0.9047619 0.90909091 0.95454545 0.9047619 - 0.95 0.77272727 1. 0.95238095] - -mean value: 0.9166450216450215 - -key: train_jcc -value: [1. 0.99462366 1. 1. 0.99462366 1. - 0.99465241 0.99465241 0.9893617 1. ] - -mean value: 0.9967913826789842 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 195 - -mean value: 195.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01330519 0.01353216 0.01044512 0.00971842 0.00956607 0.00976133 - 0.00986981 0.00953341 0.00945997 0.00939202] - -mean value: 0.01045835018157959 - -key: score_time -value: [0.01211691 0.01043463 0.01057625 0.00960898 0.00906467 0.008955 - 0.00923419 0.00883865 0.00894046 0.00897145] - -mean value: 0.00967411994934082 - -key: test_mcc -value: [0.52380952 0.3478328 0.41963703 0.36718832 0.65871309 0.51966679 - 0.41766229 0.26904762 0.56836003 0.56836003] - -mean value: 0.46602775272823205 - -key: train_mcc -value: [0.49994094 0.51360753 0.57218883 0.55700213 0.53457148 0.57456037 - 0.54451453 0.57340242 0.5491086 0.58606107] - -mean value: 0.550495789809396 - -key: test_fscore -value: [0.76190476 0.70833333 0.73913043 0.71111111 0.8372093 0.7826087 - 0.71428571 0.63414634 0.79069767 0.79069767] - -mean value: 0.7470125043695909 - -key: train_fscore -value: [0.76092545 0.77078086 0.79487179 0.78880407 0.77749361 0.7979798 - 0.7826087 0.7979798 0.78350515 0.805 ] - -mean value: 0.7859949224802306 - -key: test_precision -value: [0.76190476 0.62962963 0.68 0.66666667 0.81818182 0.72 - 0.68181818 0.61904762 0.73913043 0.73913043] - -mean value: 0.7055509546813894 - -key: train_precision -value: [0.7254902 0.72169811 0.75609756 0.74519231 0.73786408 0.74881517 - 0.74634146 0.75238095 0.75247525 0.75233645] - -mean value: 0.7438691533419045 - -key: test_recall -value: [0.76190476 0.80952381 0.80952381 0.76190476 0.85714286 0.85714286 - 0.75 0.65 0.85 0.85 ] - -mean value: 0.7957142857142856 - -key: train_recall -value: [0.8 0.82702703 0.83783784 0.83783784 0.82162162 0.85405405 - 0.82258065 0.84946237 0.8172043 0.8655914 ] - -mean value: 0.8333217088055799 - -key: test_accuracy -value: [0.76190476 0.66666667 0.70731707 0.68292683 0.82926829 0.75609756 - 0.70731707 0.63414634 0.7804878 0.7804878 ] - -mean value: 0.7306620209059232 - -key: train_accuracy -value: [0.74864865 0.75405405 0.78436658 0.77628032 0.76549865 0.78436658 - 0.77088949 0.78436658 0.77358491 0.78975741] - -mean value: 0.7731813214832083 - -key: test_roc_auc -value: [0.76190476 0.66666667 0.7047619 0.68095238 0.82857143 0.75357143 - 0.70833333 0.63452381 0.78214286 0.78214286] - -mean value: 0.7303571428571429 - -key: train_roc_auc -value: [0.74864865 0.75405405 0.78451032 0.7764458 0.76564952 0.78455391 - 0.77074978 0.78419064 0.77346702 0.78955246] - -mean value: 0.7731822144725371 - -key: test_jcc -value: [0.61538462 0.5483871 0.5862069 0.55172414 0.72 0.64285714 - 0.55555556 0.46428571 0.65384615 0.65384615] - -mean value: 0.5992093467032289 - -key: train_jcc -value: [0.61410788 0.62704918 0.65957447 0.6512605 0.63598326 0.66386555 - 0.64285714 0.66386555 0.6440678 0.67364017] - -mean value: 0.6476271499298714 - -key: TN -value: 137 - -mean value: 137.0 - -key: FP -value: 42 - -mean value: 42.0 - -key: FN -value: 69 - -mean value: 69.0 - -key: TP -value: 164 - -mean value: 164.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.43 - -Accuracy on Blind test: 0.74 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01092005 0.01027369 0.00994992 0.00966907 0.00953984 0.00965834 - 0.00972724 0.00973678 0.00973964 0.00964236] - -mean value: 0.009885692596435547 - -key: score_time -value: [0.00965691 0.00880075 0.00904751 0.00887823 0.00878286 0.00878644 - 0.00877023 0.00896668 0.00879884 0.00876403] - -mean value: 0.008925247192382812 - -key: test_mcc -value: [0.19611614 0.24253563 0.35038478 0.37171226 0.59982886 0.62325386 - 0.62048368 0.21957752 0.27179142 0.46300848] - -mean value: 0.3958692609699289 - -key: train_mcc -value: [0.5197894 0.47073308 0.4803209 0.47192026 0.46367088 0.47192026 - 0.45388122 0.48220059 0.47119158 0.49005654] - -mean value: 0.4775684717659203 - -key: test_fscore -value: [0.54054054 0.57894737 0.58823529 0.66666667 0.74285714 0.78947368 - 0.70967742 0.55555556 0.57142857 0.71794872] - -mean value: 0.646133096110126 - -key: train_fscore -value: [0.72507553 0.67515924 0.69538462 0.68535826 0.67507886 0.68535826 - 0.6809816 0.70658683 0.7005988 0.71764706] - -mean value: 0.694722903766912 - -key: test_precision -value: [0.625 0.64705882 0.76923077 0.72222222 0.92857143 0.88235294 - 1. 0.625 0.66666667 0.73684211] - -mean value: 0.7602944956660127 - -key: train_precision -value: [0.82191781 0.82170543 0.80714286 0.80882353 0.81060606 0.80882353 - 0.79285714 0.7972973 0.79054054 0.79220779] - -mean value: 0.8051921984050987 - -key: test_recall -value: [0.47619048 0.52380952 0.47619048 0.61904762 0.61904762 0.71428571 - 0.55 0.5 0.5 0.7 ] - -mean value: 0.567857142857143 - -key: train_recall -value: [0.64864865 0.57297297 0.61081081 0.59459459 0.57837838 0.59459459 - 0.59677419 0.6344086 0.62903226 0.65591398] - -mean value: 0.6116129032258065 - -key: test_accuracy -value: [0.5952381 0.61904762 0.65853659 0.68292683 0.7804878 0.80487805 - 0.7804878 0.6097561 0.63414634 0.73170732] - -mean value: 0.6897212543554007 - -key: train_accuracy -value: [0.75405405 0.72432432 0.73315364 0.7277628 0.72237197 0.7277628 - 0.71967655 0.73584906 0.73045822 0.74123989] - -mean value: 0.7316653310992934 - -key: test_roc_auc -value: [0.5952381 0.61904762 0.66309524 0.68452381 0.78452381 0.80714286 - 0.775 0.60714286 0.63095238 0.73095238] - -mean value: 0.6897619047619047 - -key: train_roc_auc -value: [0.75405405 0.72432432 0.73282476 0.72740482 0.72198489 0.72740482 - 0.72000872 0.73612322 0.73073235 0.7414705 ] - -mean value: 0.7316332461493751 - -key: test_jcc -value: [0.37037037 0.40740741 0.41666667 0.5 0.59090909 0.65217391 - 0.55 0.38461538 0.4 0.56 ] - -mean value: 0.48321428330123994 - -key: train_jcc -value: [0.56872038 0.50961538 0.53301887 0.52132701 0.50952381 0.52132701 - 0.51627907 0.5462963 0.53917051 0.55963303] - -mean value: 0.5324911370145777 - -key: TN -value: 167 - -mean value: 167.0 - -key: FP -value: 89 - -mean value: 89.0 - -key: FN -value: 39 - -mean value: 39.0 - -key: TP -value: 117 - -mean value: 117.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.28 - -Accuracy on Blind test: 0.63 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00904441 0.00994539 0.00996995 0.00994349 0.00990462 0.00975704 - 0.00993729 0.00986505 0.00999761 0.00940776] - -mean value: 0.009777259826660157 - -key: score_time -value: [0.01108098 0.0159514 0.01206779 0.01446748 0.01251292 0.01263952 - 0.01265025 0.01304054 0.01275277 0.01227689] - -mean value: 0.01294405460357666 - -key: test_mcc -value: [0.43052839 0.53357838 0.27338837 0.41766229 0.6133669 0.51190476 - 0.51320273 0.42916625 0.51190476 0.31655495] - -mean value: 0.4551257782268342 - -key: train_mcc -value: [0.64509306 0.67193129 0.66110925 0.6784174 0.65112849 0.68828497 - 0.66576577 0.65242388 0.66119778 0.68278387] - -mean value: 0.6658135765240021 - -key: test_fscore -value: [0.7 0.73684211 0.61538462 0.7 0.8 0.76190476 - 0.73684211 0.64705882 0.75 0.63157895] - -mean value: 0.7079611358713526 - -key: train_fscore -value: [0.81460674 0.82913165 0.82548476 0.83146067 0.81792717 0.83888889 - 0.83333333 0.81690141 0.82644628 0.83746556] - -mean value: 0.8271646480205639 - -key: test_precision -value: [0.73684211 0.82352941 0.66666667 0.73684211 0.84210526 0.76190476 - 0.77777778 0.78571429 0.75 0.66666667] - -mean value: 0.7548049044179075 - -key: train_precision -value: [0.84795322 0.86046512 0.84659091 0.86549708 0.84883721 0.86285714 - 0.83333333 0.85798817 0.84745763 0.85875706] - -mean value: 0.8529736858206451 - -key: test_recall -value: [0.66666667 0.66666667 0.57142857 0.66666667 0.76190476 0.76190476 - 0.7 0.55 0.75 0.6 ] - -mean value: 0.6695238095238094 - -key: train_recall -value: [0.78378378 0.8 0.80540541 0.8 0.78918919 0.81621622 - 0.83333333 0.77956989 0.80645161 0.8172043 ] - -mean value: 0.803115373437954 - -key: test_accuracy -value: [0.71428571 0.76190476 0.63414634 0.70731707 0.80487805 0.75609756 - 0.75609756 0.70731707 0.75609756 0.65853659] - -mean value: 0.7256678281068524 - -key: train_accuracy -value: [0.82162162 0.83513514 0.83018868 0.83827493 0.82479784 0.84366577 - 0.8328841 0.82479784 0.83018868 0.84097035] - -mean value: 0.8322524950826837 - -key: test_roc_auc -value: [0.71428571 0.76190476 0.63571429 0.70833333 0.80595238 0.75595238 - 0.7547619 0.70357143 0.75595238 0.65714286] - -mean value: 0.7253571428571429 - -key: train_roc_auc -value: [0.82162162 0.83513514 0.83012206 0.83817204 0.82470212 0.84359198 - 0.83288288 0.82492008 0.83025283 0.84103458] - -mean value: 0.832243533856437 - -key: test_jcc -value: [0.53846154 0.58333333 0.44444444 0.53846154 0.66666667 0.61538462 - 0.58333333 0.47826087 0.6 0.46153846] - -mean value: 0.5509884801189149 - -key: train_jcc -value: [0.68720379 0.70813397 0.70283019 0.71153846 0.69194313 0.72248804 - 0.71428571 0.69047619 0.70422535 0.72037915] - -mean value: 0.7053503983012377 - -key: TN -value: 161 - -mean value: 161.0 - -key: FP -value: 68 - -mean value: 68.0 - -key: FN -value: 45 - -mean value: 45.0 - -key: TP -value: 138 - -mean value: 138.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.7 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02168798 0.01971412 0.01750493 0.0180738 0.01735806 0.01955819 - 0.01806736 0.02039933 0.01962757 0.02065182] - -mean value: 0.01926431655883789 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -score_time -value: [0.01141286 0.01185846 0.01218891 0.01214862 0.01135635 0.01124072 - 0.01116204 0.0116775 0.01220202 0.01134872] - -mean value: 0.011659622192382812 - -key: test_mcc -value: [0.66742381 0.62187434 0.56086079 0.65952381 0.66432098 0.7098505 - 0.7197263 0.51190476 0.76500781 0.71121921] - -mean value: 0.6591712308091895 - -key: train_mcc -value: [0.78382959 0.81626392 0.79519893 0.80055217 0.80596972 0.81143372 - 0.83298455 0.81132843 0.79514676 0.80063366] - -mean value: 0.8053341444808453 - -key: test_fscore -value: [0.8372093 0.8 0.79069767 0.82926829 0.84444444 0.86363636 - 0.83333333 0.75 0.88372093 0.85714286] - -mean value: 0.828945319821667 - -key: train_fscore -value: [0.89130435 0.9076087 0.89784946 0.90026954 0.90217391 0.90616622 - 0.91733333 0.90616622 0.89784946 0.90133333] - -mean value: 0.9028054529376849 - -key: test_precision -value: [0.81818182 0.84210526 0.77272727 0.85 0.79166667 0.82608696 - 0.9375 0.75 0.82608696 0.81818182] - -mean value: 0.8232536751958948 - -key: train_precision -value: [0.89617486 0.91256831 0.89304813 0.89784946 0.90710383 0.89893617 - 0.91005291 0.90374332 0.89784946 0.89417989] - -mean value: 0.9011506337562538 - -key: test_recall -value: [0.85714286 0.76190476 0.80952381 0.80952381 0.9047619 0.9047619 - 0.75 0.75 0.95 0.9 ] - -mean value: 0.8397619047619047 - -key: train_recall -value: [0.88648649 0.9027027 0.9027027 0.9027027 0.8972973 0.91351351 - 0.92473118 0.90860215 0.89784946 0.90860215] - -mean value: 0.9045190351641965 - -key: test_accuracy -value: [0.83333333 0.80952381 0.7804878 0.82926829 0.82926829 0.85365854 - 0.85365854 0.75609756 0.87804878 0.85365854] - -mean value: 0.8277003484320558 - -key: train_accuracy -value: [0.89189189 0.90810811 0.89757412 0.90026954 0.90296496 0.90566038 - 0.91644205 0.90566038 0.89757412 0.90026954] - -mean value: 0.9026415094339623 - -key: test_roc_auc -value: [0.83333333 0.80952381 0.7797619 0.8297619 0.82738095 0.85238095 - 0.85119048 0.75595238 0.8797619 0.8547619 ] - -mean value: 0.8273809523809523 - -key: train_roc_auc -value: [0.89189189 0.90810811 0.89758791 0.90027608 0.90294972 0.90568149 - 0.91641965 0.90565243 0.89757338 0.90024702] - -mean value: 0.9026387678000581 - -key: test_jcc -value: [0.72 0.66666667 0.65384615 0.70833333 0.73076923 0.76 - 0.71428571 0.6 0.79166667 0.75 ] - -mean value: 0.7095567765567765 - -key: train_jcc -value: [0.80392157 0.83084577 0.81463415 0.81862745 0.82178218 0.82843137 - 0.84729064 0.82843137 0.81463415 0.82038835] - -mean value: 0.8228986996659561 - -key: TN -value: 168 - -mean value: 168.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 38 - -mean value: 38.0 - -key: TP -value: 173 - -mean value: 173.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.8 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.29383063 1.44016743 1.28876901 1.44384265 1.41407895 1.30052304 - 1.70417809 1.71098447 1.61445165 1.84017086] - -mean value: 1.5050996780395507 - -key: score_time -value: [0.01451826 0.01390624 0.01395798 0.01398897 0.01401591 0.01408267 - 0.01418805 0.01403236 0.02189183 0.01375771] - -mean value: 0.014833998680114747 - -key: test_mcc -value: [0.95346259 0.81322028 0.86333169 0.80907152 0.90649828 0.8547619 - 0.86240942 0.65871309 0.95238095 0.85441771] - -mean value: 0.852826743808685 - -key: train_mcc -value: [1. 0.99460913 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994609125148145 - -key: test_fscore -value: [0.97674419 0.90909091 0.92307692 0.9 0.95454545 0.92682927 - 0.91891892 0.82051282 0.97560976 0.92307692] - -mean value: 0.9228405159658705 - -key: train_fscore -value: [1. 0.99730458 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997304582210242 - -key: test_precision -value: [0.95454545 0.86956522 1. 0.94736842 0.91304348 0.95 - 1. 0.84210526 0.95238095 0.94736842] - -mean value: 0.9376377207841738 - -key: train_precision -value: [1. 0.99462366 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994623655913978 - -key: test_recall -value: [1. 0.95238095 0.85714286 0.85714286 1. 0.9047619 - 0.85 0.8 1. 0.9 ] - -mean value: 0.9121428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97619048 0.9047619 0.92682927 0.90243902 0.95121951 0.92682927 - 0.92682927 0.82926829 0.97560976 0.92682927] - -mean value: 0.9246806039488968 - -key: train_accuracy -value: [1. 0.9972973 1. 1. 1. 1. 1. - 1. 1. 1. ] - -mean value: 0.9997297297297297 - -key: test_roc_auc -value: [0.97619048 0.9047619 0.92857143 0.90357143 0.95 0.92738095 - 0.925 0.82857143 0.97619048 0.92619048] - -mean value: 0.924642857142857 - -key: train_roc_auc -value: [1. 0.9972973 1. 1. 1. 1. 1. - 1. 1. 1. ] - -mean value: 0.9997297297297297 - -key: test_jcc -value: [0.95454545 0.83333333 0.85714286 0.81818182 0.91304348 0.86363636 - 0.85 0.69565217 0.95238095 0.85714286] - -mean value: 0.859505928853755 - -key: train_jcc -value: [1. 0.99462366 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994623655913978 - -key: TN -value: 193 - -mean value: 193.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 188 - -mean value: 188.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.8 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02476811 0.02039862 0.01846981 0.01775837 0.0182929 0.01789904 - 0.01828575 0.01807499 0.01761913 0.01760459] - -mean value: 0.018917131423950195 - -key: score_time -value: [0.0121634 0.00941205 0.00896049 0.00882649 0.00887442 0.00888562 - 0.00931406 0.00897431 0.00980282 0.00914001] - -mean value: 0.009435367584228516 - -key: test_mcc -value: [0.82462113 0.95346259 1. 0.70714286 0.90692382 0.8547619 - 0.86240942 0.95238095 0.90238095 0.90238095] - -mean value: 0.8866464572934346 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.89473684 0.97674419 1. 0.85714286 0.95 0.92682927 - 0.91891892 0.97560976 0.95 0.95 ] - -mean value: 0.9399981828603794 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95454545 1. 0.85714286 1. 0.95 - 1. 0.95238095 0.95 0.95 ] - -mean value: 0.9614069264069263 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.80952381 1. 1. 0.85714286 0.9047619 0.9047619 - 0.85 1. 0.95 0.95 ] - -mean value: 0.9226190476190477 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9047619 0.97619048 1. 0.85365854 0.95121951 0.92682927 - 0.92682927 0.97560976 0.95121951 0.95121951] - -mean value: 0.9417537746806038 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9047619 0.97619048 1. 0.85357143 0.95238095 0.92738095 - 0.925 0.97619048 0.95119048 0.95119048] - -mean value: 0.9417857142857142 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.80952381 0.95454545 1. 0.75 0.9047619 0.86363636 - 0.85 0.95238095 0.9047619 0.9047619 ] - -mean value: 0.8894372294372295 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 198 - -mean value: 198.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 190 - -mean value: 190.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.11411691 0.11291766 0.11341381 0.113482 0.11605811 0.11524677 - 0.1146853 0.11328244 0.1139667 0.11258531] - -mean value: 0.11397550106048585 - -key: score_time -value: [0.01812983 0.01800847 0.01816654 0.01818466 0.01819277 0.0194211 - 0.01798368 0.01814198 0.01803756 0.01890779] - -mean value: 0.018317437171936034 - -key: test_mcc -value: [0.76277007 0.80952381 0.65871309 0.75714286 0.8547619 0.7098505 - 0.7197263 0.70714286 0.85441771 0.7565654 ] - -mean value: 0.7590614509822682 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88372093 0.9047619 0.8372093 0.87804878 0.92682927 0.86363636 - 0.83333333 0.85 0.92307692 0.87179487] - -mean value: 0.8772411677942025 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.86363636 0.9047619 0.81818182 0.9 0.95 0.82608696 - 0.9375 0.85 0.94736842 0.89473684] - -mean value: 0.8892272306259722 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.9047619 0.9047619 0.85714286 0.85714286 0.9047619 0.9047619 - 0.75 0.85 0.9 0.85 ] - -mean value: 0.8683333333333334 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.88095238 0.9047619 0.82926829 0.87804878 0.92682927 0.85365854 - 0.85365854 0.85365854 0.92682927 0.87804878] - -mean value: 0.8785714285714284 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.88095238 0.9047619 0.82857143 0.87857143 0.92738095 0.85238095 - 0.85119048 0.85357143 0.92619048 0.87738095] - -mean value: 0.8780952380952382 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.79166667 0.82608696 0.72 0.7826087 0.86363636 0.76 - 0.71428571 0.73913043 0.85714286 0.77272727] - -mean value: 0.7827284961415396 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 183 - -mean value: 183.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 179 - -mean value: 179.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.82 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00981283 0.010782 0.0110085 0.01046014 0.00998545 0.00999331 - 0.00993228 0.00992513 0.0098927 0.00979471] - -mean value: 0.010158705711364745 - -key: score_time -value: [0.00894189 0.00929785 0.00908732 0.00908136 0.00908279 0.00907326 - 0.00922275 0.00893164 0.00892639 0.0089097 ] - -mean value: 0.009055495262145996 - -key: test_mcc -value: [0.47673129 0.48112522 0.38060103 0.75714286 0.46623254 0.21823107 - 0.31960727 0.44466675 0.37171226 0.16945156] - -mean value: 0.40855018487405237 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.74418605 0.75555556 0.64864865 0.87804878 0.75555556 0.63636364 - 0.61111111 0.625 0.69767442 0.56410256] - -mean value: 0.6916246316941156 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.72727273 0.70833333 0.75 0.9 0.70833333 0.60869565 - 0.6875 0.83333333 0.65217391 0.57894737] - -mean value: 0.7154589660911173 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -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( -[0.76190476 0.80952381 0.57142857 0.85714286 0.80952381 0.66666667 - 0.55 0.5 0.75 0.55 ] - -mean value: 0.6826190476190476 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.73809524 0.73809524 0.68292683 0.87804878 0.73170732 0.6097561 - 0.65853659 0.70731707 0.68292683 0.58536585] - -mean value: 0.7012775842044133 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.73809524 0.73809524 0.68571429 0.87857143 0.7297619 0.60833333 - 0.65595238 0.70238095 0.68452381 0.58452381] - -mean value: 0.7005952380952382 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.59259259 0.60714286 0.48 0.7826087 0.60714286 0.46666667 - 0.44 0.45454545 0.53571429 0.39285714] - -mean value: 0.535927055231403 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 148 - -mean value: 148.0 - -key: FP -value: 65 - -mean value: 65.0 - -key: FN -value: 58 - -mean value: 58.0 - -key: TP -value: 141 - -mean value: 141.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.59 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.66840959 1.6835866 1.67349887 1.60515428 1.62686896 1.62213206 - 1.61394548 1.60469913 1.62373829 1.62986398] - -mean value: 1.6351897239685058 - -key: score_time -value: [0.15618062 0.0988071 0.09145093 0.09110022 0.09156513 0.09141111 - 0.09112048 0.09319663 0.0919323 0.09166527] - -mean value: 0.09884297847747803 - -key: test_mcc -value: [0.90889326 1. 0.80817439 0.90649828 1. 0.80817439 - 0.90238095 0.8547619 0.90692382 0.95238095] - -mean value: 0.9048187955773696 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95454545 1. 0.90909091 0.95454545 1. 0.90909091 - 0.95 0.92682927 0.95238095 0.97560976] - -mean value: 0.9532092704043922 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.91304348 1. 0.86956522 0.91304348 1. 0.86956522 - 0.95 0.9047619 0.90909091 0.95238095] - -mean value: 0.9281451157538113 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95238095 1. 1. 0.95238095 - 0.95 0.95 1. 1. ] - -mean value: 0.9804761904761904 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 1. 0.90243902 0.95121951 1. 0.90243902 - 0.95121951 0.92682927 0.95121951 0.97560976] - -mean value: 0.9513356562137052 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 1. 0.90119048 0.95 1. 0.90119048 - 0.95119048 0.92738095 0.95238095 0.97619048] - -mean value: 0.9511904761904763 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.91304348 1. 0.83333333 0.91304348 1. 0.83333333 - 0.9047619 0.86363636 0.90909091 0.95238095] - -mean value: 0.9122623753058535 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 190 - -mean value: 190.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 202 - -mean value: 202.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.88 - -Accuracy on Blind test: 0.95 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.96576428 1.03775167 0.95333004 0.91306329 0.939183 0.94196749 - 0.9377737 0.94258761 0.93521285 0.94724011] - -mean value: 0.9513874053955078 - -key: score_time -value: [0.22685552 0.20241451 0.17469525 0.20963097 0.21572113 0.19469857 - 0.20701981 0.23400688 0.21420932 0.19970131] - -mean value: 0.20789532661437987 - -key: test_mcc -value: [0.90889326 0.95346259 0.80817439 0.80817439 0.90238095 0.7565654 - 0.85441771 0.8047619 0.86333169 0.90692382] - -mean value: 0.8567086116301835 - -key: train_mcc -value: [0.98391316 0.97860715 0.98395676 0.98395676 0.97866529 0.97866529 - 0.97866283 0.98395537 0.97866283 0.9946235 ] - -mean value: 0.9823668931195029 - -key: test_fscore -value: [0.95454545 0.97560976 0.90909091 0.90909091 0.95238095 0.88372093 - 0.92307692 0.9 0.93023256 0.95238095] - -mean value: 0.9290129345035755 - -key: train_fscore -value: [0.9919571 0.98930481 0.9919571 0.9919571 0.98930481 0.98930481 - 0.9893617 0.992 0.9893617 0.99731903] - -mean value: 0.9911828191283462 - -key: test_precision -value: [0.91304348 1. 0.86956522 0.86956522 0.95238095 0.86363636 - 0.94736842 0.9 0.86956522 0.90909091] - -mean value: 0.9094215776595638 - -key: train_precision -value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598 - 0.97894737 0.98412698 0.97894737 0.99465241] - -mean value: 0.9825309723468607 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619 - 0.9 0.9 1. 1. ] - -mean value: 0.9514285714285714 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 0.97619048 0.90243902 0.90243902 0.95121951 0.87804878 - 0.92682927 0.90243902 0.92682927 0.95121951] - -mean value: 0.9270034843205576 - -key: train_accuracy -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( -[0.99189189 0.98918919 0.99191375 0.99191375 0.98921833 0.98921833 - 0.98921833 0.99191375 0.98921833 0.99730458] - -mean value: 0.9911000218547388 - -key: test_roc_auc -value: [0.95238095 0.97619048 0.90119048 0.90119048 0.95119048 0.87738095 - 0.92619048 0.90238095 0.92857143 0.95238095] - -mean value: 0.9269047619047619 - -key: train_roc_auc -value: [0.99189189 0.98918919 0.99193548 0.99193548 0.98924731 0.98924731 - 0.98918919 0.99189189 0.98918919 0.9972973 ] - -mean value: 0.9911014240046498 - -key: test_jcc -value: [0.91304348 0.95238095 0.83333333 0.83333333 0.90909091 0.79166667 - 0.85714286 0.81818182 0.86956522 0.90909091] - -mean value: 0.8686829474872952 - -key: train_jcc -value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598 - 0.97894737 0.98412698 0.97894737 0.99465241] - -mean value: 0.9825309723468607 - -key: TN -value: 186 - -mean value: 186.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 196 - -mean value: 196.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.10359287 0.05772686 0.06652761 0.057899 0.05960846 0.05904317 - 0.06005931 0.20866203 0.05684566 0.05950522] - -mean value: 0.07894701957702636 - -key: score_time -value: [0.01138711 0.01046109 0.01090193 0.01055264 0.01067448 0.0106988 - 0.01056552 0.01120353 0.01074457 0.01049185] - -mean value: 0.01076815128326416 - -key: test_mcc -value: [1. 0.95346259 0.95238095 0.85441771 0.95238095 0.95238095 - 0.90649828 0.86333169 0.90238095 0.95238095] - -mean value: 0.928961503294655 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97674419 0.97560976 0.93023256 0.97560976 0.97560976 - 0.94736842 0.93023256 0.95 0.97560976] - -mean value: 0.9637016747768457 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95454545 1. 0.90909091 1. 1. - 1. 0.86956522 0.95 0.95238095] - -mean value: 0.963558253340862 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.95238095 0.95238095 0.95238095 0.95238095 - 0.9 1. 0.95 1. ] - -mean value: 0.9659523809523808 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97619048 0.97560976 0.92682927 0.97560976 0.97560976 - 0.95121951 0.92682927 0.95121951 0.97560976] - -mean value: 0.963472706155633 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97619048 0.97619048 0.92619048 0.97619048 0.97619048 - 0.95 0.92857143 0.95119048 0.97619048] - -mean value: 0.9636904761904761 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.95454545 0.95238095 0.86956522 0.95238095 0.95238095 - 0.9 0.86956522 0.9047619 0.95238095] - -mean value: 0.9307961603613778 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 198 - -mean value: 198.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 199 - -mean value: 199.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03956175 0.06763196 0.06599092 0.04733467 0.07237792 0.04720092 - 0.08412266 0.0399518 0.10612369 0.05623484] - -mean value: 0.06265311241149903 - -key: score_time -value: [0.02280021 0.02403569 0.02058625 0.01286125 0.01242781 0.02172351 - 0.0125792 0.02332878 0.03291178 0.02500701] - -mean value: 0.020826148986816406 - -key: test_mcc -value: [1. 0.90889326 0.76500781 0.80907152 0.8547619 0.8547619 - 0.90649828 0.60952381 0.90238095 0.7098505 ] - -mean value: 0.8320749938182688 - -key: train_mcc -value: [0.97843556 0.98379816 0.98384191 0.98384144 0.9784365 0.98384191 - 0.97305937 0.97306016 0.96227841 0.98384191] - -mean value: 0.9784435309419832 - -key: test_fscore -value: [1. 0.95 0.87179487 0.9 0.92682927 0.92682927 - 0.94736842 0.8 0.95 0.84210526] - -mean value: 0.9114927092590766 - -key: train_fscore -value: [0.98924731 0.99191375 0.99191375 0.99186992 0.98918919 0.99191375 - 0.98659517 0.98652291 0.98113208 0.99191375] - -mean value: 0.9892211567024891 - -key: test_precision -value: [1. 1. 0.94444444 0.94736842 0.95 0.95 - 1. 0.8 0.95 0.88888889] - -mean value: 0.9430701754385964 - -key: train_precision -value: [0.98395722 0.98924731 0.98924731 0.99456522 0.98918919 0.98924731 - 0.98395722 0.98918919 0.98378378 0.99459459] - -mean value: 0.9886978348134606 - -key: test_recall -value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.9047619 - 0.9 0.8 0.95 0.8 ] - -mean value: 0.883095238095238 - -key: train_recall -value: [0.99459459 0.99459459 0.99459459 0.98918919 0.98918919 0.99459459 - 0.98924731 0.98387097 0.97849462 0.98924731] - -mean value: 0.9897616971810521 - -key: test_accuracy -value: [1. 0.95238095 0.87804878 0.90243902 0.92682927 0.92682927 - 0.95121951 0.80487805 0.95121951 0.85365854] - -mean value: 0.9147502903600465 - -key: train_accuracy -value: [0.98918919 0.99189189 0.99191375 0.99191375 0.98921833 0.99191375 - 0.98652291 0.98652291 0.98113208 0.99191375] - -mean value: 0.9892132294019087 - -key: test_roc_auc -value: [1. 0.95238095 0.8797619 0.90357143 0.92738095 0.92738095 - 0.95 0.8047619 0.95119048 0.85238095] - -mean value: 0.9148809523809524 - -key: train_roc_auc -value: [0.98918919 0.99189189 0.99192095 0.99190642 0.98921825 0.99192095 - 0.98651555 0.98653008 0.9811392 0.99192095] - -mean value: 0.9892153443766347 - -key: test_jcc -value: [1. 0.9047619 0.77272727 0.81818182 0.86363636 0.86363636 - 0.9 0.66666667 0.9047619 0.72727273] - -mean value: 0.8421645021645021 - -key: train_jcc -value: [0.9787234 0.98395722 0.98395722 0.98387097 0.97860963 0.98395722 - 0.97354497 0.97340426 0.96296296 0.98395722] - -mean value: 0.9786945066498138 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 182 - -mean value: 182.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01337481 0.01335168 0.00990558 0.00938869 0.01046014 0.01006222 - 0.00962472 0.00944638 0.00971985 0.0095315 ] - -mean value: 0.010486555099487305 - -key: score_time -value: [0.01207018 0.01088357 0.00918961 0.00873971 0.00949311 0.00893283 - 0.00898933 0.00877428 0.00887108 0.00888586] - -mean value: 0.009482955932617188 - -key: test_mcc -value: [0.38138504 0.43052839 0.16909989 0.12803277 0.56190476 0.51320273 - 0.31960727 0.16945156 0.47003614 0.41428571] - -mean value: 0.355753425845874 - -key: train_mcc -value: [0.37851665 0.42162778 0.48790839 0.46638766 0.43403959 0.47709968 - 0.4555575 0.46631793 0.41295769 0.48248903] - -mean value: 0.4482901898810191 - -key: test_fscore -value: [0.69767442 0.72727273 0.62222222 0.52631579 0.7804878 0.77272727 - 0.61111111 0.56410256 0.74418605 0.7 ] - -mean value: 0.6746099956903909 - -key: train_fscore -value: [0.69333333 0.7115903 0.74114441 0.73458445 0.71849866 0.73854447 - 0.73066667 0.73315364 0.69972452 0.74331551] - -mean value: 0.724455595971974 - -key: test_precision -value: [0.68181818 0.69565217 0.58333333 0.58823529 0.8 0.73913043 - 0.6875 0.57894737 0.69565217 0.7 ] - -mean value: 0.675026896029891 - -key: train_precision -value: [0.68421053 0.70967742 0.74725275 0.7287234 0.71276596 0.73655914 - 0.72486772 0.73513514 0.71751412 0.7393617 ] - -mean value: 0.7236067880834754 - -key: test_recall -value: [0.71428571 0.76190476 0.66666667 0.47619048 0.76190476 0.80952381 - 0.55 0.55 0.8 0.7 ] - -mean value: 0.6790476190476191 - -key: train_recall -value: [0.7027027 0.71351351 0.73513514 0.74054054 0.72432432 0.74054054 - 0.73655914 0.7311828 0.6827957 0.74731183] - -mean value: 0.7254606219122348 - -key: test_accuracy -value: [0.69047619 0.71428571 0.58536585 0.56097561 0.7804878 0.75609756 - 0.65853659 0.58536585 0.73170732 0.70731707] - -mean value: 0.6770615563298489 - -key: train_accuracy -value: [0.68918919 0.71081081 0.74393531 0.73315364 0.71698113 0.73854447 - 0.7277628 0.73315364 0.70619946 0.74123989] - -mean value: 0.7240970350404312 - -key: test_roc_auc -value: [0.69047619 0.71428571 0.58333333 0.56309524 0.78095238 0.7547619 - 0.65595238 0.58452381 0.73333333 0.70714286] - -mean value: 0.6767857142857143 - -key: train_roc_auc -value: [0.68918919 0.71081081 0.74391165 0.7331735 0.71700087 0.73854984 - 0.72773903 0.73315897 0.70626271 0.74122348] - -mean value: 0.7241020052310374 - -key: test_jcc -value: [0.53571429 0.57142857 0.4516129 0.35714286 0.64 0.62962963 - 0.44 0.39285714 0.59259259 0.53846154] - -mean value: 0.5149439521052425 - -key: train_jcc -value: [0.53061224 0.55230126 0.58874459 0.58050847 0.56066946 0.58547009 - 0.57563025 0.5787234 0.53813559 0.59148936] - -mean value: 0.5682284716264602 - -key: TN -value: 139 - -mean value: 139.0 - -key: FP -value: 66 - -mean value: 66.0 - -key: FN -value: 67 - -mean value: 67.0 - -key: TP -value: 140 - -mean value: 140.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.43 - -Accuracy on Blind test: 0.72 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01812387 0.01968002 0.02329946 0.02299213 0.02618003 0.02688098 - 0.02359438 0.02325869 0.02430868 0.02280593] - -mean value: 0.02311241626739502 - -key: score_time -value: [0.00875401 0.01142693 0.01191044 0.01212454 0.01197791 0.01205945 - 0.0119586 0.0119884 0.01208591 0.0119648 ] - -mean value: 0.011625099182128906 - -key: test_mcc -value: [1. 0.95346259 0.95238095 0.90238095 1. 0.95238095 - 0.90649828 0.76500781 0.95238095 0.90238095] - -mean value: 0.9286873437646441 - -key: train_mcc -value: [0.97837838 0.97310093 0.98927606 0.978494 0.98384191 0.98384191 - 0.9784365 0.98927544 0.9784365 0.97317407] - -mean value: 0.9806255690715325 - -key: test_fscore -value: [1. 0.97560976 0.97560976 0.95238095 1. 0.97560976 - 0.94736842 0.88372093 0.97560976 0.95 ] - -mean value: 0.9635909328056386 - -key: train_fscore -value: [0.98918919 0.98659517 0.99462366 0.98924731 0.99191375 0.99191375 - 0.98924731 0.99465241 0.98924731 0.98644986] - -mean value: 0.9903079719026987 - -key: test_precision -value: [1. 1. 1. 0.95238095 1. 1. - 1. 0.82608696 0.95238095 0.95 ] - -mean value: 0.9680848861283643 - -key: train_precision -value: [0.98918919 0.9787234 0.98930481 0.98395722 0.98924731 0.98924731 - 0.98924731 0.9893617 0.98924731 0.99453552] - -mean value: 0.9882061094095242 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 - 0.9 0.95 1. 0.95 ] - -mean value: 0.9609523809523808 - -key: train_recall -value: [0.98918919 0.99459459 1. 0.99459459 0.99459459 0.99459459 - 0.98924731 1. 0.98924731 0.97849462] - -mean value: 0.9924556814879397 - -key: test_accuracy -value: [1. 0.97619048 0.97560976 0.95121951 1. 0.97560976 - 0.95121951 0.87804878 0.97560976 0.95121951] - -mean value: 0.963472706155633 - -key: train_accuracy -value: [0.98918919 0.98648649 0.99460916 0.98921833 0.99191375 0.99191375 - 0.98921833 0.99460916 0.98921833 0.98652291] - -mean value: 0.9902899395352224 - -key: test_roc_auc -value: [1. 0.97619048 0.97619048 0.95119048 1. 0.97619048 - 0.95 0.8797619 0.97619048 0.95119048] - -mean value: 0.9636904761904761 - -key: train_roc_auc -value: [0.98918919 0.98648649 0.99462366 0.98923278 0.99192095 0.99192095 - 0.98921825 0.99459459 0.98921825 0.98654461] - -mean value: 0.9902949723917466 - -key: test_jcc -value: [1. 0.95238095 0.95238095 0.90909091 1. 0.95238095 - 0.9 0.79166667 0.95238095 0.9047619 ] - -mean value: 0.931504329004329 - -key: train_jcc -value: [0.97860963 0.97354497 0.98930481 0.9787234 0.98395722 0.98395722 - 0.9787234 0.9893617 0.9787234 0.97326203] - -mean value: 0.9808167797529499 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 198 - -mean value: 198.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.92 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01709509 0.01672006 0.01696658 0.01680875 0.01753879 0.01735544 - 0.01576948 0.01762867 0.01622939 0.01648998] - -mean value: 0.016860222816467284 - -key: score_time -value: [0.01177645 0.01180029 0.01178408 0.01206207 0.01212597 0.01212263 - 0.01181698 0.01177001 0.0121057 0.01177335] - -mean value: 0.011913752555847168 - -key: test_mcc -value: [0.8660254 0.82462113 0.74124932 0.90238095 0.95238095 0.85441771 - 0.90238095 0.7565654 0.86333169 0.67700771] - -mean value: 0.8340361214926171 - -key: train_mcc -value: [0.84285241 0.87540087 0.90229328 0.978494 0.92586351 0.90487588 - 0.96294605 0.97317407 0.90722239 0.80791198] - -mean value: 0.908103443769595 - -key: test_fscore -value: [0.92307692 0.89473684 0.83333333 0.95238095 0.97560976 0.93023256 - 0.95 0.87179487 0.93023256 0.8 ] - -mean value: 0.9061397795067976 - -key: train_fscore -value: [0.90909091 0.93142857 0.94586895 0.98924731 0.96111111 0.95287958 - 0.98153034 0.98644986 0.95384615 0.88288288] - -mean value: 0.9494335674714925 - -key: test_precision -value: [1. 1. 1. 0.95238095 1. 0.90909091 - 0.95 0.89473684 0.86956522 0.93333333] - -mean value: 0.9509107254301762 - -key: train_precision -value: [0.99358974 0.98787879 1. 0.98395722 0.98857143 0.92385787 - 0.96373057 0.99453552 0.91176471 1. ] - -mean value: 0.9747885842267824 - -key: test_recall -value: [0.85714286 0.80952381 0.71428571 0.95238095 0.95238095 0.95238095 - 0.95 0.85 1. 0.7 ] - -mean value: 0.8738095238095237 - -key: train_recall -value: [0.83783784 0.88108108 0.8972973 0.99459459 0.93513514 0.98378378 - 1. 0.97849462 1. 0.79032258] - -mean value: 0.9298546934030807 - -key: test_accuracy -value: [0.92857143 0.9047619 0.85365854 0.95121951 0.97560976 0.92682927 - 0.95121951 0.87804878 0.92682927 0.82926829] - -mean value: 0.9126016260162603 - -key: train_accuracy -value: [0.91621622 0.93513514 0.94878706 0.98921833 0.96226415 0.95148248 - 0.98113208 0.98652291 0.95148248 0.89487871] - -mean value: 0.9517119545421433 - -key: test_roc_auc -value: [0.92857143 0.9047619 0.85714286 0.95119048 0.97619048 0.92619048 - 0.95119048 0.87738095 0.92857143 0.82619048] - -mean value: 0.9127380952380951 - -key: train_roc_auc -value: [0.91621622 0.93513514 0.94864865 0.98923278 0.96219122 0.95156931 - 0.98108108 0.98654461 0.95135135 0.89516129] - -mean value: 0.9517131647776809 - -key: test_jcc -value: [0.85714286 0.80952381 0.71428571 0.90909091 0.95238095 0.86956522 - 0.9047619 0.77272727 0.86956522 0.66666667] - 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'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.18844795 0.16931653 0.1712935 0.17028832 0.17033982 0.16963387 - 0.16861916 0.16729665 0.17221189 0.16930819] - -mean value: 0.17167558670043945 - -key: score_time -value: [0.0153811 0.01510787 0.01513457 0.01511717 0.01520443 0.01527405 - 0.01510739 0.01514196 0.01515222 0.01510954] - -mean value: 0.015173029899597169 - -key: test_mcc -value: [1. 0.95346259 1. 0.85441771 0.95238095 0.95238095 - 0.90238095 0.75714286 1. 0.95238095] - -mean value: 0.9324546963771422 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97674419 1. 0.93023256 0.97560976 0.97560976 - 0.95 0.87804878 1. 0.97560976] - -mean value: 0.9661854792966533 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95454545 1. 0.90909091 1. 1. - 0.95 0.85714286 1. 0.95238095] - -mean value: 0.9623160173160172 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95238095 0.95238095 0.95238095 - 0.95 0.9 1. 1. ] - -mean value: 0.9707142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97619048 1. 0.92682927 0.97560976 0.97560976 - 0.95121951 0.87804878 1. 0.97560976] - -mean value: 0.9659117305458768 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97619048 1. 0.92619048 0.97619048 0.97619048 - 0.95119048 0.87857143 1. 0.97619048] - -mean value: 0.9660714285714287 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.95454545 1. 0.86956522 0.95238095 0.95238095 - 0.9047619 0.7826087 1. 0.95238095] - -mean value: 0.9368624129493694 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 198 - -mean value: 198.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.04259586 0.0584991 0.04867768 0.05368423 0.04338527 0.05776286 - 0.04904056 0.05466557 0.06025934 0.04896426] - -mean value: 0.05175347328186035 - -key: score_time -value: [0.02193522 0.02411842 0.02880406 0.02554083 0.02484798 0.02451158 - 0.0243988 0.0265336 0.03643608 0.02784085] - -mean value: 0.026496744155883788 - 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0.9 1. 0.95 1. ] - -mean value: 0.9516666666666665 - -key: train_recall -value: [0.99459459 1. 0.99459459 0.98918919 1. 0.99459459 - 1. 1. 1. 0.99462366] - -mean value: 0.9967596628886952 - -key: test_accuracy -value: [0.95238095 0.95238095 0.97560976 0.90243902 0.95121951 0.95121951 - 0.95121951 0.97560976 0.92682927 0.97560976] - -mean value: 0.951451800232288 - -key: train_accuracy -value: [0.99459459 0.99459459 0.99460916 0.99460916 0.99730458 0.99460916 - 1. 0.99730458 1. 0.99730458] - -mean value: 0.9964930429081372 - -key: test_roc_auc -value: [0.95238095 0.95238095 0.97619048 0.9 0.95238095 0.95119048 - 0.95 0.97619048 0.92738095 0.97619048] - -mean value: 0.9514285714285714 - -key: train_roc_auc -value: [0.99459459 0.99459459 0.99460913 0.99459459 0.99731183 0.99460913 - 1. 0.9972973 1. 0.99731183] - -mean value: 0.9964922987503634 - -key: test_jcc -value: [0.9047619 0.90909091 0.95238095 0.84 0.9047619 0.90909091 - 0.9 0.95238095 0.86363636 0.95238095] - -mean value: 0.9088484848484848 - 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... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.12224102 0.10535049 0.1016345 0.0726614 0.08337069 0.09313035 - 0.13533998 0.11137009 0.06969023 0.06248879] - -mean value: 0.09572775363922119 - -key: score_time -value: [0.02381134 0.01407385 0.02311468 0.01469684 0.02323651 0.01405168 - 0.02257681 0.028687 0.01425099 0.01401615] - -mean value: 0.019251585006713867 - -key: test_mcc -value: [0.57735027 0.57207755 0.31666667 0.37171226 0.70714286 0.61152662 - 0.53206577 0.51320273 0.65871309 0.51190476] - -mean value: 0.5372362570684607 - -key: train_mcc -value: [0.93039599 0.93039599 0.91978391 0.89264025 0.90846996 0.92500526 - 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warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -[0.83333333 0.8 0.66666667 0.72222222 0.85714286 0.7826087 - 0.85714286 0.77777778 0.84210526 0.75 ] - -mean value: 0.7888999673095782 - -key: train_precision -value: [0.98314607 0.98314607 0.97752809 0.96089385 0.96153846 0.97765363 - 0.96721311 0.97237569 0.97206704 0.97222222] - 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-mean value: 0.9582011043301366 - -key: test_jcc -value: [0.625 0.64 0.5 0.5 0.75 0.69230769 - 0.54545455 0.58333333 0.69565217 0.6 ] - -mean value: 0.6131747745008613 - -key: train_jcc -value: [0.93085106 0.93085106 0.92063492 0.89583333 0.91145833 0.92592593 - 0.921875 0.92146597 0.91099476 0.91623037] - -mean value: 0.9186120740363529 - -key: TN -value: 165 - -mean value: 165.0 - -key: FP -value: 55 - -mean value: 55.0 - -key: FN -value: 41 - -mean value: 41.0 - -key: TP -value: 151 - -mean value: 151.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.7 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.67548418 0.65576053 0.66834664 0.65980339 0.65404487 0.61867952 - 0.65014267 0.65180707 0.64954734 0.66304302] - -mean value: 0.6546659231185913 - -key: score_time -value: [0.0093596 0.00954175 0.00971174 0.00954843 0.00953078 0.00948763 - 0.01077414 0.00926828 0.00934982 0.00933218] - -mean value: 0.009590435028076171 - -key: test_mcc -value: [1. 0.9047619 1. 0.86240942 0.95238095 0.95238095 - 0.90649828 0.95238095 0.90238095 0.95238095] - -mean value: 0.9385574361533285 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.95238095 1. 0.93333333 0.97560976 0.97560976 - 0.94736842 0.97560976 0.95 0.97560976] - -mean value: 0.968552173115716 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.95238095 1. 0.875 1. 1. - 1. 0.95238095 0.95 0.95238095] - -mean value: 0.9682142857142857 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95238095 1. 1. 0.95238095 0.95238095 - 0.9 1. 0.95 1. ] - -mean value: 0.9707142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.95238095 1. 0.92682927 0.97560976 0.97560976 - 0.95121951 0.97560976 0.95121951 0.97560976] - -mean value: 0.9684088269454122 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95238095 1. 0.925 0.97619048 0.97619048 - 0.95 0.97619048 0.95119048 0.97619048] - -mean value: 0.9683333333333332 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.90909091 1. 0.875 0.95238095 0.95238095 - 0.9 0.95238095 0.9047619 0.95238095] - -mean value: 0.9398376623376624 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.91 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02539349 0.02820396 0.02839875 0.02784157 0.02765083 0.02783728 - 0.02735615 0.02679372 0.02673268 0.02854919] - -mean value: 0.027475762367248534 - -key: score_time -value: [0.01260948 0.01275754 0.01296639 0.01299047 0.01315665 0.01306176 - 0.01306844 0.01392341 0.01316857 0.01312613] - -mean value: 0.0130828857421875 - -key: test_mcc -value: [0.8660254 0.71428571 0.59335232 0.7633652 0.56190476 0.73786479 - 0.62325386 0.56086079 0.76500781 0.7098505 ] - -mean value: 0.6895771149319809 - -key: train_mcc -value: [0.88737794 0.88252261 0.95692987 0.92023091 0.92993316 0.95215551 - 0.92586351 0.940826 0.95261005 0.91637608] - -mean value: 0.9264825641731319 - -key: test_fscore -value: [0.92307692 0.85714286 0.81632653 0.88888889 0.7804878 0.875 - 0.81818182 0.76923077 0.88372093 0.84210526] - -mean value: 0.8454161785402003 - -key: train_fscore -value: [0.93678161 0.93371758 0.97849462 0.96042216 0.96495957 0.97520661 - 0.96335079 0.9701897 0.97520661 0.95530726] - -mean value: 0.9613636517371242 - -key: test_precision -value: [1. 0.85714286 0.71428571 0.83333333 0.8 0.77777778 - 0.75 0.78947368 0.82608696 0.88888889] - -mean value: 0.8236989212160838 - -key: train_precision -value: [1. 1. 0.97326203 0.93814433 0.96236559 0.99438202 - 0.93877551 0.97814208 1. 0.99418605] - -mean value: 0.9779257609070671 - -key: test_recall -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") -[0.85714286 0.85714286 0.95238095 0.95238095 0.76190476 1. - 0.9 0.75 0.95 0.8 ] - -mean value: 0.878095238095238 - -key: train_recall -value: [0.88108108 0.87567568 0.98378378 0.98378378 0.96756757 0.95675676 - 0.98924731 0.96236559 0.9516129 0.91935484] - -mean value: 0.947122929380994 - -key: test_accuracy -value: [0.92857143 0.85714286 0.7804878 0.87804878 0.7804878 0.85365854 - 0.80487805 0.7804878 0.87804878 0.85365854] - -mean value: 0.8395470383275262 - -key: train_accuracy -value: [0.94054054 0.93783784 0.97843666 0.95956873 0.96495957 0.97574124 - 0.96226415 0.9703504 0.97574124 0.95687332] - -mean value: 0.9622313688351424 - -key: test_roc_auc -value: [0.92857143 0.85714286 0.77619048 0.87619048 0.78095238 0.85 - 0.80714286 0.7797619 0.8797619 0.85238095] - -mean value: 0.8388095238095239 - -key: train_roc_auc -value: [0.94054054 0.93783784 0.97845103 0.95963383 0.96496658 0.97569021 - 0.96219122 0.97037198 0.97580645 0.95697472] - -mean value: 0.9622464399883756 - -key: test_jcc -value: [0.85714286 0.75 0.68965517 0.8 0.64 0.77777778 - 0.69230769 0.625 0.79166667 0.72727273] - -mean value: 0.7350822893581515 - -key: train_jcc -value: [0.88108108 0.87567568 0.95789474 0.92385787 0.93229167 0.9516129 - 0.92929293 0.94210526 0.9516129 0.9144385 ] - -mean value: 0.9259863529862067 - -key: TN -value: 171 - -mean value: 171.0 - -key: FP -value: 32 - -mean value: 32.0 - -key: FN -value: 35 - -mean value: 35.0 - -key: TP -value: 174 - -mean value: 174.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.13 - -Accuracy on Blind test: 0.61 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02332497 0.03366351 0.03370738 0.03406048 0.03355503 0.03292322 - 0.03243351 0.03156877 0.05635238 0.03871083] - -mean value: 0.03503000736236572 - -key: score_time -value: [0.02165461 0.02171969 0.02165103 0.02171063 0.02163982 0.02164435 - 0.02162695 0.02164865 0.02405787 0.02173615] - -mean value: 0.02190897464752197 - -key: test_mcc -value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095 - 0.90649828 0.60952381 0.95238095 0.95238095] - -mean value: 0.8941993531889617 - -key: train_mcc -value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407 - 0.96771006 0.9784365 0.96771006 0.97305937] - -mean value: 0.9687647972123978 - -key: test_fscore -value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976 - 0.94736842 0.8 0.97560976 0.97560976] - -mean value: 0.9455788862399903 - -key: train_fscore -value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517 - 0.98395722 0.98924731 0.98395722 0.98659517] - -mean value: 0.9844228567737392 - -key: test_precision -value: [1. 0.95238095 1. 0.95 0.95238095 1. - 1. 0.8 0.95238095 0.95238095] - -mean value: 0.955952380952381 - -key: train_precision -value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234 - 0.9787234 0.98924731 0.9787234 0.98395722] - -mean value: 0.9802247596621612 - -key: test_recall -value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095 - 0.9 0.8 1. 1. ] - -mean value: 0.9366666666666668 - -key: train_recall -value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459 - 0.98924731 0.98924731 0.98924731 0.98924731] - -mean value: 0.9886718977041558 - -key: test_accuracy -value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976 - 0.95121951 0.80487805 0.97560976 0.97560976] - -mean value: 0.9464576074332172 - -key: train_accuracy -value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291 - 0.98382749 0.98921833 0.98382749 0.98652291] - -mean value: 0.9843571064325781 - -key: test_roc_auc -value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048 - 0.95 0.8047619 0.97619048 0.97619048] - -mean value: 0.9466666666666667 - -key: train_roc_auc -value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461 - 0.98381285 0.98921825 0.98381285 0.98651555] - -mean value: 0.9843577448416159 - -key: test_jcc -value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095 - 0.9 0.66666667 0.95238095 0.95238095] - -mean value: 0.9010389610389611 - -key: train_jcc -value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497 - 0.96842105 0.9787234 0.96842105 0.97354497] - -mean value: 0.969339750084431 - -key: TN -value: 197 - -mean value: 197.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.24888968 0.24928164 0.25829434 0.29429793 0.29187274 0.25702906 - 0.25375938 0.25846148 0.25591302 0.25412965] - -mean value: 0.26219289302825927 - -key: score_time -value: [0.02372575 0.02388597 0.02393842 0.0240922 0.02259684 0.02371073 - 0.02040315 0.02021837 0.02421808 0.02299547] - -mean value: 0.022978496551513673 - -key: test_mcc -value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095 - 0.90649828 0.65952381 0.95238095 0.95238095] - -mean value: 0.8991993531889616 - -key: train_mcc -value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407 - 0.96771006 0.9784365 0.96771006 0.97305937] - -mean value: 0.9687647972123978 - -key: test_fscore -value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976 - 0.94736842 0.82926829 0.97560976 0.97560976] - -mean value: 0.9485057155082831 - -key: train_fscore -value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517 - 0.98395722 0.98924731 0.98395722 0.98659517] - -mean value: 0.9844228567737392 - -key: test_precision -value: [1. 0.95238095 1. 0.95 0.95238095 1. - 1. 0.80952381 0.95238095 0.95238095] - -mean value: 0.9569047619047619 - -key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: 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 - smnc_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: 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 - smnc_CV['Resampling'] = rs_smnc -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: 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 - smnc_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: 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 - smnc_BT['Resampling'] = rs_smnc -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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_precision -value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234 - 0.9787234 0.98924731 0.9787234 0.98395722] - -mean value: 0.9802247596621612 - -key: test_recall -value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095 - 0.9 0.85 1. 1. ] - -mean value: 0.9416666666666667 - -key: train_recall -value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459 - 0.98924731 0.98924731 0.98924731 0.98924731] - -mean value: 0.9886718977041558 - -key: test_accuracy -value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976 - 0.95121951 0.82926829 0.97560976 0.97560976] - -mean value: 0.948896631823461 - -key: train_accuracy -value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291 - 0.98382749 0.98921833 0.98382749 0.98652291] - -mean value: 0.9843571064325781 - -key: test_roc_auc -value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048 - 0.95 0.8297619 0.97619048 0.97619048] - -mean value: 0.9491666666666665 - -key: train_roc_auc -value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461 - 0.98381285 0.98921825 0.98381285 0.98651555] - -mean value: 0.9843577448416159 - -key: test_jcc -value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095 - 0.9 0.70833333 0.95238095 0.95238095] - -mean value: 0.9052056277056277 - -key: train_jcc -value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497 - 0.96842105 0.9787234 0.96842105 0.97354497] - -mean value: 0.969339750084431 - -key: TN -value: 197 - -mean value: 197.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 194 - -mean value: 194.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03052306 0.03408241 0.03512359 0.03415132 0.03537679 0.03478885 - 0.03490472 0.03314066 0.03462148 0.06600475] - -mean value: 0.037271761894226076 - -key: score_time -value: [0.01202464 0.01207113 0.01203728 0.01235628 0.01250768 0.01239419 - 0.01239467 0.01211047 0.01261497 0.01401401] - -mean value: 0.012452530860900878 - -key: test_mcc -value: [0.95346259 0.90889326 0.7565654 0.76500781 0.95238095 0.86333169 - 0.80817439 0.60952381 0.8547619 0.8047619 ] - -mean value: 0.8276863714013631 - -key: train_mcc -value: [0.91925472 0.94055428 0.90316864 0.91379661 0.89789222 0.92473841 - 0.92458368 0.91396351 0.91947678 0.91947678] - -mean value: 0.91769056480883 - -key: test_fscore -value: [0.97674419 0.95 0.88372093 0.87179487 0.97560976 0.92307692 - 0.89473684 0.8 0.92682927 0.9 ] - -mean value: 0.9102512777646373 - -key: train_fscore -value: [0.95890411 0.9701897 0.95081967 0.95652174 0.94794521 0.96174863 - 0.96216216 0.95652174 0.95912807 0.95912807] - -mean value: 0.9583069094189665 - -key: test_precision -value: [0.95454545 1. 0.86363636 0.94444444 1. 1. - 0.94444444 0.8 0.9047619 0.9 ] - -mean value: 0.9311832611832612 - -key: train_precision -value: [0.97222222 0.97282609 0.96132597 0.96174863 0.96111111 0.97237569 - 0.9673913 0.96703297 0.97237569 0.97237569] - -mean value: 0.968078536422446 - -key: test_recall -value: [1. 0.9047619 0.9047619 0.80952381 0.95238095 0.85714286 - 0.85 0.8 0.95 0.9 ] - -mean value: 0.8928571428571429 - -key: train_recall -value: [0.94594595 0.96756757 0.94054054 0.95135135 0.93513514 0.95135135 - 0.95698925 0.94623656 0.94623656 0.94623656] - -mean value: 0.9487590816623076 - -key: test_accuracy -value: [0.97619048 0.95238095 0.87804878 0.87804878 0.97560976 0.92682927 - 0.90243902 0.80487805 0.92682927 0.90243902] - -mean value: 0.9123693379790941 - -key: train_accuracy -value: [0.95945946 0.97027027 0.95148248 0.95687332 0.94878706 0.96226415 - 0.96226415 0.95687332 0.95956873 0.95956873] - -mean value: 0.9587411670430537 - -key: test_roc_auc -value: [0.97619048 0.95238095 0.87738095 0.8797619 0.97619048 0.92857143 - 0.90119048 0.8047619 0.92738095 0.90238095] - -mean value: 0.9126190476190474 - -key: train_roc_auc -value: [0.95945946 0.97027027 0.95145307 0.95685847 0.94875036 0.96223482 - 0.96227841 0.95690206 0.95960477 0.95960477] - -mean value: 0.9587416448706773 - -key: test_jcc -value: [0.95454545 0.9047619 0.79166667 0.77272727 0.95238095 0.85714286 - 0.80952381 0.66666667 0.86363636 0.81818182] - -mean value: 0.8391233766233765 - -key: train_jcc -value: [0.92105263 0.94210526 0.90625 0.91666667 0.90104167 0.92631579 - 0.92708333 0.91666667 0.92146597 0.92146597] - -mean value: 0.9200113954716634 - -key: TN -value: 192 - -mean value: 192.0 - -key: FP -value: 22 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -mean value: 22.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 184 - -mean value: 184.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.8054328 0.84636688 1.01926517 1.05817366 0.7309134 0.71366811 - 0.91611481 0.73690271 0.72525883 0.915658 ] - -mean value: 0.8467754364013672 - -key: score_time -value: [0.01364899 0.01264262 0.01263046 0.01277018 0.01270843 0.01261044 - 0.01264334 0.01270008 0.01384187 0.01266003] - -mean value: 0.012885642051696778 - -key: test_mcc -value: [0.95346259 1. 0.90692382 0.95238095 1. 0.90692382 - 0.90649828 0.70714286 0.95238095 0.90238095] - -mean value: 0.9188094230834079 - -key: train_mcc -value: [1. 0.98379816 1. 0.98384191 0.98921825 0.98384191 - 1. 0.9946235 0.98384144 1. ] - -mean value: 0.9919165157084897 - -key: test_fscore -value: [0.97674419 1. 0.95 0.97560976 1. 0.95 - 0.94736842 0.85 0.97560976 0.95 ] - -mean value: 0.9575332119294264 - -key: train_fscore -value: [1. 0.99191375 1. 0.99191375 0.99459459 0.99191375 - 1. 0.99731903 0.9919571 1. ] - -mean value: 0.9959611973896966 - -key: test_precision -value: [0.95454545 1. 1. 1. 1. 1. - 1. 0.85 0.95238095 0.95 ] - -mean value: 0.9706926406926406 - -key: train_precision -value: [1. 0.98924731 1. 0.98924731 0.99459459 0.98924731 - 1. 0.99465241 0.98930481 1. ] - -mean value: 0.9946293749329802 - -key: test_recall -value: [1. 1. 0.9047619 0.95238095 1. 0.9047619 - 0.9 0.85 1. 0.95 ] - -mean value: 0.9461904761904762 - -key: train_recall -value: [1. 0.99459459 1. 0.99459459 0.99459459 0.99459459 - 1. 1. 0.99462366 1. ] - -mean value: 0.9973002034292356 - -key: test_accuracy -value: [0.97619048 1. 0.95121951 0.97560976 1. 0.95121951 - 0.95121951 0.85365854 0.97560976 0.95121951] - -mean value: 0.9585946573751452 - -key: train_accuracy -value: [1. 0.99189189 1. 0.99191375 0.99460916 0.99191375 - 1. 0.99730458 0.99191375 1. ] - -mean value: 0.9959546878414802 - -key: test_roc_auc -value: [0.97619048 1. 0.95238095 0.97619048 1. 0.95238095 - 0.95 0.85357143 0.97619048 0.95119048] - -mean value: 0.9588095238095237 - -key: train_roc_auc -value: [1. 0.99189189 1. 0.99192095 0.99460913 0.99192095 - 1. 0.9972973 0.99190642 1. ] - -mean value: 0.995954664341761 - -key: test_jcc -value: [0.95454545 1. 0.9047619 0.95238095 1. 0.9047619 - 0.9 0.73913043 0.95238095 0.9047619 ] - -mean value: 0.9212723508375683 - -key: train_jcc -value: [1. 0.98395722 1. 0.98395722 0.98924731 0.98395722 - 1. 0.99465241 0.98404255 1. ] - -mean value: 0.991981392919057 - -key: TN -value: 200 - -mean value: 200.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 194 - -mean value: 194.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01342845 0.01186204 0.01067162 0.00947356 0.00962114 0.00934386 - 0.00946069 0.00932765 0.00938511 0.00942278] - -mean value: 0.010199689865112304 - -key: score_time -value: [0.01247454 0.00964475 0.00987816 0.00903606 0.00877929 0.00873184 - 0.00876999 0.0087111 0.00880551 0.00896764] - -mean value: 0.009379887580871582 - -key: test_mcc -value: [0.42857143 0.47673129 0.23018043 0.26730386 0.57570364 0.56086079 - 0.46428571 0.26904762 0.62325386 0.56836003] - -mean value: 0.44642986720816974 - -key: train_mcc -value: [0.50978658 0.47746734 0.47521493 0.49836503 0.45863438 0.50506482 - 0.52229262 0.51472381 0.45731256 0.498771 ] - -mean value: 0.491763305975787 - -key: test_fscore -value: [0.71428571 0.74418605 0.68 0.65116279 0.80851064 0.79069767 - 0.73170732 0.63414634 0.81818182 0.79069767] - -mean value: 0.7363576015348501 - -key: train_fscore -value: [0.74366197 0.75621891 0.75 0.76262626 0.74168798 0.76691729 - 0.77120823 0.77192982 0.7403599 0.765 ] - -mean value: 0.7569610360657331 - -key: test_precision -value: [0.71428571 0.72727273 0.5862069 0.63636364 0.73076923 0.77272727 - 0.71428571 0.61904762 0.75 0.73913043] - -mean value: 0.6990089246086246 - -key: train_precision -value: [0.77647059 0.70046083 0.71014493 0.71563981 0.7038835 0.71495327 - 0.73891626 0.72300469 0.70935961 0.71495327] - -mean value: 0.7207786749797507 - -key: test_recall -value: [0.71428571 0.76190476 0.80952381 0.66666667 0.9047619 0.80952381 - 0.75 0.65 0.9 0.85 ] - -mean value: 0.7816666666666666 - -key: train_recall -value: [0.71351351 0.82162162 0.79459459 0.81621622 0.78378378 0.82702703 - 0.80645161 0.82795699 0.77419355 0.82258065] - -mean value: 0.7987939552455681 - -key: test_accuracy -value: [0.71428571 0.73809524 0.6097561 0.63414634 0.7804878 0.7804878 - 0.73170732 0.63414634 0.80487805 0.7804878 ] - -mean value: 0.7208478513356562 - -key: train_accuracy -value: [0.75405405 0.73513514 0.73584906 0.74663073 0.7277628 0.74932615 - 0.76010782 0.75471698 0.7277628 0.74663073] - -mean value: 0.7437976251183799 - -key: test_roc_auc -value: [0.71428571 0.73809524 0.6047619 0.63333333 0.77738095 0.7797619 - 0.73214286 0.63452381 0.80714286 0.78214286] - -mean value: 0.7203571428571429 - -key: train_roc_auc -value: [0.75405405 0.73513514 0.73600697 0.74681779 0.7279134 0.74953502 - 0.75998256 0.75451904 0.72763731 0.74642546] - -mean value: 0.7438026736413834 - -key: test_jcc -value: [0.55555556 0.59259259 0.51515152 0.48275862 0.67857143 0.65384615 - 0.57692308 0.46428571 0.69230769 0.65384615] - -mean value: 0.5865838503769538 - -key: train_jcc -value: [0.59192825 0.608 0.6 0.61632653 0.58943089 0.62195122 - 0.62761506 0.62857143 0.5877551 0.6194332 ] - -mean value: 0.6091011687308778 - -key: TN -value: 136 - -mean value: 136.0 - -key: FP -value: 45 - -mean value: 45.0 - -key: FN -value: 70 - -mean value: 70.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.38 - -Accuracy on Blind test: 0.72 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01096463 0.00961113 0.00975704 0.00960565 0.00958586 0.00966382 - 0.0096786 0.0097096 0.00990868 0.0098772 ] - -mean value: 0.009836220741271972 - -key: score_time -value: [0.00957084 0.0090189 0.00870156 0.00873661 0.00881624 0.00886679 - 0.00888467 0.00873137 0.00875568 0.00878763] - -mean value: 0.008887028694152832 - -key: test_mcc -value: [ 0. 0.30304576 0.41327851 0.29113032 0.45524446 0.63994524 - 0.62048368 -0.04029115 0.18976803 0.41428571] - -mean value: 0.32868905707398166 - -key: train_mcc -value: [0.46108397 0.40209243 0.41598338 0.41863013 0.42311032 0.36261286 - 0.43869287 0.425884 0.46299486 0.42319846] - -mean value: 0.4234283285039922 - -key: test_fscore -value: [0.43243243 0.57142857 0.60606061 0.57142857 0.64705882 0.77777778 - 0.70967742 0.32258065 0.4137931 0.7 ] - -mean value: 0.5752237950621776 - -key: train_fscore -value: [0.66666667 0.60338983 0.65420561 0.64984227 0.64308682 0.6038961 - 0.65822785 0.65408805 0.66878981 0.67069486] - -mean value: 0.647288786794278 - -key: test_precision -value: [0.5 0.71428571 0.83333333 0.71428571 0.84615385 0.93333333 - 1. 0.45454545 0.66666667 0.7 ] - -mean value: 0.7362604062604063 - -key: train_precision -value: [0.81889764 0.80909091 0.77205882 0.78030303 0.79365079 0.75609756 - 0.8 0.78787879 0.8203125 0.76551724] - -mean value: 0.7903807284603128 - -key: test_recall -value: [0.38095238 0.47619048 0.47619048 0.47619048 0.52380952 0.66666667 - 0.55 0.25 0.3 0.7 ] - -mean value: 0.48 - -key: train_recall -value: [0.56216216 0.48108108 0.56756757 0.55675676 0.54054054 0.5027027 - 0.55913978 0.55913978 0.56451613 0.59677419] - -mean value: 0.5490380703283929 - -key: test_accuracy -value: [0.5 0.64285714 0.68292683 0.63414634 0.70731707 0.80487805 - 0.7804878 0.48780488 0.58536585 0.70731707] - -mean value: 0.6533101045296166 - -key: train_accuracy -value: [0.71891892 0.68378378 0.70080863 0.70080863 0.70080863 0.67115903 - 0.70889488 0.70350404 0.71967655 0.70619946] - -mean value: 0.7014562540977635 - -key: test_roc_auc -value: [0.5 0.64285714 0.68809524 0.63809524 0.71190476 0.80833333 - 0.775 0.48214286 0.57857143 0.70714286] - -mean value: 0.6532142857142857 - -key: train_roc_auc -value: [0.71891892 0.68378378 0.70045045 0.70042139 0.7003778 0.67070619 - 0.70929962 0.70389422 0.7200959 0.7064952 ] - -mean value: 0.7014443475733797 - -key: test_jcc -value: [0.27586207 0.4 0.43478261 0.4 0.47826087 0.63636364 - 0.55 0.19230769 0.26086957 0.53846154] - -mean value: 0.41669079795766456 - -key: train_jcc -value: [0.5 0.43203883 0.48611111 0.48130841 0.47393365 0.43255814 - 0.49056604 0.48598131 0.50239234 0.50454545] - -mean value: 0.478943529129163 - -key: TN -value: 170 - -mean value: 170.0 - -key: FP -value: 107 - -mean value: 107.0 - -key: FN -value: 36 - -mean value: 36.0 - -key: TP -value: 99 - -mean value: 99.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.61 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00891542 0.00999284 0.00998735 0.00997591 0.00999928 0.0100472 - 0.00998139 0.0099628 0.00964546 0.00991631] - -mean value: 0.009842395782470703 - -key: score_time -value: [0.01492429 0.01553607 0.01342964 0.01545405 0.01206732 0.01232624 - 0.01210999 0.01499987 0.01185274 0.01187205] - -mean value: 0.013457226753234863 - -key: test_mcc -value: [0.43052839 0.38490018 0.17142857 0.37171226 0.6133669 0.56086079 - 0.51320273 0.07159925 0.57570364 0.37309549] - -mean value: 0.40663981862522663 - -key: train_mcc -value: [0.57917445 0.56736651 0.59587155 0.60158874 0.5713147 0.5850851 - 0.5912125 0.63077465 0.59830079 0.59830079] - -mean value: 0.59189897754378 - -key: test_fscore -value: [0.7 0.66666667 0.58536585 0.66666667 0.8 0.79069767 - 0.73684211 0.51282051 0.74285714 0.62857143] - -mean value: 0.6830488050922717 - -key: train_fscore -value: [0.7696793 0.76521739 0.79452055 0.79558011 0.77272727 0.7890411 - 0.79005525 0.8056338 0.78873239 0.78873239] - -mean value: 0.78599195588241 - -key: test_precision -value: [0.73684211 0.72222222 0.6 0.72222222 0.84210526 0.77272727 - 0.77777778 0.52631579 0.86666667 0.73333333] - -mean value: 0.7300212652844231 - -key: train_precision -value: [0.83544304 0.825 0.80555556 0.81355932 0.81437126 0.8 - 0.8125 0.84615385 0.82840237 0.82840237] - -mean value: 0.8209387752930825 - -key: test_recall -value: [0.66666667 0.61904762 0.57142857 0.61904762 0.76190476 0.80952381 - 0.7 0.5 0.65 0.55 ] - -mean value: 0.6447619047619048 - -key: train_recall -value: [0.71351351 0.71351351 0.78378378 0.77837838 0.73513514 0.77837838 - 0.7688172 0.7688172 0.75268817 0.75268817] - -mean value: 0.7545713455390876 - -key: test_accuracy -value: [0.71428571 0.69047619 0.58536585 0.68292683 0.80487805 0.7804878 - 0.75609756 0.53658537 0.7804878 0.68292683] - -mean value: 0.7014518002322879 - -key: train_accuracy -value: [0.78648649 0.78108108 0.79784367 0.80053908 0.78436658 0.79245283 - 0.79514825 0.81401617 0.79784367 0.79784367] - -mean value: 0.7947621475923363 - -key: test_roc_auc -value: [0.71428571 0.69047619 0.58571429 0.68452381 0.80595238 0.7797619 - 0.7547619 0.53571429 0.77738095 0.6797619 ] - -mean value: 0.7008333333333333 - -key: train_roc_auc -value: [0.78648649 0.78108108 0.79780587 0.80047951 0.78423423 0.792415 - 0.79521941 0.81413833 0.79796571 0.79796571] - -mean value: 0.7947791339726823 - -key: test_jcc -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -[0.53846154 0.5 0.4137931 0.5 0.66666667 0.65384615 - 0.58333333 0.34482759 0.59090909 0.45833333] - -mean value: 0.5250170806205288 - -key: train_jcc -value: [0.62559242 0.61971831 0.65909091 0.66055046 0.62962963 0.65158371 - 0.65296804 0.6745283 0.65116279 0.65116279] - -mean value: 0.6475987354575963 - -key: TN -value: 156 - -mean value: 156.0 - -key: FP -value: 73 - -mean value: 73.0 - -key: FN -value: 50 - -mean value: 50.0 - -key: TP -value: 133 - -mean value: 133.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.31 - -Accuracy on Blind test: 0.65 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0182035 0.01881266 0.01938367 0.01898909 0.01740909 0.0181179 - 0.01810026 0.01760936 0.0182271 0.01885557] - -mean value: 0.018370819091796876 - -key: score_time -value: [0.01155233 0.01083374 0.01057696 0.01137233 0.01090479 0.01087809 - 0.01130199 0.01125836 0.01051879 0.01087332] - -mean value: 0.011007070541381836 - -key: test_mcc -value: [0.62187434 0.71754731 0.60952381 0.66668392 0.8547619 0.65871309 - 0.7633652 0.51190476 0.70714286 0.65871309] - -mean value: 0.6770230281474807 - -key: train_mcc -value: [0.76946971 0.80658462 0.78453492 0.77396856 0.76871644 0.74663605 - 0.77898224 0.80112842 0.78512038 0.78553828] - -mean value: 0.780067963437501 - -key: test_fscore -value: [0.81818182 0.85 0.80952381 0.82051282 0.92682927 0.8372093 - 0.86486486 0.75 0.85 0.82051282] - -mean value: 0.8347634704214398 - -key: train_fscore -value: [0.87955182 0.9 0.89071038 0.88461538 0.8815427 0.87262873 - 0.89008043 0.89863014 0.89010989 0.88950276] - -mean value: 0.8877372232350673 - -key: test_precision -value: [0.7826087 0.89473684 0.80952381 0.88888889 0.95 0.81818182 - 0.94117647 0.75 0.85 0.84210526] - -mean value: 0.8527221788098082 - -key: train_precision -value: [0.9127907 0.92571429 0.90055249 0.89944134 0.8988764 0.875 - 0.88770053 0.91620112 0.91011236 0.91477273] - -mean value: 0.9041161953754138 - -key: test_recall -value: [0.85714286 0.80952381 0.80952381 0.76190476 0.9047619 0.85714286 - 0.8 0.75 0.85 0.8 ] - -mean value: 0.82 - -key: train_recall -value: [0.84864865 0.87567568 0.88108108 0.87027027 0.86486486 0.87027027 - 0.89247312 0.88172043 0.87096774 0.8655914 ] - -mean value: 0.8721563498982853 - -key: test_accuracy -value: [0.80952381 0.85714286 0.80487805 0.82926829 0.92682927 0.82926829 - 0.87804878 0.75609756 0.85365854 0.82926829] - -mean value: 0.83739837398374 - -key: train_accuracy -value: [0.88378378 0.9027027 0.89218329 0.88679245 0.88409704 0.87331536 - 0.88948787 0.90026954 0.89218329 0.89218329] - -mean value: 0.8896998615866542 - -key: test_roc_auc -value: [0.80952381 0.85714286 0.8047619 0.83095238 0.92738095 0.82857143 - 0.87619048 0.75595238 0.85357143 0.82857143] - -mean value: 0.8372619047619047 - -key: train_roc_auc -value: [0.88378378 0.9027027 0.89215344 0.88674804 0.88404534 0.87330718 - 0.8894798 0.90031967 0.89224063 0.89225516] - -mean value: 0.8897035745422844 - -key: test_jcc -value: [0.69230769 0.73913043 0.68 0.69565217 0.86363636 0.72 - 0.76190476 0.6 0.73913043 0.69565217] - -mean value: 0.7187414035240123 - -key: train_jcc -value: [0.785 0.81818182 0.80295567 0.79310345 0.78817734 0.77403846 - 0.80193237 0.8159204 0.8019802 0.80099502] - -mean value: 0.7982284720977383 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 37 - -mean value: 37.0 - -key: FN -value: 30 - -mean value: 30.0 - -key: TP -value: 169 - -mean value: 169.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.81 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.38255119 1.43847704 1.32005191 1.51834154 1.42000461 1.32521057 - 1.5029707 1.40174365 1.32761645 1.41051412] - -mean value: 1.4047481775283814 - -key: score_time -value: [0.01294279 0.01267171 0.01273942 0.01282597 0.01268029 0.01274633 - 0.01404238 0.01446939 0.0127387 0.01268911] - -mean value: 0.013054609298706055 - -key: test_mcc -value: [0.90889326 0.9047619 0.71121921 0.86333169 1. 0.90692382 - 0.86240942 0.65952381 0.95238095 0.8547619 ] - -mean value: 0.8624205974031269 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95454545 0.95238095 0.85 0.92307692 1. 0.95 - 0.91891892 0.82926829 0.97560976 0.92682927] - -mean value: 0.9280629565995419 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.91304348 0.95238095 0.89473684 1. 1. 1. - 1. 0.80952381 0.95238095 0.9047619 ] - -mean value: 0.9426827939413751 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95238095 0.80952381 0.85714286 1. 0.9047619 - 0.85 0.85 1. 0.95 ] - -mean value: 0.9173809523809522 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 0.95238095 0.85365854 0.92682927 1. 0.95121951 - 0.92682927 0.82926829 0.97560976 0.92682927] - -mean value: 0.929500580720093 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 0.95238095 0.8547619 0.92857143 1. 0.95238095 - 0.925 0.8297619 0.97619048 0.92738095] - -mean value: 0.9298809523809524 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.91304348 0.90909091 0.73913043 0.85714286 1. 0.9047619 - 0.85 0.70833333 0.95238095 0.86363636] - -mean value: 0.8697520233389799 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 194 - -mean value: 194.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 189 - -mean value: 189.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.89 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02127624 0.02067518 0.01595592 0.01767468 0.01548076 0.01587391 - 0.01521254 0.0156157 0.01792765 0.01475978] - -mean value: 0.017045235633850096 - -key: score_time -value: [0.01265335 0.00976419 0.00935817 0.0089314 0.00892782 0.00888157 - 0.00895119 0.00915241 0.00912976 0.00949645] - -mean value: 0.00952463150024414 - -key: test_mcc -value: [0.90889326 0.90889326 1. 0.8047619 1. 0.90692382 - 1. 0.86333169 0.95227002 0.95238095] - -mean value: 0.929745490920746 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95 0.95 1. 0.9047619 1. 0.95 - 1. 0.93023256 0.97435897 0.97560976] - -mean value: 0.9634963193357976 - -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. 0.9047619 1. 1. - 1. 0.86956522 1. 0.95238095] - -mean value: 0.9726708074534163 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.9047619 0.9047619 1. 0.9047619 1. 0.9047619 1. - 1. 0.95 1. ] - -mean value: 0.9569047619047618 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 0.95238095 1. 0.90243902 1. 0.95121951 - 1. 0.92682927 0.97560976 0.97560976] - -mean value: 0.9636469221835074 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 0.95238095 1. 0.90238095 1. 0.95238095 - 1. 0.92857143 0.975 0.97619048] - -mean value: 0.9639285714285715 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.9047619 0.9047619 1. 0.82608696 1. 0.9047619 - 1. 0.86956522 0.95 0.95238095] - -mean value: 0.931231884057971 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 200 - -mean value: 200.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.10961461 0.10851312 0.109653 0.11035824 0.11034012 0.11315393 - 0.10982227 0.10867882 0.10954762 0.10860085] - -mean value: 0.10982825756072997 - -key: score_time -value: [0.01763701 0.01784396 0.0181098 0.01774406 0.01787066 0.0188036 - 0.01838684 0.01768804 0.01775908 0.01774549] - -mean value: 0.017958855628967284 - -key: test_mcc -value: [0.71754731 0.81322028 0.75714286 0.8547619 0.90692382 0.90238095 - 0.80817439 0.56190476 0.8547619 0.70714286] - -mean value: 0.7883961048324287 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.86363636 0.90909091 0.87804878 0.92682927 0.95 0.95238095 - 0.89473684 0.7804878 0.92682927 0.85 ] - -mean value: 0.8932040189164707 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.82608696 0.86956522 0.9 0.95 1. 0.95238095 - 0.94444444 0.76190476 0.9047619 0.85 ] - -mean value: 0.8959144237405108 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.9047619 0.95238095 0.85714286 0.9047619 0.9047619 0.95238095 - 0.85 0.8 0.95 0.85 ] - -mean value: 0.8926190476190475 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.85714286 0.9047619 0.87804878 0.92682927 0.95121951 0.95121951 - 0.90243902 0.7804878 0.92682927 0.85365854] - -mean value: 0.8932636469221835 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.85714286 0.9047619 0.87857143 0.92738095 0.95238095 0.95119048 - 0.90119048 0.78095238 0.92738095 0.85357143] - -mean value: 0.893452380952381 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.76 0.83333333 0.7826087 0.86363636 0.9047619 0.90909091 - 0.80952381 0.64 0.86363636 0.73913043] - -mean value: 0.8105721814417466 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 184 - -mean value: 184.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 184 - -mean value: 184.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.54 - -Accuracy on Blind test: 0.8 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01006579 0.00969267 0.00944519 0.00950289 0.00946856 0.0098021 - 0.0096879 0.00964212 0.00958872 0.00959158] - -mean value: 0.009648752212524415 - -key: score_time -value: [0.00878835 0.00860691 0.00863194 0.00864887 0.00862575 0.00864124 - 0.00870109 0.00867701 0.00868344 0.00871062] - -mean value: 0.00867152214050293 - -key: test_mcc -value: [0.33954988 0.43656413 0.75714286 0.41766229 0.76500781 0.8047619 - 0.77831178 0.31960727 0.7098505 0.75714286] - -mean value: 0.6085601274524202 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.69565217 0.68421053 0.87804878 0.7 0.87179487 0.9047619 - 0.85714286 0.61111111 0.84210526 0.87804878] - -mean value: 0.7922876269173081 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.64 0.76470588 0.9 0.73684211 0.94444444 0.9047619 - 1. 0.6875 0.88888889 0.85714286] - -mean value: 0.8324286082854195 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.76190476 0.61904762 0.85714286 0.66666667 0.80952381 0.9047619 - 0.75 0.55 0.8 0.9 ] - -mean value: 0.761904761904762 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.66666667 0.71428571 0.87804878 0.70731707 0.87804878 0.90243902 - 0.87804878 0.65853659 0.85365854 0.87804878] - -mean value: 0.8015098722415797 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.66666667 0.71428571 0.87857143 0.70833333 0.8797619 0.90238095 - 0.875 0.65595238 0.85238095 0.87857143] - -mean value: 0.8011904761904761 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.53333333 0.52 0.7826087 0.53846154 0.77272727 0.82608696 - 0.75 0.44 0.72727273 0.7826087 ] - -mean value: 0.6673099219620959 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 173 - -mean value: 173.0 - -key: FP -value: 49 - -mean value: 49.0 - -key: FN -value: 33 - -mean value: 33.0 - -key: TP -value: 157 - -mean value: 157.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.41 - -Accuracy on Blind test: 0.73 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.51198244 1.54020786 1.51207709 1.50037861 1.5325613 1.51260614 - 1.51920581 1.49548864 1.51993775 1.50940585] - -mean value: 1.5153851509094238 - -key: score_time -value: [0.10183477 0.09141374 0.0912416 0.09255338 0.09245515 0.09194899 - 0.09139752 0.09136343 0.09181547 0.09130526] - -mean value: 0.09273293018341064 - -key: test_mcc -value: [0.90889326 0.85811633 0.85441771 0.85441771 0.95238095 0.90692382 - 0.95227002 0.76500781 0.95238095 0.90692382] - -mean value: 0.8911732380263115 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95454545 0.93023256 0.93023256 0.93023256 0.97560976 0.95 - 0.97435897 0.88372093 0.97560976 0.95238095] - -mean value: 0.9456923498131665 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.91304348 0.90909091 0.90909091 0.90909091 1. 1. - 1. 0.82608696 0.95238095 0.90909091] - -mean value: 0.9327875023527197 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619 - 0.95 0.95 1. 1. ] - -mean value: 0.9614285714285714 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 0.92857143 0.92682927 0.92682927 0.97560976 0.95121951 - 0.97560976 0.87804878 0.97560976 0.95121951] - -mean value: 0.9441927990708479 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 0.92857143 0.92619048 0.92619048 0.97619048 0.95238095 - 0.975 0.8797619 0.97619048 0.95238095] - -mean value: 0.9445238095238094 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.91304348 0.86956522 0.86956522 0.86956522 0.95238095 0.9047619 - 0.95 0.79166667 0.95238095 0.90909091] - -mean value: 0.8982020515716167 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 191 - -mean value: 191.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 198 - -mean value: 198.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.94 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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( -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.92990637 0.95555329 0.89409733 0.94846439 0.97272491 0.92180276 - 0.9189589 0.93088937 0.8951056 1.03274155] - -mean value: 0.940024447441101 - -key: score_time -value: [0.20952916 0.21944141 0.19545674 0.20378399 0.12587929 0.18719935 - 0.19421124 0.20344734 0.24252033 0.22045898] - -mean value: 0.20019278526306153 - -key: test_mcc -value: [0.81322028 0.81322028 0.7565654 0.85441771 0.95238095 0.90692382 - 1. 0.65952381 0.90692382 0.86333169] - -mean value: 0.852650778186983 - -key: train_mcc -value: [0.989247 0.98391316 0.97866529 0.98384191 0.98395676 0.98927606 - 0.98395537 0.98395537 0.97866283 0.98395537] - -mean value: 0.9839429109753077 - -key: test_fscore -value: [0.90909091 0.90909091 0.88372093 0.93023256 0.97560976 0.95 - 1. 0.82926829 0.95238095 0.93023256] - -mean value: 0.9269626865854885 - -key: train_fscore -value: [0.99462366 0.9919571 0.98930481 0.99191375 0.9919571 0.99462366 - 0.992 0.992 0.9893617 0.992 ] - -mean value: 0.9919741782535849 - -key: test_precision -value: [0.86956522 0.86956522 0.86363636 0.90909091 1. 1. - 1. 0.80952381 0.90909091 0.86956522] - -mean value: 0.9100037643515904 - -key: train_precision -value: [0.98930481 0.98404255 0.97883598 0.98924731 0.98404255 0.98930481 - 0.98412698 0.98412698 0.97894737 0.98412698] - -mean value: 0.984610634351737 - -key: test_recall -value: [0.95238095 0.95238095 0.9047619 0.95238095 0.95238095 0.9047619 - 1. 0.85 1. 1. ] - -mean value: 0.9469047619047618 - -key: train_recall -value: [1. 1. 1. 0.99459459 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994594594594595 - -key: test_accuracy -value: [0.9047619 0.9047619 0.87804878 0.92682927 0.97560976 0.95121951 - 1. 0.82926829 0.95121951 0.92682927] - -mean value: 0.9248548199767713 - -key: train_accuracy -value: [0.99459459 0.99189189 0.98921833 0.99191375 0.99191375 0.99460916 - 0.99191375 0.99191375 0.98921833 0.99191375] - -mean value: 0.9919101041742551 - -key: test_roc_auc -value: [0.9047619 0.9047619 0.87738095 0.92619048 0.97619048 0.95238095 - 1. 0.8297619 0.95238095 0.92857143] - -mean value: 0.9252380952380952 - -key: train_roc_auc -value: [0.99459459 0.99189189 0.98924731 0.99192095 0.99193548 0.99462366 - 0.99189189 0.99189189 0.98918919 0.99189189] - -mean value: 0.991907875617553 - -key: test_jcc -value: [0.83333333 0.83333333 0.79166667 0.86956522 0.95238095 0.9047619 - 1. 0.70833333 0.90909091 0.86956522] - -mean value: 0.8672030867683042 - -key: train_jcc -value: [0.98930481 0.98404255 0.97883598 0.98395722 0.98404255 0.98930481 - 0.98412698 0.98412698 0.97894737 0.98412698] - -mean value: 0.9840816250940749 - -key: TN -value: 186 - -mean value: 186.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 195 - -mean value: 195.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.8 - -Accuracy on Blind test: 0.91 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.07739592 0.05289793 0.05677772 0.0575881 0.06224346 0.06622791 - 0.06729889 0.06369901 0.06968975 0.06554985] - -mean value: 0.06393685340881347 - -key: score_time -value: [0.01081038 0.0105238 0.01094198 0.01074648 0.01077628 0.01114082 - 0.0110867 0.01144195 0.01085472 0.01093197] - -mean value: 0.010925507545471192 - -key: test_mcc -value: [1. 1. 1. 1. 1. 0.95238095 - 0.90649828 0.86333169 0.95227002 0.95238095] - -mean value: 0.9626861893882752 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 1. 1. 0.97560976 - 0.94736842 0.93023256 0.97435897 0.97560976] - -mean value: 0.9803179465746261 - -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. 1. 1. 1. - 1. 0.86956522 1. 0.95238095] - -mean value: 0.9821946169772258 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 0.95238095 - 0.9 1. 0.95 1. ] - -mean value: 0.9802380952380952 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 1. 1. 0.97560976 - 0.95121951 0.92682927 0.97560976 0.97560976] - -mean value: 0.9804878048780488 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 1. 1. 0.97619048 - 0.95 0.92857143 0.975 0.97619048] - -mean value: 0.9805952380952381 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 1. 1. 0.95238095 - 0.9 0.86956522 0.95 0.95238095] - -mean value: 0.9624327122153209 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 202 - -mean value: 202.0 - -key: FP -value: 4 - -mean value: 4.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 202 - -mean value: 202.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02928782 0.04429269 0.09572411 0.04821539 0.03643346 0.03635001 - 0.09062386 0.06526732 0.07165194 0.07450461] - -mean value: 0.059235119819641115 - -key: score_time -value: [0.01215029 0.02206373 0.03581309 0.0122366 0.01228285 0.01228428 - 0.02322578 0.02300858 0.02337956 0.02231598] - -mean value: 0.01987607479095459 - -key: test_mcc -value: [1. 0.85811633 0.71121921 0.75714286 0.8547619 0.8213423 - 0.86240942 0.70714286 0.90238095 0.7197263 ] - -mean value: 0.819424213092508 - -key: train_mcc -value: [0.98379816 0.98379816 0.978494 0.97317407 0.98384144 0.98384191 - 0.98384144 0.98384191 0.9784365 0.98384191] - -mean value: 0.9816909473768508 - -key: test_fscore -value: [1. 0.92682927 0.85 0.87804878 0.92682927 0.89473684 - 0.91891892 0.85 0.95 0.83333333] - -mean value: 0.9028696411430687 - -key: train_fscore -value: [0.99191375 0.99191375 0.98924731 0.98659517 0.99186992 0.99191375 - 0.9919571 0.99191375 0.98924731 0.99191375] - -mean value: 0.9908485554329116 - -key: test_precision -value: [1. 0.95 0.89473684 0.9 0.95 1. - 1. 0.85 0.95 0.9375 ] - -mean value: 0.9432236842105264 - -key: train_precision -value: [0.98924731 0.98924731 0.98395722 0.9787234 0.99456522 0.98924731 - 0.98930481 0.99459459 0.98924731 0.99459459] - -mean value: 0.9892729090233203 - -key: test_recall -value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.80952381 - 0.85 0.85 0.95 0.75 ] - -mean value: 0.8685714285714287 - -key: train_recall -value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98918919 0.99459459 - 0.99462366 0.98924731 0.98924731 0.98924731] - -mean value: 0.9924527753560011 - -key: test_accuracy -value: [1. 0.92857143 0.85365854 0.87804878 0.92682927 0.90243902 - 0.92682927 0.85365854 0.95121951 0.85365854] - -mean value: 0.9074912891986063 - -key: train_accuracy -value: [0.99189189 0.99189189 0.98921833 0.98652291 0.99191375 0.99191375 - 0.99191375 0.99191375 0.98921833 0.99191375] - -mean value: 0.9908312085670575 - -key: test_roc_auc -value: [1. 0.92857143 0.8547619 0.87857143 0.92738095 0.9047619 - 0.925 0.85357143 0.95119048 0.85119048] - -mean value: 0.9075 - -key: train_roc_auc -value: [0.99189189 0.99189189 0.98923278 0.98654461 0.99190642 0.99192095 - 0.99190642 0.99192095 0.98921825 0.99192095] - -mean value: 0.9908355129322871 - -key: test_jcc -value: [1. 0.86363636 0.73913043 0.7826087 0.86363636 0.80952381 - 0.85 0.73913043 0.9047619 0.71428571] - -mean value: 0.8266713721061547 - -key: train_jcc -value: [0.98395722 0.98395722 0.9787234 0.97354497 0.98387097 0.98395722 - 0.98404255 0.98395722 0.9787234 0.98395722] - -mean value: 0.9818691399245723 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 179 - -mean value: 179.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.86 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.00992703 0.00981212 0.009444 0.00925446 0.00927901 0.00920343 - 0.00935507 0.00949359 0.00937295 0.00925636] - -mean value: 0.009439802169799805 - -key: score_time -value: [0.01170301 0.00905418 0.00861382 0.00861716 0.00858498 0.00860953 - 0.00862718 0.00857091 0.00866532 0.00863743] - -mean value: 0.008968353271484375 - -key: test_mcc -value: [0.23918244 0.47673129 0.06905393 0.22195767 0.46300848 0.56190476 - 0.41487884 0.16945156 0.56190476 0.46428571] - -mean value: 0.3642359453605911 - -key: train_mcc -value: [0.36235278 0.38387352 0.43399024 0.41274834 0.38004592 0.41782673 - 0.39622203 0.4018383 0.41258723 0.44486121] - -mean value: 0.40463463119473336 - -key: test_fscore -value: [0.63636364 0.73170732 0.6122449 0.6 0.74418605 0.7804878 - 0.68421053 0.56410256 0.7804878 0.73170732] - -mean value: 0.686549791515524 - -key: train_fscore -value: [0.68617021 0.69518717 0.71389646 0.71087533 0.68834688 0.70967742 - 0.69892473 0.70712401 0.70299728 0.72679045] - -mean value: 0.7039989938565314 - -key: test_precision -value: [0.60869565 0.75 0.53571429 0.63157895 0.72727273 0.8 - 0.72222222 0.57894737 0.76190476 0.71428571] - -mean value: 0.6830621679363098 - -key: train_precision -value: [0.67539267 0.68783069 0.71978022 0.69791667 0.69021739 0.70588235 - 0.69892473 0.69430052 0.71270718 0.71727749] - -mean value: 0.7000229907229114 - -key: test_recall -value: [0.66666667 0.71428571 0.71428571 0.57142857 0.76190476 0.76190476 - 0.65 0.55 0.8 0.75 ] - -mean value: 0.694047619047619 - -key: train_recall -value: [0.6972973 0.7027027 0.70810811 0.72432432 0.68648649 0.71351351 - 0.69892473 0.72043011 0.69354839 0.73655914] - -mean value: 0.708189479802383 - -key: test_accuracy -value: [0.61904762 0.73809524 0.53658537 0.6097561 0.73170732 0.7804878 - 0.70731707 0.58536585 0.7804878 0.73170732] - -mean value: 0.6820557491289199 - -key: train_accuracy -value: [0.68108108 0.69189189 0.71698113 0.70619946 0.69002695 0.70889488 - 0.69811321 0.70080863 0.70619946 0.72237197] - -mean value: 0.7022568660304509 - -key: test_roc_auc -value: [0.61904762 0.73809524 0.53214286 0.61071429 0.73095238 0.78095238 - 0.70595238 0.58452381 0.78095238 0.73214286] - -mean value: 0.681547619047619 - -key: train_roc_auc -value: [0.68108108 0.69189189 0.71695728 0.70624818 0.69001744 0.70890729 - 0.69811101 0.70075559 0.70623365 0.72233362] - -mean value: 0.7022537053182214 - -key: test_jcc -value: [0.46666667 0.57692308 0.44117647 0.42857143 0.59259259 0.64 - 0.52 0.39285714 0.64 0.57692308] - -mean value: 0.5275710455122219 - -key: train_jcc -value: [0.52226721 0.53278689 0.55508475 0.55144033 0.52479339 0.55 - 0.53719008 0.54693878 0.54201681 0.57083333] - -mean value: 0.543335155334506 - -key: TN -value: 138 - -mean value: 138.0 - -key: FP -value: 63 - -mean value: 63.0 - -key: FN -value: 68 - -mean value: 68.0 - -key: TP -value: 143 - -mean value: 143.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.68 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.02252197 0.01936436 0.02320266 0.01887608 0.02798271 0.02770257 - 0.02312875 0.02318501 0.0263114 0.02959681] - -mean value: 0.024187231063842775 - -key: score_time -value: [0.00863934 0.01137352 0.01186848 0.01171231 0.01185107 0.01182008 - 0.01177526 0.01178193 0.0118196 0.0117321 ] - -mean value: 0.011437368392944337 - -key: test_mcc -value: [0.95346259 1. 0.90692382 0.86333169 1. 0.95238095 - 0.95227002 0.76500781 0.90238095 0.95238095] - -mean value: 0.9248138787322764 - -key: train_mcc -value: [0.98379816 0.98379816 0.98927606 0.9361732 0.98927606 0.98384191 - 0.98384144 0.97866283 0.98384144 0.9946235 ] - -mean value: 0.9807132751752643 - -key: test_fscore -value: [0.97674419 1. 0.95 0.92307692 1. 0.97560976 - 0.97435897 0.88372093 0.95 0.97560976] - -mean value: 0.960912052591009 - -key: train_fscore -value: [0.99191375 0.99191375 0.99462366 0.96685083 0.99462366 0.99191375 - 0.9919571 0.9893617 0.9919571 0.99731903] - -mean value: 0.990243432654491 - -key: test_precision -value: [0.95454545 1. 1. 1. 1. 1. - 1. 0.82608696 0.95 0.95238095] - -mean value: 0.9683013363448145 - -key: train_precision -value: [0.98924731 0.98924731 0.98930481 0.98870056 0.98930481 0.98924731 - 0.98930481 0.97894737 0.98930481 0.99465241] - -mean value: 0.9887261526630686 - -key: test_recall -value: [1. 1. 0.9047619 0.85714286 1. 0.95238095 - 0.95 0.95 0.95 1. ] - -mean value: 0.9564285714285713 - -key: train_recall -value: [0.99459459 0.99459459 1. 0.94594595 1. 0.99459459 - 0.99462366 1. 0.99462366 1. ] - -mean value: 0.9918977041557687 - -key: test_accuracy -value: [0.97619048 1. 0.95121951 0.92682927 1. 0.97560976 - 0.97560976 0.87804878 0.95121951 0.97560976] - -mean value: 0.9610336817653892 - -key: train_accuracy -value: [0.99189189 0.99189189 0.99460916 0.96765499 0.99460916 0.99191375 - 0.99191375 0.98921833 0.99191375 0.99730458] - -mean value: 0.9902921250091061 - -key: test_roc_auc -value: [0.97619048 1. 0.95238095 0.92857143 1. 0.97619048 - 0.975 0.8797619 0.95119048 0.97619048] - -mean value: 0.9615476190476191 - -key: train_roc_auc -value: [0.99189189 0.99189189 0.99462366 0.96759663 0.99462366 0.99192095 - 0.99190642 0.98918919 0.99190642 0.9972973 ] - -mean value: 0.9902848009299621 - -key: test_jcc -value: [0.95454545 1. 0.9047619 0.85714286 1. 0.95238095 - 0.95 0.79166667 0.9047619 0.95238095] - -mean value: 0.9267640692640693 - -key: train_jcc -value: [0.98395722 0.98395722 0.98930481 0.93582888 0.98930481 0.98395722 - 0.98404255 0.97894737 0.98404255 0.99465241] - -mean value: 0.9807995041648951 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01611996 0.01744223 0.01719046 0.01823139 0.01707792 0.01627302 - 0.01734209 0.01785684 0.01796865 0.0183959 ] - -mean value: 0.017389845848083497 - -key: score_time -value: [0.01184702 0.0118773 0.01190281 0.01194453 0.01188087 0.01207185 - 0.01205659 0.01236892 0.01192665 0.01186895] - -mean value: 0.011974549293518067 - -key: test_mcc -value: [0.72760688 0.95346259 0.90692382 0.95227002 1. 0.90692382 - 0.66668392 0.75714286 0.77831178 0.80817439] - -mean value: 0.8457500078758784 - -key: train_mcc -value: [0.69419957 0.97310093 0.97305937 0.9734012 0.97317407 0.95709306 - 0.90722239 0.98384191 0.73247456 0.94236768] - -mean value: 0.9109934736638234 - -key: test_fscore -value: [0.84210526 0.97674419 0.95 0.97674419 1. 0.95 - 0.8372093 0.87804878 0.85714286 0.89473684] - -mean value: 0.9162731417312425 - -key: train_fscore -value: [0.79220779 0.98637602 0.98644986 0.98666667 0.98659517 0.97814208 - 0.95384615 0.99191375 0.82278481 0.96952909] - -mean value: 0.9454511392412976 - -key: test_precision -value: [0.94117647 0.95454545 1. 0.95454545 1. 1. - 0.7826087 0.85714286 1. 0.94444444] - -mean value: 0.943446337691862 - -key: train_precision -value: [0.99186992 0.99450549 0.98913043 0.97368421 0.9787234 0.98895028 - 0.91176471 0.99459459 1. 1. ] - -mean value: 0.9823223039488965 - -key: test_recall -value: [0.76190476 1. 0.9047619 1. 1. 0.9047619 - 0.9 0.9 0.75 0.85 ] - -mean value: 0.8971428571428571 - -key: train_recall -value: [0.65945946 0.97837838 0.98378378 1. 0.99459459 0.96756757 - 1. 0.98924731 0.69892473 0.94086022] - -mean value: 0.9212816041848301 - -key: test_accuracy -value: [0.85714286 0.97619048 0.95121951 0.97560976 1. 0.95121951 - 0.82926829 0.87804878 0.87804878 0.90243902] - -mean value: 0.9199186991869919 - -key: train_accuracy -value: [0.82702703 0.98648649 0.98652291 0.98652291 0.98652291 0.97843666 - 0.95148248 0.99191375 0.8490566 0.9703504 ] - -mean value: 0.9514322138850441 - -key: test_roc_auc -value: [0.85714286 0.97619048 0.95238095 0.975 1. 0.95238095 - 0.83095238 0.87857143 0.875 0.90119048] - -mean value: 0.9198809523809525 - -key: train_roc_auc -value: [0.82702703 0.98648649 0.98651555 0.98655914 0.98654461 0.97840744 - 0.95135135 0.99192095 0.84946237 0.97043011] - -mean value: 0.9514705027608255 - -key: test_jcc -value: [0.72727273 0.95454545 0.9047619 0.95454545 1. 0.9047619 - 0.72 0.7826087 0.75 0.80952381] - -mean value: 0.850801995106343 - -key: train_jcc -value: [0.65591398 0.97311828 0.97326203 0.97368421 0.97354497 0.95721925 - 0.91176471 0.98395722 0.69892473 0.94086022] - -mean value: 0.9042249596928515 - -key: TN -value: 194 - -mean value: 194.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 185 - -mean value: 185.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.45 - -Accuracy on Blind test: 0.64 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.16417074 0.14623141 0.14611936 0.14649653 0.14675474 0.15263605 - 0.15256119 0.1455555 0.14614868 0.14716053] - -mean value: 0.14938347339630126 - -key: score_time -value: [0.01506853 0.01520467 0.01521516 0.01508999 0.01510882 0.0154891 - 0.01506519 0.01622915 0.01549888 0.0156498 ] - -mean value: 0.015361928939819336 - -key: test_mcc -value: [0.9047619 1. 1. 0.95227002 1. 0.95238095 - 0.95227002 0.86333169 1. 0.95238095] - -mean value: 0.9577395534494869 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.95238095 1. 1. 0.97674419 1. 0.97560976 - 0.97435897 0.93023256 1. 0.97560976] - -mean value: 0.9784936183121097 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95238095 1. 1. 0.95454545 1. 1. - 1. 0.86956522 1. 0.95238095] - -mean value: 0.9728872576698663 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 1. 1. 1. 1. 0.95238095 - 0.95 1. 1. 1. ] - -mean value: 0.9854761904761904 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.95238095 1. 1. 0.97560976 1. 0.97560976 - 0.97560976 0.92682927 1. 0.97560976] - -mean value: 0.9781649245063878 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.95238095 1. 1. 0.975 1. 0.97619048 - 0.975 0.92857143 1. 0.97619048] - -mean value: 0.9783333333333333 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.90909091 1. 1. 0.95454545 1. 0.95238095 - 0.95 0.86956522 1. 0.95238095] - -mean value: 0.9587963485789572 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 200 - -mean value: 200.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 203 - -mean value: 203.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.93 - -Accuracy on Blind test: 0.97 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03610611 0.04993343 0.05819893 0.05507755 0.06369472 0.05800819 - 0.05741405 0.04725194 0.0697515 0.05640531] - -mean value: 0.05518417358398438 - -key: score_time -value: [0.02264643 0.02638483 0.02947092 0.03190923 0.03001213 0.03039432 - 0.02232504 0.0379746 0.03620601 0.01787853] - -mean value: 0.02852020263671875 - -key: test_mcc -value: [0.90889326 0.90889326 0.90238095 0.95227002 0.95238095 0.95238095 - 0.90649828 0.8213423 0.95227002 0.95238095] - -mean value: 0.9209690938025448 - -key: train_mcc -value: [1. 1. 0.98927544 0.9946235 0.98927544 0.98927544 - 0.9946235 0.98921825 0.98384191 0.98921825] - -mean value: 0.9919351733305355 - -key: test_fscore -value: [0.95 0.95 0.95238095 0.97674419 0.97560976 0.97560976 - 0.94736842 0.90909091 0.97435897 0.97560976] - -mean value: 0.9586772711222661 - -key: train_fscore -value: [1. 1. 0.99456522 0.99728997 0.99456522 0.99456522 - 0.99731903 0.99462366 0.99191375 0.99462366] - -mean value: 0.9959465718384873 - -key: test_precision -value: [1. 1. 0.95238095 0.95454545 1. 1. - 1. 0.83333333 1. 0.95238095] - -mean value: 0.9692640692640693 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.99465241 0.99462366 0.99459459 0.99462366] - -mean value: 0.9978494312839663 - -key: test_recall -value: [0.9047619 0.9047619 0.95238095 1. 0.95238095 0.95238095 - 0.9 1. 0.95 1. ] - -mean value: 0.9516666666666665 - -key: train_recall -value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919 - 1. 0.99462366 0.98924731 0.99462366] - -mean value: 0.9940656785818076 - -key: test_accuracy -value: [0.95238095 0.95238095 0.95121951 0.97560976 0.97560976 0.97560976 - 0.95121951 0.90243902 0.97560976 0.97560976] - -mean value: 0.9587688734030199 - -key: train_accuracy -value: [1. 1. 0.99460916 0.99730458 0.99460916 0.99460916 - 0.99730458 0.99460916 0.99191375 0.99460916] - -mean value: 0.9959568733153639 - -key: test_roc_auc -value: [0.95238095 0.95238095 0.95119048 0.975 0.97619048 0.97619048 - 0.95 0.9047619 0.975 0.97619048] - -mean value: 0.9589285714285714 - -key: train_roc_auc -value: [1. 1. 0.99459459 0.9972973 0.99459459 0.99459459 - 0.9972973 0.99460913 0.99192095 0.99460913] - -mean value: 0.995951758209823 - -key: test_jcc -value: [0.9047619 0.9047619 0.90909091 0.95454545 0.95238095 0.95238095 - 0.9 0.83333333 0.95 0.95238095] - -mean value: 0.9213636363636363 - -key: train_jcc -value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919 - 0.99465241 0.98930481 0.98395722 0.98930481] - -mean value: 0.991938141349906 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 196 - -mean value: 196.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.91 - -Accuracy on Blind test: 0.96 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0951314 0.0657649 0.08133554 0.10624385 0.10583973 0.05929923 - 0.07229829 0.14089751 0.10813332 0.08627105] - -mean value: 0.09212148189544678 - -key: score_time -value: [0.0226891 0.01400733 0.02260351 0.02237463 0.02234697 0.01414204 - 0.01397967 0.02263641 0.0223701 0.01710892] - -mean value: 0.01942586898803711 - -key: test_mcc -value: [0.62187434 0.61904762 0.6133669 0.48063079 0.80907152 0.76500781 - 0.57570364 0.46300848 0.7098505 0.65871309] - -mean value: 0.6316274678175477 - -key: train_mcc -value: [0.92567765 0.91485102 0.92538015 0.89861855 0.91506448 0.91458711 - 0.92501826 0.93618785 0.92588469 0.92539732] - -mean value: 0.9206667079567575 - -key: test_fscore -value: [0.8 0.80952381 0.8 0.7027027 0.9 0.87179487 - 0.74285714 0.71794872 0.84210526 0.82051282] - -mean value: 0.800744532849796 - -key: train_fscore -value: [0.96111111 0.95555556 0.96132597 0.94736842 0.95555556 0.9558011 - 0.96174863 0.96703297 0.96132597 0.96153846] - -mean value: 0.9588363744400097 - -key: test_precision -value: [0.84210526 0.80952381 0.84210526 0.8125 0.94736842 0.94444444 - 0.86666667 0.73684211 0.88888889 0.84210526] - -mean value: 0.8532550125313282 - -key: train_precision -value: [0.98857143 0.98285714 0.98305085 0.97159091 0.98285714 0.97740113 - 0.97777778 0.98876404 0.98863636 0.98314607] - -mean value: 0.9824652854551446 - -key: test_recall -value: [0.76190476 0.80952381 0.76190476 0.61904762 0.85714286 0.80952381 - 0.65 0.7 0.8 0.8 ] - -mean value: 0.7569047619047619 - -key: train_recall -value: [0.93513514 0.92972973 0.94054054 0.92432432 0.92972973 0.93513514 - 0.94623656 0.94623656 0.93548387 0.94086022] - -mean value: 0.9363411798895671 - -key: test_accuracy -value: [0.80952381 0.80952381 0.80487805 0.73170732 0.90243902 0.87804878 - 0.7804878 0.73170732 0.85365854 0.82926829] - -mean value: 0.813124274099884 - -key: train_accuracy -value: [0.96216216 0.95675676 0.96226415 0.94878706 0.95687332 0.95687332 - 0.96226415 0.96765499 0.96226415 0.96226415] - -mean value: 0.9598164201937787 - -key: test_roc_auc -value: [0.80952381 0.80952381 0.80595238 0.73452381 0.90357143 0.8797619 - 0.77738095 0.73095238 0.85238095 0.82857143] - -mean value: 0.8132142857142857 - -key: train_roc_auc -value: [0.96216216 0.95675676 0.96220575 0.9487213 0.95680035 0.95681488 - 0.96230747 0.96771287 0.96233653 0.962322 ] - -mean value: 0.9598140075559429 - -key: test_jcc -value: [0.66666667 0.68 0.66666667 0.54166667 0.81818182 0.77272727 - 0.59090909 0.56 0.72727273 0.69565217] - -mean value: 0.6719743083003953 - -key: train_jcc -value: [0.92513369 0.91489362 0.92553191 0.9 0.91489362 0.91534392 - 0.92631579 0.93617021 0.92553191 0.92592593] - -mean value: 0.9209740597178842 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 50 - -mean value: 50.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 156 - -mean value: 156.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.74 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.53300953 0.51279473 0.51417923 0.54170537 0.52085948 0.51572061 - 0.51508975 0.50532246 0.51649785 0.51727605] - -mean value: 0.5192455053329468 - -key: score_time -value: [0.00918722 0.00914145 0.00938344 0.00916529 0.00929856 0.00910997 - 0.00938439 0.00916171 0.00917625 0.00926542] - -mean value: 0.009227371215820313 - -key: test_mcc -value: [1. 1. 1. 0.90238095 1. 0.95238095 - 1. 0.86333169 0.95227002 0.95238095] - -mean value: 0.9622744566930101 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.95238095 1. 0.97560976 - 1. 0.93023256 0.97435897 0.97560976] - -mean value: 0.9808191997074583 - -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. 0.95238095 1. 1. - 1. 0.86956522 1. 0.95238095] - -mean value: 0.977432712215321 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.95238095 1. 0.95238095 - 1. 1. 0.95 1. ] - -mean value: 0.9854761904761904 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.95121951 1. 0.97560976 - 1. 0.92682927 0.97560976 0.97560976] - -mean value: 0.9804878048780488 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.95119048 1. 0.97619048 - 1. 0.92857143 0.975 0.97619048] - -mean value: 0.9807142857142856 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.90909091 1. 0.95238095 - 1. 0.86956522 0.95 0.95238095] - -mean value: 0.9633418031244118 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 201 - -mean value: 201.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/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: 5 - -mean value: 5.0 - -key: TP -value: 203 - -mean value: 203.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.91 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02316737 0.02680206 0.02732277 0.02696562 0.02697563 0.02746534 - 0.02724886 0.02720308 0.02752709 0.02719426] - -mean value: 0.026787209510803222 - -key: score_time -value: [0.01268864 0.0126133 0.01294136 0.01298118 0.0129559 0.01299787 - 0.01317263 0.01305532 0.01269197 0.01263595] - -mean value: 0.012873411178588867 - -key: test_mcc -value: [0.48737732 0.78446454 0.7633652 0.7197263 0.86333169 0.73786479 - 0.80817439 0.56836003 0.86333169 0.56190476] - -mean value: 0.7157900728918658 - -key: train_mcc -value: [1. 0.98391316 0.98921825 0.9734012 0.97339739 0.97339739 - 0.99462366 0.98921825 0.99462366 0.95776892] - -mean value: 0.9829561871566351 - -key: test_fscore -value: [0.76923077 0.89361702 0.88888889 0.86956522 0.92307692 0.875 - 0.89473684 0.79069767 0.93023256 0.7804878 ] - -mean value: 0.8615533699405933 - -key: train_fscore -value: [1. 0.9919571 0.99459459 0.98666667 0.98630137 0.98630137 - 0.99730458 0.99462366 0.99730458 0.97802198] - -mean value: 0.991307590390137 - -key: test_precision -value: [0.64516129 0.80769231 0.83333333 0.8 1. 0.77777778 - 0.94444444 0.73913043 0.86956522 0.76190476] - -mean value: 0.8179009567649118 - -key: train_precision -value: [1. 0.98404255 0.99459459 0.97368421 1. 1. - 1. 0.99462366 1. 1. ] - -mean value: 0.9946945014226378 - -key: test_recall -value: [0.95238095 1. 0.95238095 0.95238095 0.85714286 1. - 0.85 0.85 1. 0.8 ] - -mean value: 0.9214285714285715 - -key: train_recall -value: [1. 1. 0.99459459 1. 0.97297297 0.97297297 - 0.99462366 0.99462366 0.99462366 0.95698925] - -mean value: 0.9881400755594305 - -key: test_accuracy -value: [0.71428571 0.88095238 0.87804878 0.85365854 0.92682927 0.85365854 - 0.90243902 0.7804878 0.92682927 0.7804878 ] - -mean value: 0.8497677119628341 - -key: train_accuracy -value: [1. 0.99189189 0.99460916 0.98652291 0.98652291 0.98652291 - 0.99730458 0.99460916 0.99730458 0.97843666] - -mean value: 0.9913724775988927 - -key: test_roc_auc -value: [0.71428571 0.88095238 0.87619048 0.85119048 0.92857143 0.85 - 0.90119048 0.78214286 0.92857143 0.78095238] - -mean value: 0.849404761904762 - -key: train_roc_auc -value: [1. 0.99189189 0.99460913 0.98655914 0.98648649 0.98648649 - 0.99731183 0.99460913 0.99731183 0.97849462] - -mean value: 0.9913760534728278 - -key: test_jcc -value: [0.625 0.80769231 0.8 0.76923077 0.85714286 0.77777778 - 0.80952381 0.65384615 0.86956522 0.64 ] - -mean value: 0.7609778892604979 - -key: train_jcc -value: [1. 0.98404255 0.98924731 0.97368421 0.97297297 0.97297297 - 0.99462366 0.98930481 0.99462366 0.95698925] - -mean value: 0.9828461393465717 - -key: TN -value: 160 - -mean value: 160.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 46 - -mean value: 46.0 - -key: TP -value: 190 - -mean value: 190.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.68 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01527357 0.01521778 0.01518393 0.03679013 0.036515 0.04166365 - 0.03657699 0.04537487 0.02655697 0.03394699] - -mean value: 0.03030998706817627 - -key: score_time -value: [0.01232934 0.01232672 0.01249433 0.02470875 0.02159786 0.02464294 - 0.02181315 0.03624797 0.02282119 0.03252983] - -mean value: 0.02215120792388916 - -key: test_mcc -value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095 - 0.95227002 0.65952381 0.90692382 0.85441771] - -mean value: 0.8840883660082678 - -key: train_mcc -value: [0.97298719 0.97298719 0.97305937 0.95692987 0.96771006 0.96771006 - 0.97306016 0.96765475 0.95692987 0.96771194] - -mean value: 0.967674046655907 - -key: test_fscore -value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976 - 0.97435897 0.82926829 0.95238095 0.92307692] - -mean value: 0.9413115339944609 - -key: train_fscore -value: [0.98644986 0.98644986 0.98644986 0.97849462 0.98369565 0.98369565 - 0.98652291 0.98387097 0.97837838 0.98378378] - -mean value: 0.9837791562454985 - -key: test_precision -value: [1. 1. 0.9047619 0.9 1. 1. - 1. 0.80952381 0.90909091 0.94736842] - -mean value: 0.9470745044429254 - -key: train_precision -value: [0.98913043 0.98913043 0.98913043 0.97326203 0.98907104 0.98907104 - 0.98918919 0.98387097 0.98369565 0.98913043] - -mean value: 0.9864681656823766 - -key: test_recall -value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095 - 0.95 0.85 1. 0.9 ] - -mean value: 0.9366666666666668 - -key: train_recall -value: [0.98378378 0.98378378 0.98378378 0.98378378 0.97837838 0.97837838 - 0.98387097 0.98387097 0.97311828 0.97849462] - -mean value: 0.9811246730601569 - -key: test_accuracy -value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976 - 0.97560976 0.82926829 0.95121951 0.92682927] - -mean value: 0.9415214866434379 - -key: train_accuracy -value: [0.98648649 0.98648649 0.98652291 0.97843666 0.98382749 0.98382749 - 0.98652291 0.98382749 0.97843666 0.98382749] - -mean value: 0.9838202083485104 - -key: test_roc_auc -value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048 - 0.975 0.8297619 0.95238095 0.92619048] - -mean value: 0.9416666666666667 - -key: train_roc_auc -value: [0.98648649 0.98648649 0.98651555 0.97845103 0.98381285 0.98381285 - 0.98653008 0.98382738 0.97845103 0.98384191] - -mean value: 0.983821563498983 - -key: test_jcc -value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095 - 0.95 0.70833333 0.90909091 0.85714286] - -mean value: 0.8938024656502919 - -key: train_jcc -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: 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 - ros_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: 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 - ros_CV['Resampling'] = rs_ros -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: 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 - ros_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: 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 - ros_BT['Resampling'] = rs_ros -[0.97326203 0.97326203 0.97326203 0.95789474 0.96791444 0.96791444 - 0.97340426 0.96825397 0.95767196 0.96808511] - -mean value: 0.9680924997732191 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.3271625 0.26242065 0.26160622 0.2503655 0.16396499 0.2712121 - 0.25434446 0.27765441 0.22024608 0.19233727] - -mean value: 0.2481314182281494 - -key: score_time -value: [0.02375269 0.01582861 0.02272272 0.02287126 0.01536274 0.0208714 - 0.02181959 0.03867531 0.01217222 0.02051806] - -mean value: 0.021459460258483887 - -key: test_mcc -value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095 - 0.95227002 0.65952381 0.90692382 0.85441771] - -mean value: 0.8840883660082678 - -key: train_mcc -value: [0.97298719 0.97298719 0.98384191 0.95692987 0.96771006 0.96771006 - 0.97306016 0.9784365 0.95692987 0.96771194] - -mean value: 0.9698304748061133 - -key: test_fscore -value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976 - 0.97435897 0.82926829 0.95238095 0.92307692] - -mean value: 0.9413115339944609 - -key: train_fscore -value: [0.98644986 0.98644986 0.99191375 0.97849462 0.98369565 0.98369565 - 0.98652291 0.98924731 0.97837838 0.98378378] - -mean value: 0.984863178867309 - -key: test_precision -value: [1. 1. 0.9047619 0.9 1. 1. - 1. 0.80952381 0.90909091 0.94736842] - -mean value: 0.9470745044429254 - -key: train_precision -value: [0.98913043 0.98913043 0.98924731 0.97326203 0.98907104 0.98907104 - 0.98918919 0.98924731 0.98369565 0.98913043] - -mean value: 0.9870174877955137 - -key: test_recall -value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095 - 0.95 0.85 1. 0.9 ] - -mean value: 0.9366666666666668 - -key: train_recall -value: [0.98378378 0.98378378 0.99459459 0.98378378 0.97837838 0.97837838 - 0.98387097 0.98924731 0.97311828 0.97849462] - -mean value: 0.9827433885498402 - -key: test_accuracy -value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976 - 0.97560976 0.82926829 0.95121951 0.92682927] - -mean value: 0.9415214866434379 - -key: train_accuracy -value: [0.98648649 0.98648649 0.99191375 0.97843666 0.98382749 0.98382749 - 0.98652291 0.98921833 0.97843666 0.98382749] - -mean value: 0.9848983754644133 - -key: test_roc_auc -value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048 - 0.975 0.8297619 0.95238095 0.92619048] - -mean value: 0.9416666666666667 - -key: train_roc_auc -value: [0.98648649 0.98648649 0.99192095 0.97845103 0.98381285 0.98381285 - 0.98653008 0.98921825 0.97845103 0.98384191] - -mean value: 0.9849011915140947 - -key: test_jcc -value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095 - 0.95 0.70833333 0.90909091 0.85714286] - -mean value: 0.8938024656502919 - -key: train_jcc -value: [0.97326203 0.97326203 0.98395722 0.95789474 0.96791444 0.96791444 - 0.97340426 0.9787234 0.95767196 0.96808511] - -mean value: 0.9702089620899317 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02878523 0.02737093 0.03174114 0.02732897 0.02855468 0.04491949 - 0.0289259 0.03289247 0.02990484 0.02912402] - -mean value: 0.030954766273498534 - -key: score_time -value: [0.01180434 0.01176667 0.01328897 0.01191807 0.01183844 0.01215386 - 0.01213384 0.01283312 0.01175165 0.01184511] - -mean value: 0.012133407592773437 - -key: test_mcc -value: [0.73029674 0.54772256 1. 0.90829511 0.71562645 0.44038551 - 0.63305416 1. 0.62641448 0.13483997] - -mean value: 0.6736634981203286 - -key: train_mcc -value: [0.88425952 0.85282059 0.8848558 0.93717105 0.88486842 0.85339912 - 0.86391052 0.87454765 0.86509383 0.89528509] - -mean value: 0.8796211592825314 - -key: test_fscore -value: [0.86956522 0.7826087 1. 0.94736842 0.84210526 0.72727273 - 0.8 1. 0.83333333 0.64 ] - -mean value: 0.8442253657860064 - -key: train_fscore -value: [0.94240838 0.92708333 0.94300518 0.96875 0.94240838 0.92708333 - 0.93121693 0.9375 0.93333333 0.94736842] - -mean value: 0.9400157287543415 - -key: test_precision -value: [0.83333333 0.75 1. 1. 0.88888889 0.66666667 - 0.88888889 1. 0.76923077 0.57142857] - -mean value: 0.8368437118437118 - -key: train_precision -value: [0.9375 0.91752577 0.93814433 0.96875 0.94736842 0.92708333 - 0.93617021 0.92783505 0.91 0.94736842] - -mean value: 0.9357745542843728 - -key: test_recall -value: [0.90909091 0.81818182 1. 0.9 0.8 0.8 - 0.72727273 1. 0.90909091 0.72727273] - -mean value: 0.859090909090909 - -key: train_recall -value: [0.94736842 0.93684211 0.94791667 0.96875 0.9375 0.92708333 - 0.92631579 0.94736842 0.95789474 0.94736842] - -mean value: 0.9444407894736843 - -key: test_accuracy -value: [0.86363636 0.77272727 1. 0.95238095 0.85714286 0.71428571 - 0.80952381 1. 0.80952381 0.57142857] - -mean value: 0.8350649350649351 - -key: train_accuracy -value: [0.94210526 0.92631579 0.94240838 0.96858639 0.94240838 0.92670157 - 0.93193717 0.93717277 0.93193717 0.94764398] - -mean value: 0.9397216864149904 - -key: test_roc_auc -value: [0.86363636 0.77272727 1. 0.95 0.85454545 0.71818182 - 0.81363636 1. 0.80454545 0.56363636] - -mean value: 0.8340909090909092 - -key: train_roc_auc -value: [0.94210526 0.92631579 0.94237939 0.96858553 0.94243421 0.92669956 - 0.93190789 0.93722588 0.93207237 0.94764254] - -mean value: 0.9397368421052631 - -key: test_jcc -value: [0.76923077 0.64285714 1. 0.9 0.72727273 0.57142857 - 0.66666667 1. 0.71428571 0.47058824] - -mean value: 0.7462329827035709 - -key: train_jcc -value: [0.89108911 0.86407767 0.89215686 0.93939394 0.89108911 0.86407767 - 0.87128713 0.88235294 0.875 0.9 ] - -mean value: 0.8870524429655987 - -key: TN -value: 86 - -mean value: 86.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 91 - -mean value: 91.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.85 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.8474319 0.64568019 0.65819812 0.74121666 0.64531708 0.66669083 - 0.67523885 0.69857788 0.63087988 0.70886588] - -mean value: 0.6918097257614135 - -key: score_time -value: [0.01244688 0.01247382 0.01246166 0.01256037 0.01257372 0.0124898 - 0.01269221 0.01252818 0.01268768 0.01263762] - -mean value: 0.012555193901062012 - -key: test_mcc -value: [1. 0.83205029 1. 0.90829511 0.90909091 0.80909091 - 0.74795759 1. 0.82275335 0.58630197] - -mean value: 0.8615540131941046 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.9 1. 0.94736842 0.95238095 0.9 - 0.84210526 1. 0.91666667 0.81481481] - -mean value: 0.927333611807296 - -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. 1. 0.90909091 0.9 - 1. 1. 0.84615385 0.6875 ] - -mean value: 0.9342744755244755 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.81818182 1. 0.9 1. 0.9 - 0.72727273 1. 1. 1. ] - -mean value: 0.9345454545454546 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.90909091 1. 0.95238095 0.95238095 0.9047619 - 0.85714286 1. 0.9047619 0.76190476] - -mean value: 0.9242424242424242 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.90909091 1. 0.95 0.95454545 0.90454545 - 0.86363636 1. 0.9 0.75 ] - -mean value: 0.9231818181818182 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.81818182 1. 0.9 0.90909091 0.81818182 - 0.72727273 1. 0.84615385 0.6875 ] - -mean value: 0.8706381118881119 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 97 - -mean value: 97.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 98 - -mean value: 98.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.9 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01245213 0.01243806 0.00974703 0.00927186 0.0097537 0.01013827 - 0.00901365 0.00911999 0.00905824 0.00975323] - -mean value: 0.010074615478515625 - -key: score_time -value: [0.01222992 0.01084161 0.00986362 0.00998068 0.00956964 0.0091145 - 0.00861526 0.00893164 0.0087111 0.00935578] - -mean value: 0.00972137451171875 - -key: test_mcc -value: [0.20412415 0.18898224 0.82572282 0.14545455 0.55161872 0.14545455 - 0.44038551 0.43007562 0.03739788 0.24120908] - -mean value: 0.32104250973123794 - -key: train_mcc -value: [0.41809986 0.44147316 0.43405367 0.45081174 0.45172183 0.400897 - 0.48801375 0.44832596 0.47224501 0.41856152] - -mean value: 0.4424203495544211 - -key: test_fscore -value: [0.66666667 0.64 0.90909091 0.57142857 0.7826087 0.57142857 - 0.7 0.75 0.58333333 0.69230769] - -mean value: 0.6866864439907918 - -key: train_fscore -value: [0.73076923 0.74285714 0.74178404 0.75229358 0.74641148 0.72641509 - 0.73224044 0.73631841 0.75121951 0.7254902 ] - -mean value: 0.7385799120152144 - -key: test_precision -value: [0.5625 0.57142857 0.83333333 0.54545455 0.69230769 0.54545455 - 0.77777778 0.69230769 0.53846154 0.6 ] - -mean value: 0.6359025696525695 - -key: train_precision -value: [0.67256637 0.67826087 0.67521368 0.67213115 0.69026549 0.6637931 - 0.76136364 0.69811321 0.7 0.67889908] - -mean value: 0.6890606580654846 - -key: test_recall -value: [0.81818182 0.72727273 1. 0.6 0.9 0.6 - 0.63636364 0.81818182 0.63636364 0.81818182] - -mean value: 0.7554545454545455 - -key: train_recall -value: [0.8 0.82105263 0.82291667 0.85416667 0.8125 0.80208333 - 0.70526316 0.77894737 0.81052632 0.77894737] - -mean value: 0.7986403508771931 - -key: test_accuracy -value: [0.59090909 0.59090909 0.9047619 0.57142857 0.76190476 0.57142857 - 0.71428571 0.71428571 0.52380952 0.61904762] - -mean value: 0.6562770562770563 - -key: train_accuracy -value: [0.70526316 0.71578947 0.71204188 0.71727749 0.72251309 0.69633508 - 0.7434555 0.72251309 0.73298429 0.70680628] - -mean value: 0.7174979333149627 - -key: test_roc_auc -value: [0.59090909 0.59090909 0.90909091 0.57272727 0.76818182 0.57272727 - 0.71818182 0.70909091 0.51818182 0.60909091] - -mean value: 0.655909090909091 - -key: train_roc_auc -value: [0.70526316 0.71578947 0.71145833 0.71655702 0.72203947 0.69577851 - 0.74325658 0.72280702 0.73338816 0.70718202] - -mean value: 0.7173519736842104 - -key: test_jcc -value: [0.5 0.47058824 0.83333333 0.4 0.64285714 0.4 - 0.53846154 0.6 0.41176471 0.52941176] - -mean value: 0.5326416720534368 - -key: train_jcc -value: [0.57575758 0.59090909 0.58955224 0.60294118 0.59541985 0.57037037 - 0.57758621 0.58267717 0.6015625 0.56923077] - -mean value: 0.585600694112349 - -key: TN -value: 59 - -mean value: 59.0 - -key: FP -value: 26 - -mean value: 26.0 - -key: FN -value: 47 - -mean value: 47.0 - -key: TP -value: 80 - -mean value: 80.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.35 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00917554 0.00898218 0.00888371 0.00896883 0.00891352 0.00882316 - 0.00896239 0.00886655 0.00893283 0.00890827] - -mean value: 0.00894169807434082 - -key: score_time -value: [0.00879407 0.00865746 0.00865412 0.00859976 0.00862265 0.00858164 - 0.00859523 0.00866842 0.00866795 0.0087111 ] - -mean value: 0.008655238151550292 - -key: test_mcc -value: [ 0.09245003 0.18898224 -0.05504819 0.13483997 0.43007562 -0.23636364 - 0.18090681 0.13762047 -0.35527986 -0.03015113] - -mean value: 0.0488032319211866 - -key: train_mcc -value: [0.50386393 0.41134755 0.45548246 0.47824733 0.44500555 0.51226436 - 0.48461189 0.55362565 0.48123065 0.53504756] - -mean value: 0.48607269365804306 - -key: test_fscore -value: [0.5 0.52631579 0.42105263 0.47058824 0.66666667 0.38095238 - 0.47058824 0.60869565 0.41666667 0.35294118] - -mean value: 0.4814467434571082 - -key: train_fscore -value: [0.72093023 0.69565217 0.72916667 0.70175439 0.68604651 0.77073171 - 0.70930233 0.73809524 0.71590909 0.74285714] - -mean value: 0.7210445475490609 - -key: test_precision -value: [0.55555556 0.625 0.44444444 0.57142857 0.75 0.36363636 - 0.66666667 0.58333333 0.38461538 0.5 ] - -mean value: 0.544468031968032 - -key: train_precision -value: [0.80519481 0.71910112 0.72916667 0.8 0.77631579 0.72477064 - 0.79220779 0.84931507 0.77777778 0.8125 ] - -mean value: 0.7786349665611217 - -key: test_recall -value: [0.45454545 0.45454545 0.4 0.4 0.6 0.4 - 0.36363636 0.63636364 0.45454545 0.27272727] - -mean value: 0.44363636363636355 - -key: train_recall -value: [0.65263158 0.67368421 0.72916667 0.625 0.61458333 0.82291667 - 0.64210526 0.65263158 0.66315789 0.68421053] - -mean value: 0.6760087719298246 - -key: test_accuracy -value: [0.54545455 0.59090909 0.47619048 0.57142857 0.71428571 0.38095238 - 0.57142857 0.57142857 0.33333333 0.47619048] - -mean value: 0.523160173160173 - -key: train_accuracy -value: [0.74736842 0.70526316 0.72774869 0.73298429 0.71727749 0.7539267 - 0.7382199 0.76963351 0.7382199 0.76439791] - -mean value: 0.739503995591072 - -key: test_roc_auc -value: [0.54545455 0.59090909 0.47272727 0.56363636 0.70909091 0.38181818 - 0.58181818 0.56818182 0.32727273 0.48636364] - -mean value: 0.5227272727272727 - -key: train_roc_auc -value: [0.74736842 0.70526316 0.72774123 0.73355263 0.71781798 0.7535636 - 0.7377193 0.76902412 0.73782895 0.76398026] - -mean value: 0.7393859649122807 - -key: test_jcc -value: [0.33333333 0.35714286 0.26666667 0.30769231 0.5 0.23529412 - 0.30769231 0.4375 0.26315789 0.21428571] - -mean value: 0.32227651991970874 - -key: train_jcc -value: [0.56363636 0.53333333 0.57377049 0.54054054 0.52212389 0.62698413 - 0.54954955 0.58490566 0.55752212 0.59090909] - -mean value: 0.5643275174832757 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 59 - -mean value: 59.0 - -key: FN -value: 42 - -mean value: 42.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.06 - -Accuracy on Blind test: 0.49 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00854278 0.00899529 0.00837398 0.00849628 0.00835133 0.00847888 - 0.00856066 0.00836992 0.00841522 0.00843906] - -mean value: 0.008502340316772461 - -key: score_time -value: [0.01104236 0.01535344 0.0098412 0.00971603 0.00971007 0.00972986 - 0.00974631 0.00971174 0.00977564 0.00984955] - -mean value: 0.01044762134552002 - -key: test_mcc -value: [ 0.09245003 0.36514837 0.23636364 0.63305416 0.52727273 0.03015113 - 0.14545455 0.43007562 -0.05504819 -0.05504819] - -mean value: 0.2349873851761913 - -key: train_mcc -value: [0.58950634 0.56845255 0.51847791 0.52878605 0.54973133 0.56027205 - 0.55060406 0.53932217 0.61350202 0.55103649] - -mean value: 0.556969097894801 - -key: test_fscore -value: [0.5 0.69565217 0.6 0.81818182 0.76190476 0.375 - 0.57142857 0.75 0.52173913 0.52173913] - -mean value: 0.611564558629776 - -key: train_fscore -value: [0.79581152 0.78306878 0.75789474 0.76683938 0.77720207 0.78350515 - 0.77948718 0.76595745 0.81025641 0.76502732] - -mean value: 0.7785050002608345 - -key: test_precision -value: [0.55555556 0.66666667 0.6 0.75 0.72727273 0.5 - 0.6 0.69230769 0.5 0.5 ] - -mean value: 0.6091802641802643 - -key: train_precision -value: [0.79166667 0.78723404 0.76595745 0.7628866 0.77319588 0.7755102 - 0.76 0.77419355 0.79 0.79545455] - -mean value: 0.7776098928178448 - -key: test_recall -value: [0.45454545 0.72727273 0.6 0.9 0.8 0.3 - 0.54545455 0.81818182 0.54545455 0.54545455] - -mean value: 0.6236363636363637 - -key: train_recall -value: [0.8 0.77894737 0.75 0.77083333 0.78125 0.79166667 - 0.8 0.75789474 0.83157895 0.73684211] - -mean value: 0.7799013157894736 - -key: test_accuracy -value: [0.54545455 0.68181818 0.61904762 0.80952381 0.76190476 0.52380952 - 0.57142857 0.71428571 0.47619048 0.47619048] - -mean value: 0.6179653679653679 - -key: train_accuracy -value: [0.79473684 0.78421053 0.7591623 0.76439791 0.77486911 0.78010471 - 0.77486911 0.76963351 0.80628272 0.77486911] - -mean value: 0.7783135850096445 - -key: test_roc_auc -value: [0.54545455 0.68181818 0.61818182 0.81363636 0.76363636 0.51363636 - 0.57272727 0.70909091 0.47272727 0.47272727] - -mean value: 0.6163636363636364 - -key: train_roc_auc -value: [0.79473684 0.78421053 0.75921053 0.76436404 0.77483553 0.78004386 - 0.775 0.76957237 0.80641447 0.77467105] - -mean value: 0.7783059210526316 - -key: test_jcc -value: [0.33333333 0.53333333 0.42857143 0.69230769 0.61538462 0.23076923 - 0.4 0.6 0.35294118 0.35294118] - -mean value: 0.453958198664081 - -key: train_jcc -value: [0.66086957 0.64347826 0.61016949 0.62184874 0.63559322 0.6440678 - 0.63865546 0.62068966 0.68103448 0.61946903] - -mean value: 0.6375875700721912 - -key: TN -value: 65 - -mean value: 65.0 - -key: FP -value: 40 - -mean value: 40.0 - -key: FN -value: 41 - -mean value: 41.0 - -key: TP -value: 66 - -mean value: 66.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.62 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01135945 0.01094675 0.01100111 0.01088858 0.01091003 0.01119184 - 0.01110053 0.01104259 0.01101637 0.01096416] - -mean value: 0.011042141914367675 - -key: score_time -value: [0.00942183 0.00925756 0.00914049 0.00912261 0.00915551 0.00912094 - 0.00912237 0.00918698 0.0099318 0.00918579] - -mean value: 0.009264588356018066 - -key: test_mcc -value: [0.54772256 0.46225016 0.52295779 0.71818182 0.52727273 0.23373675 - 0.55161872 0.45226702 0.03015113 0.13483997] - -mean value: 0.4180998650339932 - -key: train_mcc -value: [0.65266774 0.6843622 0.63426775 0.73019067 0.71757751 0.68585526 - 0.66740308 0.60269927 0.72927062 0.68652637] - -mean value: 0.6790820480507576 - -key: test_fscore -value: [0.76190476 0.75 0.73684211 0.85714286 0.76190476 0.55555556 - 0.73684211 0.76923077 0.61538462 0.64 ] - -mean value: 0.7184807531649636 - -key: train_fscore -value: [0.82539683 0.84375 0.82233503 0.87 0.86153846 0.84375 - 0.83838384 0.80412371 0.86734694 0.84536082] - -mean value: 0.842198562555782 - -key: test_precision -value: [0.8 0.69230769 0.77777778 0.81818182 0.72727273 0.625 - 0.875 0.66666667 0.53333333 0.57142857] - -mean value: 0.7086968586968586 - -key: train_precision -value: [0.82978723 0.83505155 0.8019802 0.83653846 0.84848485 0.84375 - 0.80582524 0.78787879 0.84158416 0.82828283] - -mean value: 0.8259163305773323 - -key: test_recall -value: [0.72727273 0.81818182 0.7 0.9 0.8 0.5 - 0.63636364 0.90909091 0.72727273 0.72727273] - -mean value: 0.7445454545454546 - -key: train_recall -value: [0.82105263 0.85263158 0.84375 0.90625 0.875 0.84375 - 0.87368421 0.82105263 0.89473684 0.86315789] - -mean value: 0.8595065789473683 - -key: test_accuracy -value: [0.77272727 0.72727273 0.76190476 0.85714286 0.76190476 0.61904762 - 0.76190476 0.71428571 0.52380952 0.57142857] - -mean value: 0.7071428571428571 - -key: train_accuracy -value: [0.82631579 0.84210526 0.81675393 0.86387435 0.85863874 0.84293194 - 0.83246073 0.80104712 0.86387435 0.84293194] - -mean value: 0.8390934141636814 - -key: test_roc_auc -value: [0.77272727 0.72727273 0.75909091 0.85909091 0.76363636 0.61363636 - 0.76818182 0.70454545 0.51363636 0.56363636] - -mean value: 0.7045454545454545 - -key: train_roc_auc -value: [0.82631579 0.84210526 0.81661184 0.86365132 0.85855263 0.84292763 - 0.83267544 0.80115132 0.86403509 0.84303728] - -mean value: 0.8391063596491228 - -key: test_jcc -value: [0.61538462 0.6 0.58333333 0.75 0.61538462 0.38461538 - 0.58333333 0.625 0.44444444 0.47058824] - -mean value: 0.5672083961789844 - -key: train_jcc -value: [0.7027027 0.72972973 0.69827586 0.7699115 0.75675676 0.72972973 - 0.72173913 0.67241379 0.76576577 0.73214286] - -mean value: 0.7279167831859517 - -key: TN -value: 71 - -mean value: 71.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 35 - -mean value: 35.0 - -key: TP -value: 79 - -mean value: 79.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.43 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.69027948 1.232337 0.704772 0.73980832 0.75131059 0.69982934 - 0.72087932 0.74564934 0.70578551 0.71666169] - -mean value: 0.770731258392334 - -key: score_time -value: [0.01450229 0.0136013 0.01349425 0.01363969 0.01377344 0.01384234 - 0.01815867 0.01383829 0.01365829 0.01377439] - -mean value: 0.014228296279907227 - -key: test_mcc -value: [ 0.45454545 0.54772256 0.82572282 0.66332496 0.61818182 0.23636364 - 0.63305416 0.66332496 0.23636364 -0.08528029] - -mean value: 0.4793323721352055 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.72727273 0.7826087 0.90909091 0.75 0.8 0.6 - 0.8 0.84615385 0.63636364 0.59259259] - -mean value: 0.7444082407125886 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.72727273 0.75 0.83333333 1. 0.8 0.6 - 0.88888889 0.73333333 0.63636364 0.5 ] - -mean value: 0.746919191919192 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.72727273 0.81818182 1. 0.6 0.8 0.6 - 0.72727273 1. 0.63636364 0.72727273] - -mean value: 0.7636363636363637 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.72727273 0.77272727 0.9047619 0.80952381 0.80952381 0.61904762 - 0.80952381 0.80952381 0.61904762 0.47619048] - -mean value: 0.7357142857142858 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.72727273 0.77272727 0.90909091 0.8 0.80909091 0.61818182 - 0.81363636 0.8 0.61818182 0.46363636] - -mean value: 0.7331818181818182 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.57142857 0.64285714 0.83333333 0.6 0.66666667 0.42857143 - 0.66666667 0.73333333 0.46666667 0.42105263] - -mean value: 0.6030576441102757 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 75 - -mean value: 75.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 31 - -mean value: 31.0 - -key: TP -value: 81 - -mean value: 81.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.63 - -Accuracy on Blind test: 0.84 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0173099 0.01580215 0.01309872 0.01248264 0.01198626 0.01239681 - 0.01150823 0.01147032 0.0118463 0.01268888] - -mean value: 0.013059020042419434 - -key: score_time -value: [0.01170039 0.00911403 0.0089891 0.00858307 0.00857282 0.00855732 - 0.00863171 0.00857186 0.00849724 0.00859404] - -mean value: 0.008981156349182128 - -key: test_mcc -value: [1. 1. 0.80909091 0.90829511 0.80909091 0.90829511 - 0.71818182 0.90909091 0.80909091 1. ] - -mean value: 0.887113566700395 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.9 0.94736842 0.9 0.94736842 - 0.85714286 0.95238095 0.90909091 1. ] - -mean value: 0.9413351560719981 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.9 1. 0.9 1. - 0.9 1. 0.90909091 1. ] - -mean value: 0.9609090909090907 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.9 0.9 0.9 0.9 - 0.81818182 0.90909091 0.90909091 1. ] - -mean value: 0.9236363636363636 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.9047619 0.95238095 0.9047619 0.95238095 - 0.85714286 0.95238095 0.9047619 1. ] - -mean value: 0.9428571428571428 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.90454545 0.95 0.90454545 0.95 - 0.85909091 0.95454545 0.90454545 1. ] - -mean value: 0.9427272727272727 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.81818182 0.9 0.81818182 0.9 - 0.75 0.90909091 0.83333333 1. ] - -mean value: 0.8928787878787879 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 102 - -mean value: 102.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 98 - -mean value: 98.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.89 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.09913039 0.09283686 0.09363127 0.09323001 0.09556413 0.09252 - 0.09354663 0.09332609 0.09272718 0.09323263] - -mean value: 0.09397451877593994 - -key: score_time -value: [0.01736093 0.01741695 0.01741195 0.01764488 0.01735115 0.01733899 - 0.01745343 0.01734233 0.017344 0.01745343] - -mean value: 0.01741180419921875 - -key: test_mcc -value: [0.36514837 0.54772256 0.90829511 0.71818182 0.80909091 0.33709993 - 0.61818182 0.80909091 0.23636364 0.23636364] - -mean value: 0.5585538693908872 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.7826087 0.94736842 0.85714286 0.9 0.58823529 - 0.81818182 0.90909091 0.63636364 0.63636364] - -mean value: 0.7742021934631977 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.7 0.75 1. 0.81818182 0.9 0.71428571 - 0.81818182 0.90909091 0.63636364 0.63636364] - -mean value: 0.7882467532467533 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.63636364 0.81818182 0.9 0.9 0.9 0.5 - 0.81818182 0.90909091 0.63636364 0.63636364] - -mean value: 0.7654545454545455 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.68181818 0.77272727 0.95238095 0.85714286 0.9047619 0.66666667 - 0.80952381 0.9047619 0.61904762 0.61904762] - -mean value: 0.7787878787878787 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.68181818 0.77272727 0.95 0.85909091 0.90454545 0.65909091 - 0.80909091 0.90454545 0.61818182 0.61818182] - -mean value: 0.7777272727272727 - -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 0.64285714 0.9 0.75 0.81818182 0.41666667 - 0.69230769 0.83333333 0.46666667 0.46666667] - -mean value: 0.6486679986679986 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 84 - -mean value: 84.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 81 - -mean value: 81.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.52 - -Accuracy on Blind test: 0.75 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.008847 0.00880098 0.00887179 0.00869989 0.0088563 0.00886416 - 0.00878453 0.00897479 0.00888658 0.00894952] - -mean value: 0.008853554725646973 - -key: score_time -value: [0.00852537 0.00856757 0.00852895 0.00844431 0.00855374 0.00850129 - 0.00854421 0.00863028 0.00860786 0.00860882] - -mean value: 0.008551239967346191 - -key: test_mcc -value: [ 0.09245003 0.36514837 0.42727273 0.43007562 0.71818182 -0.15894099 - 0.42727273 -0.06741999 0.13483997 -0.04545455] - -mean value: 0.23234257449958196 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.5 0.69565217 0.7 0.66666667 0.85714286 0.33333333 - 0.72727273 0.56 0.64 0.47619048] - -mean value: 0.6156258234519104 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.55555556 0.66666667 0.7 0.75 0.81818182 0.375 - 0.72727273 0.5 0.57142857 0.5 ] - -mean value: 0.6164105339105339 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.45454545 0.72727273 0.7 0.6 0.9 0.3 - 0.72727273 0.63636364 0.72727273 0.45454545] - -mean value: 0.6227272727272727 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.54545455 0.68181818 0.71428571 0.71428571 0.85714286 0.42857143 - 0.71428571 0.47619048 0.57142857 0.47619048] - -mean value: 0.6179653679653679 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.54545455 0.68181818 0.71363636 0.70909091 0.85909091 0.42272727 - 0.71363636 0.46818182 0.56363636 0.47727273] - -mean value: 0.6154545454545455 - -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 0.53333333 0.53846154 0.5 0.75 0.2 - 0.57142857 0.38888889 0.47058824 0.3125 ] - -mean value: 0.4598533900739783 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 65 - -mean value: 65.0 - -key: FP -value: 40 - -mean value: 40.0 - -key: FN -value: 41 - -mean value: 41.0 - -key: TP -value: 66 - -mean value: 66.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.11 - -Accuracy on Blind test: 0.55 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -[1.24118233 1.25259256 1.26126266 1.25328112 1.22599387 1.22450852 - 1.22039127 1.22889853 1.221771 1.22961187] - -mean value: 1.2359493732452393 - -key: score_time -value: [0.09635234 0.09605432 0.09466815 0.08944607 0.09384155 0.09182 - 0.08958817 0.09731269 0.09603953 0.09355998] - -mean value: 0.09386827945709228 - -key: test_mcc -value: [0.83205029 0.56694671 0.90829511 0.90909091 0.90909091 0.61818182 - 0.63305416 0.90909091 0.80909091 0.52295779] - -mean value: 0.7617849518009066 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.91666667 0.8 0.94736842 0.95238095 0.95238095 0.8 - 0.8 0.95238095 0.90909091 0.7826087 ] - -mean value: 0.8812877549605238 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.84615385 0.71428571 1. 0.90909091 0.90909091 0.8 - 0.88888889 1. 0.90909091 0.75 ] - -mean value: 0.8726601176601175 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.90909091 0.9 1. 1. 0.8 - 0.72727273 0.90909091 0.90909091 0.81818182] - -mean value: 0.8972727272727272 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.90909091 0.77272727 0.95238095 0.95238095 0.95238095 0.80952381 - 0.80952381 0.95238095 0.9047619 0.76190476] - -mean value: 0.8777056277056279 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.90909091 0.77272727 0.95 0.95454545 0.95454545 0.80909091 - 0.81363636 0.95454545 0.90454545 0.75909091] - -mean value: 0.8781818181818183 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.84615385 0.66666667 0.9 0.90909091 0.90909091 0.66666667 - 0.66666667 0.90909091 0.83333333 0.64285714] - -mean value: 0.794961704961705 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 91 - -mean value: 91.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 95 - -mean value: 95.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.8 - -Accuracy on Blind test: 0.9 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.83920407 0.89417028 0.90412354 0.90621185 0.87500978 0.87239695 - 0.8454318 0.97728825 0.8785305 0.85419989] - -mean value: 0.8846566915512085 - -key: score_time -value: [0.15499449 0.18715811 0.16653514 0.1395185 0.19633818 0.17854738 - 0.18856549 0.19819713 0.19455981 0.18306184] - -mean value: 0.17874760627746583 - -key: test_mcc -value: [0.75592895 0.46225016 0.90829511 0.82572282 0.90909091 0.52727273 - 0.74795759 0.90909091 0.80909091 0.42727273] - -mean value: 0.7281972813438443 - -key: train_mcc -value: [0.95810708 0.95874497 0.96863692 0.93798081 0.94810203 0.93798081 - 0.94811895 0.95832877 0.94769737 0.95832877] - -mean value: 0.9522026470211411 - -key: test_fscore -value: [0.88 0.75 0.94736842 0.90909091 0.95238095 0.76190476 - 0.84210526 0.95238095 0.90909091 0.72727273] - -mean value: 0.8631594896331739 - -key: train_fscore -value: [0.97916667 0.97938144 0.98445596 0.96938776 0.97435897 0.96938776 - 0.97409326 0.97916667 0.97382199 0.97916667] - -mean value: 0.9762387140188749 - -key: test_precision -value: [0.78571429 0.69230769 1. 0.83333333 0.90909091 0.72727273 - 1. 1. 0.90909091 0.72727273] - -mean value: 0.8584082584082584 - -key: train_precision -value: [0.96907216 0.95959596 0.97938144 0.95 0.95959596 0.95 - 0.95918367 0.96907216 0.96875 0.96907216] - -mean value: 0.9633723530805636 - -key: test_recall -value: [1. 0.81818182 0.9 1. 1. 0.8 - 0.72727273 0.90909091 0.90909091 0.72727273] - -mean value: 0.8790909090909089 - -key: train_recall -value: [0.98947368 1. 0.98958333 0.98958333 0.98958333 0.98958333 - 0.98947368 0.98947368 0.97894737 0.98947368] - -mean value: 0.9895175438596493 - -key: test_accuracy -value: [0.86363636 0.72727273 0.95238095 0.9047619 0.95238095 0.76190476 - 0.85714286 0.95238095 0.9047619 0.71428571] - -mean value: 0.859090909090909 - -key: train_accuracy -value: [0.97894737 0.97894737 0.98429319 0.96858639 0.97382199 0.96858639 - 0.97382199 0.97905759 0.97382199 0.97905759] - -mean value: 0.9758941857260954 - -key: test_roc_auc -value: [0.86363636 0.72727273 0.95 0.90909091 0.95454545 0.76363636 - 0.86363636 0.95454545 0.90454545 0.71363636] - -mean value: 0.8604545454545454 - -key: train_roc_auc -value: [0.97894737 0.97894737 0.98426535 0.96847588 0.97373904 0.96847588 - 0.97390351 0.97911184 0.97384868 0.97911184] - -mean value: 0.9758826754385966 - -key: test_jcc -value: [0.78571429 0.6 0.9 0.83333333 0.90909091 0.61538462 - 0.72727273 0.90909091 0.83333333 0.57142857] - -mean value: 0.7684648684648685 - -key: train_jcc -value: [0.95918367 0.95959596 0.96938776 0.94059406 0.95 0.94059406 - 0.94949495 0.95918367 0.94897959 0.95918367] - -mean value: 0.9536197395249729 - -key: TN -value: 89 - -mean value: 89.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 93 - -mean value: 93.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.78 - -Accuracy on Blind test: 0.9 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.06178808 0.04554296 0.04399633 0.04482245 0.21215343 0.04420877 - 0.04602003 0.04388404 0.04623508 0.04778576] - -mean value: 0.0636436939239502 - -key: score_time -value: [0.01035094 0.01071358 0.01052022 0.01035261 0.01173902 0.01051378 - 0.01033735 0.01026654 0.01043367 0.01034498] - -mean value: 0.010557270050048828 - -key: test_mcc -value: [1. 0.91287093 0.90829511 1. 0.90909091 0.90829511 - 0.80909091 0.90909091 0.80909091 0.90829511] - -mean value: 0.9074119884226655 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.95652174 0.94736842 1. 0.95238095 0.94736842 - 0.90909091 0.95238095 0.90909091 0.95652174] - -mean value: 0.9530724043309856 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.91666667 1. 1. 0.90909091 1. - 0.90909091 1. 0.90909091 0.91666667] - -mean value: 0.9560606060606058 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.9 1. 1. 0.9 - 0.90909091 0.90909091 0.90909091 1. ] - -mean value: 0.9527272727272725 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.95454545 0.95238095 1. 0.95238095 0.95238095 - 0.9047619 0.95238095 0.9047619 0.95238095] - -mean value: 0.9525974025974027 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.95454545 0.95 1. 0.95454545 0.95 - 0.90454545 0.95454545 0.90454545 0.95 ] - -mean value: 0.9522727272727272 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.91666667 0.9 1. 0.90909091 0.9 - 0.83333333 0.90909091 0.83333333 0.91666667] - -mean value: 0.9118181818181817 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 101 - -mean value: 101.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 101 - -mean value: 101.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.89 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02883172 0.05682755 0.05887222 0.06574631 0.0676229 0.05554724 - 0.06847453 0.04513168 0.02737117 0.05307961] - -mean value: 0.05275049209594727 - -key: score_time -value: [0.02276325 0.02177358 0.02305865 0.02589798 0.01629591 0.02290034 - 0.02269125 0.01211476 0.01202202 0.02856231] - -mean value: 0.02080800533294678 - -key: test_mcc -value: [0.83205029 0.63636364 0.90909091 0.80909091 0.71562645 0.71818182 - 0.44038551 0.90909091 0.61818182 0.62641448] - -mean value: 0.7214476731026188 - -key: train_mcc -value: [0.97894737 1. 0.97905702 1. 0.98958333 1. - 1. 0.97927405 1. 1. ] - -mean value: 0.9926861768392901 - -key: test_fscore -value: [0.9 0.81818182 0.95238095 0.9 0.84210526 0.85714286 - 0.7 0.95238095 0.81818182 0.83333333] - -mean value: 0.8573706994759627 - -key: train_fscore -value: [0.98947368 1. 0.98958333 1. 0.9947644 1. - 1. 0.98958333 1. 1. ] - -mean value: 0.9963404748782952 - -key: test_precision -value: [1. 0.81818182 0.90909091 0.9 0.88888889 0.81818182 - 0.77777778 1. 0.81818182 0.76923077] - -mean value: 0.86995337995338 - -key: train_precision -value: [0.98947368 1. 0.98958333 1. 1. 1. - 1. 0.97938144 1. 1. ] - -mean value: 0.9958438460842828 - -key: test_recall -value: [0.81818182 0.81818182 1. 0.9 0.8 0.9 - 0.63636364 0.90909091 0.81818182 0.90909091] - -mean value: 0.8509090909090908 - -key: train_recall -value: [0.98947368 1. 0.98958333 1. 0.98958333 1. - 1. 1. 1. 1. ] - -mean value: 0.9968640350877193 - -key: test_accuracy -value: [0.90909091 0.81818182 0.95238095 0.9047619 0.85714286 0.85714286 - 0.71428571 0.95238095 0.80952381 0.80952381] - -mean value: 0.8584415584415584 - -key: train_accuracy -value: [0.98947368 1. 0.9895288 1. 0.9947644 1. - 1. 0.9895288 1. 1. ] - -mean value: 0.9963295673739323 - -key: test_roc_auc -value: [0.90909091 0.81818182 0.95454545 0.90454545 0.85454545 0.85909091 - 0.71818182 0.95454545 0.80909091 0.80454545] - -mean value: 0.8586363636363636 - -key: train_roc_auc -value: [0.98947368 1. 0.98952851 1. 0.99479167 1. - 1. 0.98958333 1. 1. ] - -mean value: 0.9963377192982457 - -key: test_jcc -value: [0.81818182 0.69230769 0.90909091 0.81818182 0.72727273 0.75 - 0.53846154 0.90909091 0.69230769 0.71428571] - -mean value: 0.756918081918082 - -key: train_jcc -value: [0.97916667 1. 0.97938144 1. 0.98958333 1. - 1. 0.97938144 1. 1. ] - -mean value: 0.9927512886597938 - -key: TN -value: 92 - -mean value: 92.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 90 - -mean value: 90.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.81 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02240634 0.00912261 0.00886822 0.00868654 0.00866103 0.00864649 - 0.00873685 0.00869918 0.00888872 0.00869131] - -mean value: 0.010140728950500489 - -key: score_time -value: [0.0115993 0.00894094 0.00857878 0.00843859 0.00846052 0.0084939 - 0.00852418 0.00846744 0.00855279 0.00850368] - -mean value: 0.00885601043701172 - -key: test_mcc -value: [ 0.09090909 0.18257419 0.61818182 0.33636364 0.42727273 0.13483997 - 0.24771685 0.43007562 -0.06741999 0.03739788] - -mean value: 0.24379117869663122 - -key: train_mcc -value: [0.36850272 0.37896836 0.38226912 0.39449154 0.41362292 0.36131235 - 0.41359649 0.36156007 0.45732729 0.37171053] - -mean value: 0.39033613998907246 - -key: test_fscore -value: [0.54545455 0.57142857 0.8 0.66666667 0.7 0.47058824 - 0.6 0.75 0.56 0.58333333] - -mean value: 0.6247471352177235 - -key: train_fscore -value: [0.6875 0.69109948 0.6974359 0.71287129 0.71134021 0.68717949 - 0.70526316 0.67027027 0.73737374 0.68421053] - -mean value: 0.6984544046223988 - -key: test_precision -value: [0.54545455 0.6 0.8 0.63636364 0.7 0.57142857 - 0.66666667 0.69230769 0.5 0.53846154] - -mean value: 0.6250682650682651 - -key: train_precision -value: [0.68041237 0.6875 0.68686869 0.67924528 0.70408163 0.67676768 - 0.70526316 0.68888889 0.70873786 0.68421053] - -mean value: 0.6901976087619398 - -key: test_recall -value: [0.54545455 0.54545455 0.8 0.7 0.7 0.4 - 0.54545455 0.81818182 0.63636364 0.63636364] - -mean value: 0.6327272727272728 - -key: train_recall -value: [0.69473684 0.69473684 0.70833333 0.75 0.71875 0.69791667 - 0.70526316 0.65263158 0.76842105 0.68421053] - -mean value: 0.7075 - -key: test_accuracy -value: [0.54545455 0.59090909 0.80952381 0.66666667 0.71428571 0.57142857 - 0.61904762 0.71428571 0.47619048 0.52380952] - -mean value: 0.6231601731601731 - -key: train_accuracy -value: [0.68421053 0.68947368 0.69109948 0.69633508 0.70680628 0.68062827 - 0.70680628 0.68062827 0.72774869 0.68586387] - -mean value: 0.6949600440892809 - -key: test_roc_auc -value: [0.54545455 0.59090909 0.80909091 0.66818182 0.71363636 0.56363636 - 0.62272727 0.70909091 0.46818182 0.51818182] - -mean value: 0.6209090909090909 - -key: train_roc_auc -value: [0.68421053 0.68947368 0.69100877 0.69605263 0.70674342 0.68053728 - 0.70679825 0.68048246 0.72796053 0.68585526] - -mean value: 0.6949122807017544 - -key: test_jcc -value: [0.375 0.4 0.66666667 0.5 0.53846154 0.30769231 - 0.42857143 0.6 0.38888889 0.41176471] - -mean value: 0.46170455361631835 - -key: train_jcc -value: [0.52380952 0.528 0.53543307 0.55384615 0.552 0.5234375 - 0.54471545 0.50406504 0.584 0.52 ] - -mean value: 0.5369306736326698 - -key: TN -value: 65 - -mean value: 65.0 - -key: FP -value: 39 - -mean value: 39.0 - -key: FN -value: 41 - -mean value: 41.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.32 - -Accuracy on Blind test: 0.68 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01359797 0.01863289 0.01495814 0.01887131 0.01808858 0.01531839 - 0.01982331 0.01887655 0.01818299 0.01601124] - -mean value: 0.01723613739013672 - -key: score_time -value: [0.00850892 0.01137233 0.01131248 0.01158357 0.01162577 0.01158118 - 0.01159835 0.0116539 0.01165724 0.01160526] - -mean value: 0.011249899864196777 - -key: test_mcc -value: [0.61237244 0.81818182 0.90909091 0.82275335 0.62641448 0.30934411 - 0.71818182 0.90829511 0.61818182 0.24120908] - -mean value: 0.6584024928181599 - -key: train_mcc -value: [0.85951623 0.96847471 0.91801541 1. 0.92922547 0.39904191 - 1. 0.96906883 0.92917291 0.94811895] - -mean value: 0.8920634419087234 - -key: test_fscore -value: [0.70588235 0.90909091 0.95238095 0.88888889 0.77777778 0.68965517 - 0.85714286 0.95652174 0.81818182 0.69230769] - -mean value: 0.82478301602563 - -key: train_fscore -value: [0.92134831 0.98429319 0.95959596 1. 0.96216216 0.73563218 - 1. 0.98445596 0.96174863 0.97409326] - -mean value: 0.9483329670667896 - -key: test_precision -value: [1. 0.90909091 0.90909091 1. 0.875 0.52631579 - 0.9 0.91666667 0.81818182 0.6 ] - -mean value: 0.8454346092503988 - -key: train_precision -value: [0.98795181 0.97916667 0.93137255 1. 1. 0.58181818 - 1. 0.96938776 1. 0.95918367] - -mean value: 0.94088806333048 - -key: test_recall -value: [0.54545455 0.90909091 1. 0.8 0.7 1. - 0.81818182 1. 0.81818182 0.81818182] - -mean value: 0.840909090909091 - -key: train_recall -value: [0.86315789 0.98947368 0.98958333 1. 0.92708333 1. - 1. 1. 0.92631579 0.98947368] - -mean value: 0.9685087719298245 - -key: test_accuracy -value: [0.77272727 0.90909091 0.95238095 0.9047619 0.80952381 0.57142857 - 0.85714286 0.95238095 0.80952381 0.61904762] - -mean value: 0.8158008658008657 - -key: train_accuracy -value: [0.92631579 0.98421053 0.95811518 1. 0.96335079 0.63874346 - 1. 0.98429319 0.96335079 0.97382199] - -mean value: 0.9392201708459631 - -key: test_roc_auc -value: [0.77272727 0.90909091 0.95454545 0.9 0.80454545 0.59090909 - 0.85909091 0.95 0.80909091 0.60909091] - -mean value: 0.8159090909090908 - -key: train_roc_auc -value: [0.92631579 0.98421053 0.95794956 1. 0.96354167 0.63684211 - 1. 0.984375 0.96315789 0.97390351] - -mean value: 0.9390296052631578 - -key: test_jcc -value: [0.54545455 0.83333333 0.90909091 0.8 0.63636364 0.52631579 - 0.75 0.91666667 0.69230769 0.52941176] - -mean value: 0.7138944337396349 - -key: train_jcc -value: [0.85416667 0.96907216 0.9223301 1. 0.92708333 0.58181818 - 1. 0.96938776 0.92631579 0.94949495] - -mean value: 0.9099668937924689 - -key: TN -value: 84 - -mean value: 84.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 89 - -mean value: 89.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.87 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01456928 0.01509285 0.01491833 0.01472616 0.01364017 0.01490045 - 0.01457667 0.01463819 0.0145216 0.01388884] - -mean value: 0.014547252655029297 - -key: score_time -value: [0.01182294 0.01178408 0.01197004 0.01183724 0.01172924 0.01182938 - 0.01196957 0.01184297 0.01184607 0.0117929 ] - -mean value: 0.011842441558837891 - -key: test_mcc -value: [0.68313005 0.73029674 0.90909091 0.90829511 0.45226702 0.55161872 - 0.4719399 0.90909091 0.34027852 0.15569979] - -mean value: 0.6111707676032829 - -key: train_mcc -value: [0.87856517 0.92884073 0.92795293 1. 0.57673971 0.79066814 - 0.79946001 0.87603267 0.77378259 0.70617821] - -mean value: 0.8258220151047642 - -key: test_fscore -value: [0.77777778 0.86956522 0.95238095 0.94736842 0.625 0.7826087 - 0.66666667 0.95238095 0.73333333 0.68965517] - -mean value: 0.7996737189049584 - -key: train_fscore -value: [0.93333333 0.96446701 0.96446701 1. 0.68027211 0.89719626 - 0.87573964 0.93478261 0.88785047 0.85585586] - -mean value: 0.899396429082304 - -key: test_precision -value: [1. 0.83333333 0.90909091 1. 0.83333333 0.69230769 - 0.85714286 1. 0.57894737 0.55555556] - -mean value: 0.8259711049184734 - -key: train_precision -value: [0.98823529 0.93137255 0.94059406 1. 0.98039216 0.81355932 - 1. 0.96629213 0.79831933 0.7480315 ] - -mean value: 0.9166796340065385 - -key: test_recall -value: [0.63636364 0.90909091 1. 0.9 0.5 0.9 - 0.54545455 0.90909091 1. 0.90909091] - -mean value: 0.8209090909090909 - -key: train_recall -value: [0.88421053 1. 0.98958333 1. 0.52083333 1. - 0.77894737 0.90526316 1. 1. ] - -mean value: 0.9078837719298246 - -key: test_accuracy -value: [0.81818182 0.86363636 0.95238095 0.95238095 0.71428571 0.76190476 - 0.71428571 0.95238095 0.61904762 0.57142857] - -mean value: 0.7919913419913419 - -key: train_accuracy -value: [0.93684211 0.96315789 0.96335079 1. 0.7539267 0.88481675 - 0.89005236 0.93717277 0.87434555 0.83246073] - -mean value: 0.9036125654450261 - -key: test_roc_auc -value: [0.81818182 0.86363636 0.95454545 0.95 0.70454545 0.76818182 - 0.72272727 0.95454545 0.6 0.55454545] - -mean value: 0.7890909090909091 - -key: train_roc_auc -value: [0.93684211 0.96315789 0.96321272 1. 0.75515351 0.88421053 - 0.88947368 0.93700658 0.875 0.83333333] - -mean value: 0.9037390350877195 - -key: test_jcc -value: [0.63636364 0.76923077 0.90909091 0.9 0.45454545 0.64285714 - 0.5 0.90909091 0.57894737 0.52631579] - -mean value: 0.6826441979073559 - -key: train_jcc -value: [0.875 0.93137255 0.93137255 1. 0.51546392 0.81355932 - 0.77894737 0.87755102 0.79831933 0.7480315 ] - -mean value: 0.8269617550222188 - -key: TN -value: 81 - -mean value: 81.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 87 - -mean value: 87.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.81 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.12517595 0.10848331 0.10823202 0.1077652 0.10984206 0.10818481 - 0.10810757 0.1085639 0.10866189 0.10933065] - -mean value: 0.11023473739624023 - -key: score_time -value: [0.01501179 0.01486182 0.01477385 0.01486659 0.01476145 0.01496387 - 0.01477718 0.01481032 0.0147624 0.01616335] - -mean value: 0.014975261688232423 - -key: test_mcc -value: [1. 0.63636364 0.80909091 1. 0.90909091 0.90909091 - 0.80909091 0.90909091 0.80909091 0.90829511] - -mean value: 0.8699204197138339 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.81818182 0.9 1. 0.95238095 0.95238095 - 0.90909091 0.95238095 0.90909091 0.95652174] - -mean value: 0.9350028232636929 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.81818182 0.9 1. 0.90909091 0.90909091 - 0.90909091 1. 0.90909091 0.91666667] - -mean value: 0.927121212121212 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.81818182 0.9 1. 1. 1. - 0.90909091 0.90909091 0.90909091 1. ] - -mean value: 0.9445454545454546 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.81818182 0.9047619 1. 0.95238095 0.95238095 - 0.9047619 0.95238095 0.9047619 0.95238095] - -mean value: 0.9341991341991343 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.81818182 0.90454545 1. 0.95454545 0.95454545 - 0.90454545 0.95454545 0.90454545 0.95 ] - -mean value: 0.9345454545454543 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.69230769 0.81818182 1. 0.90909091 0.90909091 - 0.83333333 0.90909091 0.83333333 0.91666667] - -mean value: 0.8821095571095571 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 100 - -mean value: 100.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.93 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03018928 0.03429747 0.03949785 0.05620551 0.03740382 0.03586698 - 0.03402066 0.03567505 0.03418803 0.04045534] - -mean value: 0.03777999877929687 - -key: score_time -value: [0.01721668 0.02065182 0.02762413 0.02365732 0.0261569 0.01647234 - 0.02367759 0.0223124 0.0182445 0.02581906] - -mean value: 0.02218327522277832 - -key: test_mcc -value: [1. 0.83205029 0.90829511 0.82275335 0.80909091 1. - 0.80909091 0.90909091 0.80909091 0.90829511] - -mean value: 0.8807757494367419 - -key: train_mcc -value: [1. 0.98952851 1. 0.97905702 0.97905702 0.97905702 - 1. 0.98958333 0.9895822 1. ] - -mean value: 0.9905865090197933 - -key: test_fscore -value: [1. 0.91666667 0.94736842 0.88888889 0.9 1. - 0.90909091 0.95238095 0.90909091 0.95652174] - -mean value: 0.9380008486301392 - -key: train_fscore -value: [1. 0.9947644 1. 0.98958333 0.98958333 0.98958333 - 1. 0.9947644 0.99470899 1. ] - -mean value: 0.9952987790520513 - -key: test_precision -value: [1. 0.84615385 1. 1. 0.9 1. - 0.90909091 1. 0.90909091 0.91666667] - -mean value: 0.948100233100233 - -key: train_precision -value: [1. 0.98958333 1. 0.98958333 0.98958333 0.98958333 - 1. 0.98958333 1. 1. ] - -mean value: 0.9947916666666667 - -key: test_recall -value: [1. 1. 0.9 0.8 0.9 1. - 0.90909091 0.90909091 0.90909091 1. ] - -mean value: 0.9327272727272726 - -key: train_recall -value: [1. 1. 1. 0.98958333 0.98958333 0.98958333 - 1. 1. 0.98947368 1. ] - -mean value: 0.9958223684210527 - -key: test_accuracy -value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 1. - 0.9047619 0.95238095 0.9047619 0.95238095] - -mean value: 0.9385281385281387 - -key: train_accuracy -value: [1. 0.99473684 1. 0.9895288 0.9895288 0.9895288 - 1. 0.9947644 0.9947644 1. ] - -mean value: 0.9952852025351338 - -key: test_roc_auc -value: [1. 0.90909091 0.95 0.9 0.90454545 1. - 0.90454545 0.95454545 0.90454545 0.95 ] - -mean value: 0.9377272727272727 - -key: train_roc_auc -value: [1. 0.99473684 1. 0.98952851 0.98952851 0.98952851 - 1. 0.99479167 0.99473684 1. ] - -mean value: 0.9952850877192982 - -key: test_jcc -value: [1. 0.84615385 0.9 0.8 0.81818182 1. - 0.83333333 0.90909091 0.83333333 0.91666667] - -mean value: 0.8856759906759907 - -key: train_jcc -value: [1. 0.98958333 1. 0.97938144 0.97938144 0.97938144 - 1. 0.98958333 0.98947368 1. ] - -mean value: 0.99067846807741 - -key: TN -value: 100 - -mean value: 100.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 99 - -mean value: 99.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.87 - -Accuracy on Blind test: 0.94 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03967857 0.04909682 0.06145763 0.02647614 0.02489495 0.07871032 - 0.03897619 0.02560925 0.02572608 0.06365681] - -mean value: 0.04342827796936035 - -key: score_time -value: [0.02542925 0.02239847 0.01280093 0.0128448 0.01276064 0.02025652 - 0.01270938 0.01264906 0.01257157 0.01288128] - -mean value: 0.01573019027709961 - -key: test_mcc -value: [ 0.09245003 0.27272727 0.33028913 0.71818182 0.61818182 0.13483997 - 0.24771685 0.23373675 -0.05504819 -0.35527986] - -mean value: 0.22377955940216845 - -key: train_mcc -value: [0.94784115 0.94742091 0.94769737 0.94769164 0.93717105 0.94769737 - 0.91641992 0.95831967 0.95832877 0.97905702] - -mean value: 0.9487644872499189 - -key: test_fscore -value: [0.5 0.63636364 0.63157895 0.85714286 0.8 0.47058824 - 0.6 0.66666667 0.52173913 0.41666667] - -mean value: 0.6100746139937148 - -key: train_fscore -value: [0.97326203 0.97382199 0.97382199 0.97409326 0.96875 0.97382199 - 0.95744681 0.9787234 0.97916667 0.98947368] - -mean value: 0.9742381828563804 - -key: test_precision -value: [0.55555556 0.63636364 0.66666667 0.81818182 0.8 0.57142857 - 0.66666667 0.61538462 0.5 0.38461538] - -mean value: 0.6214862914862915 - -key: train_precision -value: [0.98913043 0.96875 0.97894737 0.96907216 0.96875 0.97894737 - 0.96774194 0.98924731 0.96907216 0.98947368] - -mean value: 0.9769132433043974 - -key: test_recall -value: [0.45454545 0.63636364 0.6 0.9 0.8 0.4 - 0.54545455 0.72727273 0.54545455 0.45454545] - -mean value: 0.6063636363636363 - -key: train_recall -value: [0.95789474 0.97894737 0.96875 0.97916667 0.96875 0.96875 - 0.94736842 0.96842105 0.98947368 0.98947368] - -mean value: 0.9716995614035089 - -key: test_accuracy -value: [0.54545455 0.63636364 0.66666667 0.85714286 0.80952381 0.57142857 - 0.61904762 0.61904762 0.47619048 0.33333333] - -mean value: 0.6134199134199133 - -key: train_accuracy -value: [0.97368421 0.97368421 0.97382199 0.97382199 0.96858639 0.97382199 - 0.95811518 0.97905759 0.97905759 0.9895288 ] - -mean value: 0.9743179939377239 - -key: test_roc_auc -value: [0.54545455 0.63636364 0.66363636 0.85909091 0.80909091 0.56363636 - 0.62272727 0.61363636 0.47272727 0.32727273] - -mean value: 0.6113636363636362 - -key: train_roc_auc -value: [0.97368421 0.97368421 0.97384868 0.97379386 0.96858553 0.97384868 - 0.95805921 0.97900219 0.97911184 0.98952851] - -mean value: 0.9743146929824562 - -key: test_jcc -value: [0.33333333 0.46666667 0.46153846 0.75 0.66666667 0.30769231 - 0.42857143 0.5 0.35294118 0.26315789] - -mean value: 0.4530567935676295 - -key: train_jcc -value: [0.94791667 0.94897959 0.94897959 0.94949495 0.93939394 0.94897959 - 0.91836735 0.95833333 0.95918367 0.97916667] - -mean value: 0.9498795351473923 - -key: TN -value: 66 - -mean value: 66.0 - -key: FP -value: 42 - -mean value: 42.0 - -key: FN -value: 40 - -mean value: 40.0 - -key: TP -value: 64 - -mean value: 64.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.61 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.37137389 0.33760333 0.33995962 0.34063864 0.3470366 0.3378973 - 0.3002677 0.34190512 0.30660367 0.34101677] - -mean value: 0.3364302635192871 - -key: score_time -value: [0.00910854 0.00920963 0.00930381 0.00901294 0.00916195 0.00908446 - 0.00947428 0.00910878 0.00905728 0.00914216] - -mean value: 0.009166383743286132 - -key: test_mcc -value: [1. 0.83205029 0.90909091 0.82275335 0.80909091 0.90909091 - 0.71818182 1. 0.80909091 0.90829511] - -mean value: 0.8717644206319989 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.91666667 0.95238095 0.88888889 0.9 0.95238095 - 0.85714286 1. 0.90909091 0.95652174] - -mean value: 0.9333072965681662 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.84615385 0.90909091 1. 0.9 0.90909091 - 0.9 1. 0.90909091 0.91666667] - -mean value: 0.9290093240093238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 0.8 0.9 1. - 0.81818182 1. 0.90909091 1. ] - -mean value: 0.9427272727272726 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 0.95238095 - 0.85714286 1. 0.9047619 0.95238095] - -mean value: 0.9337662337662339 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.90909091 0.95454545 0.9 0.90454545 0.95454545 - 0.85909091 1. 0.90454545 0.95 ] - -mean value: 0.9336363636363636 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.84615385 0.90909091 0.8 0.81818182 0.90909091 - 0.75 1. 0.83333333 0.91666667] - -mean value: 0.8782517482517482 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 100 - -mean value: 100.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01949501 0.02266788 0.02243805 0.02208471 0.02231646 0.02282453 - 0.02309084 0.0220356 0.02272868 0.02262187] - -mean value: 0.022230362892150878 - -key: score_time -value: [0.01193953 0.01205492 0.01245713 0.01253104 0.01255751 0.01247668 - 0.01246691 0.01270652 0.01268959 0.01259542] - -mean value: 0.012447524070739745 - -key: test_mcc -value: [-0.09245003 0.20412415 0.18090681 0.18090681 0.21968621 0.06741999 - 0.03739788 -0.13762047 -0.13762047 -0.03015113] - -mean value: 0.04925997289082072 - -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 0.64 0.64 0.66666667 0.58333333 - 0.58333333 0.4 0.4 0.35294118] - -mean value: 0.5432941176470588 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.46153846 0.5625 0.53333333 0.53333333 0.52941176 0.5 - 0.53846154 0.44444444 0.44444444 0.5 ] - -mean value: 0.5047467320261438 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.54545455 0.81818182 0.8 0.8 0.9 0.7 - 0.63636364 0.36363636 0.36363636 0.27272727] - -mean value: 0.62 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.45454545 0.59090909 0.57142857 0.57142857 0.57142857 0.52380952 - 0.52380952 0.42857143 0.42857143 0.47619048] - -mean value: 0.5140692640692641 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.45454545 0.59090909 0.58181818 0.58181818 0.58636364 0.53181818 - 0.51818182 0.43181818 0.43181818 0.48636364] - -mean value: 0.5195454545454545 - -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 0.47058824 0.47058824 0.5 0.41176471 - 0.41176471 0.25 0.25 0.21428571] - -mean value: 0.38123249299719886 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 43 - -mean value: 43.0 - -key: FN -value: 64 - -mean value: 64.0 - -key: TP -value: 63 - -mean value: 63.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.13 - -Accuracy on Blind test: 0.57 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.0221839 0.03195024 0.03572011 0.03568769 0.03169751 0.03557086 - 0.03364134 0.03437328 0.03475642 0.03443766] - -mean value: 0.03300189971923828 - -key: score_time -value: [0.02333236 0.02400351 0.02397799 0.02430987 0.02144504 0.02073979 - 0.02383351 0.02371478 0.02098441 0.02247763] - -mean value: 0.02288188934326172 - -key: test_mcc -value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182 - 0.71818182 1. 0.61818182 0.62641448] - -mean value: 0.813852940846407 - -key: train_mcc -value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967 - 0.95832877 0.95832877 0.97927405 0.96864035] - -mean value: 0.9624855237784811 - -key: test_fscore -value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286 - 0.85714286 1. 0.81818182 0.83333333] - -mean value: 0.9096837944664034 - -key: train_fscore -value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144 - 0.97916667 0.97916667 0.98958333 0.98429319] - -mean value: 0.9813304021061862 - -key: test_precision -value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182 - 0.9 1. 0.81818182 0.76923077] - -mean value: 0.8957109557109557 - -key: train_precision -value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776 - 0.96907216 0.96907216 0.97938144 0.97916667] - -mean value: 0.9712802201480404 - -key: test_recall -value: [0.90909091 0.90909091 1. 1. 1. 0.9 - 0.81818182 1. 0.81818182 0.90909091] - -mean value: 0.9263636363636364 - -key: train_recall -value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333 - 0.98947368 0.98947368 1. 0.98947368] - -mean value: 0.9916118421052632 - -key: test_accuracy -value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286 - 0.85714286 1. 0.80952381 0.80952381] - -mean value: 0.905844155844156 - -key: train_accuracy -value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759 - 0.97905759 0.97905759 0.9895288 0.98429319] - -mean value: 0.9811270322402865 - -key: test_roc_auc -value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091 - 0.85909091 1. 0.80909091 0.80454545] - -mean value: 0.9059090909090909 - -key: train_roc_auc -value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219 - 0.97911184 0.97911184 0.98958333 0.98432018] - -mean value: 0.981123903508772 - -key: test_jcc -value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75 - 0.75 1. 0.69230769 0.71428571] - -mean value: 0.841824841824842 - -key: train_jcc -value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596 - 0.95918367 0.95918367 0.97938144 0.96907216] - -mean value: 0.9633776222141466 - -key: TN -value: 94 - -mean value: 94.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 98 - -mean value: 98.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.74 - -Accuracy on Blind test: 0.88 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.2252419 0.22955441 0.22431469 0.22547555 0.24449944 0.23760462 - 0.29956794 0.22531152 0.23448348 0.22501922] - -mean value: 0.2371072769165039 - -key: score_time -value: [0.02095652 0.02331495 0.02272773 0.02966738 0.02431035 0.02423763 - 0.02446938 0.02391839 0.02422833 0.0242424 ] - -mean value: 0.024207305908203126 - -key: test_mcc -value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182 - 0.71818182 1. 0.61818182 0.62641448] - -mean value: 0.813852940846407 - -key: train_mcc -value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967 - 0.95832877 0.95832877 0.97927405 0.96864035] - -mean value: 0.9624855237784811 - -key: test_fscore -value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286 - 0.85714286 1. 0.81818182 0.83333333] - -mean value: 0.9096837944664034 - -key: train_fscore -value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144 - 0.97916667 0.97916667 0.98958333 0.98429319] - -mean value: 0.9813304021061862 - -key: test_precision -value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182 - 0.9 1. 0.81818182 0.76923077] - -mean value: 0.8957109557109557 - -key: train_precision -value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776 - 0.96907216 0.96907216 0.97938144 0.97916667] - -mean value: 0.9712802201480404 - -key: test_recall -value: [0.90909091 0.90909091 1. 1. 1. 0.9 - 0.81818182 1. 0.81818182 0.90909091] - -mean value: 0.9263636363636364 - -key: train_recall -value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333 - 0.98947368 0.98947368 1. 0.98947368] - -mean value: 0.9916118421052632 - -key: test_accuracy -value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286 - 0.85714286 1. 0.80952381 0.80952381] - -mean value: 0.905844155844156 - -key: train_accuracy -value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759 - 0.97905759 0.97905759 0.9895288 0.98429319] - -mean value: 0.9811270322402865 - -key: test_roc_auc -value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091 - 0.85909091 1. 0.80909091 0.80454545] - -mean value: 0.9059090909090909 - -key: train_roc_auc -value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219 - 0.97911184 0.97911184 0.98958333 0.98432018] - -mean value: 0.981123903508772 - -key: test_jcc -value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75 - 0.75 1. 0.69230769 0.71428571] - -mean value: 0.841824841824842 - -key: train_jcc -value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596 - 0.95918367 0.95918367 0.97938144 0.96907216] - -mean value: 0.9633776222141466 - -key: TN -value: 94 - -mean value: 94.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 98 - -mean value: 98.0 - -key: trainingY_neg -value: 106 - -mean value: 106.0 - -key: trainingY_pos -value: 106 - -mean value: 106.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: 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 - rus_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: 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 - rus_CV['Resampling'] = rs_rus -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: 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 - rus_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: 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 - rus_BT['Resampling'] = rs_rus -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -MCC on Blind test: 0.74 - -Accuracy on Blind test: 0.88 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.03361392 0.02674818 0.03462672 0.03609133 0.03604364 0.03495097 - 0.03474545 0.03721118 0.05540252 0.05710363] - -mean value: 0.03865375518798828 - -key: score_time -value: [0.01211977 0.01200271 0.01244116 0.01268816 0.0126245 0.01258707 - 0.01261234 0.01320767 0.01244402 0.01252508] - -mean value: 0.012525248527526855 - -key: test_mcc -value: [0.80952381 0.8660254 0.7098505 0.90238095 0.70714286 0.85441771 - 0.80907152 0.95238095 0.86240942 0.95238095] - -mean value: 0.8425584072097785 - -key: train_mcc -value: [0.90317784 0.92454046 0.89769524 0.91915141 0.91380162 0.93553077 - 0.90846996 0.91421693 0.92473841 0.93025158] - -mean value: 0.9171574208092543 - -key: test_fscore -value: [0.9047619 0.92307692 0.84210526 0.95 0.85 0.92307692 - 0.9 0.97560976 0.93333333 0.97560976] - -mean value: 0.9177573859602102 - -key: train_fscore -value: [0.95054945 0.96174863 0.94850949 0.95956873 0.95675676 0.9673913 - 0.95367847 0.95604396 0.96174863 0.96438356] - -mean value: 0.9580378989464318 - -key: test_precision -value: [0.9047619 1. 0.88888889 0.95 0.85 0.94736842 - 0.94736842 1. 0.875 1. ] - -mean value: 0.9363387635756057 - -key: train_precision -value: [0.96648045 0.97237569 0.95628415 0.96216216 0.96195652 0.97802198 - 0.96153846 0.97206704 0.97237569 0.97777778] - -mean value: 0.9681039921493962 - -key: test_recall -value: [0.9047619 0.85714286 0.8 0.95 0.85 0.9 - 0.85714286 0.95238095 1. 0.95238095] - -mean value: 0.9023809523809524 - -key: train_recall -value: [0.93513514 0.95135135 0.94086022 0.95698925 0.9516129 0.95698925 - 0.94594595 0.94054054 0.95135135 0.95135135] - -mean value: 0.9482127288578901 - -key: test_accuracy -value: [0.9047619 0.92857143 0.85365854 0.95121951 0.85365854 0.92682927 - 0.90243902 0.97560976 0.92682927 0.97560976] - -mean value: 0.9199186991869919 - -key: train_accuracy -value: [0.95135135 0.96216216 0.94878706 0.95956873 0.95687332 0.96765499 - 0.9541779 0.95687332 0.96226415 0.96495957] - -mean value: 0.9584672543163109 - -key: test_roc_auc -value: [0.9047619 0.92857143 0.85238095 0.95119048 0.85357143 0.92619048 - 0.90357143 0.97619048 0.925 0.97619048] - -mean value: 0.9197619047619048 - -key: train_roc_auc -value: [0.95135135 0.96216216 0.94880849 0.9595757 0.95688753 0.96768381 - 0.95415577 0.95682941 0.96223482 0.96492299] - -mean value: 0.9584612031386225 - -key: test_jcc -value: [0.82608696 0.85714286 0.72727273 0.9047619 0.73913043 0.85714286 - 0.81818182 0.95238095 0.875 0.95238095] - -mean value: 0.8509481460568417 - -key: train_jcc -value: [0.90575916 0.92631579 0.90206186 0.92227979 0.91709845 0.93684211 - 0.91145833 0.91578947 0.92631579 0.93121693] - -mean value: 0.9195137678760738 - -key: TN -value: 193 - -mean value: 193.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 186 - -mean value: 186.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.72588873 0.72638083 0.79159212 0.71572828 0.72554207 0.8414104 - 0.73313403 1.04889321 0.86867905 0.7414546 ] - -mean value: 0.7918703317642212 - -key: score_time -value: [0.0126152 0.01262069 0.01263237 0.01266813 0.01270723 0.01270628 - 0.0126307 0.01389456 0.01262283 0.01270795] - -mean value: 0.012780594825744628 - -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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.85811633 0.90889326 0.90649828 0.90649828 0.95238095 0.90692382 - 0.90692382 0.90692382 0.90238095 0.95238095] - -mean value: 0.9107920477231237 - -key: train_mcc -value: [1. 0.98918919 0.98927544 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978464631087196 - -key: test_fscore -value: [0.93023256 0.95 0.94736842 0.94736842 0.97560976 0.95238095 - 0.95 0.95 0.95238095 0.97560976] - -mean value: 0.9530950817201823 - -key: train_fscore -value: [1. 0.99459459 0.99465241 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9989247001011707 - -key: test_precision -value: [0.90909091 1. 1. 1. 0.95238095 0.90909091 - 1. 1. 0.95238095 1. ] - -mean value: 0.9722943722943723 - -key: train_precision -value: [1. 0.99459459 0.9893617 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9983956296722255 - -key: test_recall -value: [0.95238095 0.9047619 0.9 0.9 1. 1. - 0.9047619 0.9047619 0.95238095 0.95238095] - -mean value: 0.9371428571428572 - -key: train_recall -value: [1. 0.99459459 1. 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994594594594595 - -key: test_accuracy -value: [0.92857143 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951 - 0.95121951 0.95121951 0.95121951 0.97560976] - -mean value: 0.9539488966318235 - -key: train_accuracy -value: [1. 0.99459459 0.99460916 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.998920375901508 - -key: test_roc_auc -value: [0.92857143 0.95238095 0.95 0.95 0.97619048 0.95238095 - 0.95238095 0.95238095 0.95119048 0.97619048] - -mean value: 0.9541666666666666 - -key: train_roc_auc -value: [1. 0.99459459 0.99459459 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.998918918918919 - -key: test_jcc -value: [0.86956522 0.9047619 0.9 0.9 0.95238095 0.90909091 - 0.9047619 0.9047619 0.90909091 0.95238095] - -mean value: 0.910679465462074 - -key: train_jcc -value: [1. 0.98924731 0.9893617 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9978609013955616 - -key: TN -value: 200 - -mean value: 200.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.94 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01363659 0.01345634 0.00994134 0.00960851 0.00949359 0.00958729 - 0.0094173 0.00937819 0.00946307 0.00946736] - -mean value: 0.010344958305358887 - -key: score_time -value: [0.01365209 0.00971627 0.00930667 0.00895095 0.0087657 0.0089097 - 0.00887871 0.00876594 0.00890136 0.00898695] - -mean value: 0.009483432769775391 - -key: test_mcc -value: [0.23809524 0.58834841 0.23472895 0.56836003 0.32612121 0.36666667 - 0.41428571 0.60952381 0.51966679 0.31655495] - -mean value: 0.4182351767630109 - -key: train_mcc -value: [0.51446836 0.49423687 0.50746481 0.49629637 0.53221435 0.49149084 - 0.49254324 0.50655846 0.50342023 0.50200402] - -mean value: 0.5040697536773684 - -key: test_fscore -value: [0.61904762 0.80851064 0.65217391 0.79069767 0.68181818 0.68292683 - 0.71428571 0.80952381 0.7826087 0.68181818] - -mean value: 0.7223411257173928 - -key: train_fscore -value: [0.77192982 0.75773196 0.76649746 0.73295455 0.77402597 0.75949367 - 0.75949367 0.7628866 0.76455696 0.76732673] - -mean value: 0.7616897399142623 - -key: test_precision -value: [0.61904762 0.73076923 0.57692308 0.73913043 0.625 0.66666667 - 0.71428571 0.80952381 0.72 0.65217391] - -mean value: 0.6853520465042204 - -key: train_precision -value: [0.71962617 0.72413793 0.72596154 0.77710843 0.74874372 0.71770335 - 0.71428571 0.72906404 0.71904762 0.70776256] - -mean value: 0.7283441069150348 - -key: test_recall -value: [0.61904762 0.9047619 0.75 0.85 0.75 0.7 - 0.71428571 0.80952381 0.85714286 0.71428571] - -mean value: 0.766904761904762 - -key: train_recall -value: [0.83243243 0.79459459 0.81182796 0.69354839 0.80107527 0.80645161 - 0.81081081 0.8 0.81621622 0.83783784] - -mean value: 0.8004795117698343 - -key: test_accuracy -value: [0.61904762 0.78571429 0.6097561 0.7804878 0.65853659 0.68292683 - 0.70731707 0.80487805 0.75609756 0.65853659] - -mean value: 0.7063298490127757 - -key: train_accuracy -value: [0.75405405 0.74594595 0.75202156 0.74663073 0.76549865 0.74393531 - 0.74393531 0.75202156 0.74932615 0.74663073] - -mean value: 0.7500000000000001 - -key: test_roc_auc -value: [0.61904762 0.78571429 0.61309524 0.78214286 0.66071429 0.68333333 - 0.70714286 0.8047619 0.75357143 0.65714286] - -mean value: 0.7066666666666668 - -key: train_roc_auc -value: [0.75405405 0.74594595 0.75185992 0.74677419 0.7654025 0.74376635 - 0.74411508 0.75215054 0.74950596 0.74687591] - -mean value: 0.750045045045045 - -key: test_jcc -value: [0.44827586 0.67857143 0.48387097 0.65384615 0.51724138 0.51851852 - 0.55555556 0.68 0.64285714 0.51724138] - -mean value: 0.569597838778039 - -key: train_jcc -value: [0.62857143 0.60995851 0.62139918 0.57847534 0.63135593 0.6122449 - 0.6122449 0.61666667 0.61885246 0.62248996] - -mean value: 0.6152259261717272 - -key: TN -value: 133 - -mean value: 133.0 - -key: FP -value: 48 - -mean value: 48.0 - -key: FN -value: 73 - -mean value: 73.0 - -key: TP -value: 158 - -mean value: 158.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.38 - -Accuracy on Blind test: 0.72 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00960875 0.00961447 0.00955772 0.00955391 0.00958133 0.00977755 - 0.00968075 0.00956798 0.01065421 0.00960755] - -mean value: 0.009720420837402344 - -key: score_time -value: [0.00918698 0.00876713 0.00875735 0.00879526 0.00878072 0.00886106 - 0.00873804 0.00886297 0.00907731 0.00888038] - -mean value: 0.008870720863342285 - -key: test_mcc -value: [ 0.1490712 0.3478328 0.12293728 0.29009627 0.44466675 0.51320273 - 0.21904762 0.35737186 -0.06905393 0.35038478] - -mean value: 0.27255573395110366 - -key: train_mcc -value: [0.39922511 0.42369499 0.42411077 0.39660058 0.40199754 0.39002634 - 0.4362526 0.42849689 0.41863013 0.41598338] - -mean value: 0.413501833326159 - -key: test_fscore -value: [0.5 0.61111111 0.4375 0.51612903 0.625 0.73684211 - 0.61904762 0.48275862 0.35294118 0.58823529] - -mean value: 0.5469564958957842 - -key: train_fscore -value: [0.63022508 0.6407767 0.64536741 0.61639344 0.64375 0.62619808 - 0.65822785 0.65830721 0.64984227 0.65420561] - -mean value: 0.6423293654148221 - -key: test_precision -value: [0.6 0.73333333 0.58333333 0.72727273 0.83333333 0.77777778 - 0.61904762 0.875 0.46153846 0.76923077] - -mean value: 0.6979867354867355 - -key: train_precision -value: [0.77777778 0.7983871 0.79527559 0.78991597 0.76865672 0.77165354 - 0.79389313 0.78358209 0.78030303 0.77205882] - -mean value: 0.7831503764370378 - -key: test_recall -value: [0.42857143 0.52380952 0.35 0.4 0.5 0.7 - 0.61904762 0.33333333 0.28571429 0.47619048] - -mean value: 0.4616666666666666 - -key: train_recall -value: [0.52972973 0.53513514 0.54301075 0.50537634 0.55376344 0.52688172 - 0.56216216 0.56756757 0.55675676 0.56756757] - -mean value: 0.5447951176983434 - -key: test_accuracy -value: [0.57142857 0.66666667 0.56097561 0.63414634 0.70731707 0.75609756 - 0.6097561 0.63414634 0.46341463 0.65853659] - -mean value: 0.6262485481997677 - -key: train_accuracy -value: [0.68918919 0.7 0.70080863 0.68463612 0.69272237 0.68463612 - 0.70889488 0.70619946 0.70080863 0.70080863] - -mean value: 0.6968704013987033 - -key: test_roc_auc -value: [0.57142857 0.66666667 0.55595238 0.62857143 0.70238095 0.7547619 - 0.60952381 0.64166667 0.46785714 0.66309524] - -mean value: 0.6261904761904762 - -key: train_roc_auc -value: [0.68918919 0.7 0.70123511 0.6851206 0.69309794 0.68506248 - 0.70850044 0.70582679 0.70042139 0.70045045] - -mean value: 0.6968904388259227 - -key: test_jcc -value: [0.33333333 0.44 0.28 0.34782609 0.45454545 0.58333333 - 0.44827586 0.31818182 0.21428571 0.41666667] - -mean value: 0.38364482693718077 - -key: train_jcc -value: [0.4600939 0.47142857 0.47641509 0.44549763 0.47465438 0.45581395 - 0.49056604 0.49065421 0.48130841 0.48611111] - -mean value: 0.47325432898515085 - -key: TN -value: 163 - -mean value: 163.0 - -key: FP -value: 111 - -mean value: 111.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 95 - -mean value: 95.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.61 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00936174 0.00998497 0.01001406 0.00935721 0.00906157 0.0099709 - 0.0100174 0.01004004 0.01016951 0.01013422] - -mean value: 0.009811162948608398 - -key: score_time -value: [0.01630521 0.01282525 0.012254 0.01248646 0.01178503 0.01724839 - 0.017205 0.01558185 0.01626253 0.0120213 ] - -mean value: 0.014397501945495605 - -key: test_mcc -value: [0.14285714 0.46352358 0.21957752 0.27179142 0.46300848 0.31960727 - 0.57570364 0.56836003 0.27338837 0.53864117] - -mean value: 0.38364586184785077 - -key: train_mcc -value: [0.61730841 0.61209986 0.58747618 0.57456037 0.58691428 0.61839695 - 0.55346014 0.60226766 0.60102483 0.5810842 ] - -mean value: 0.5934592871144924 - -key: test_fscore -value: [0.57142857 0.64705882 0.55555556 0.57142857 0.71794872 0.61111111 - 0.80851064 0.76923077 0.61538462 0.72222222] - -mean value: 0.6589879596137418 - -key: train_fscore -value: [0.80222841 0.79888268 0.78309859 0.76878613 0.78431373 0.8033241 - 0.76880223 0.79329609 0.7826087 0.78089888] - -mean value: 0.7866239527605026 - -key: test_precision -value: [0.57142857 0.84615385 0.625 0.66666667 0.73684211 0.6875 - 0.73076923 0.83333333 0.66666667 0.86666667] - -mean value: 0.7231027086948141 - -key: train_precision -value: [0.82758621 0.8265896 0.82248521 0.83125 0.81871345 0.82857143 - 0.79310345 0.82080925 0.84375 0.8128655 ] - -mean value: 0.822572408214349 - -key: test_recall -value: [0.57142857 0.52380952 0.5 0.5 0.7 0.55 - 0.9047619 0.71428571 0.57142857 0.61904762] - -mean value: 0.6154761904761905 - -key: train_recall -value: [0.77837838 0.77297297 0.74731183 0.71505376 0.75268817 0.77956989 - 0.74594595 0.76756757 0.72972973 0.75135135] - -mean value: 0.7540569601859926 - -key: test_accuracy -value: [0.57142857 0.71428571 0.6097561 0.63414634 0.73170732 0.65853659 - 0.7804878 0.7804878 0.63414634 0.75609756] - -mean value: 0.6871080139372822 - -key: train_accuracy -value: [0.80810811 0.80540541 0.79245283 0.78436658 0.79245283 0.80862534 - 0.77628032 0.80053908 0.79784367 0.78975741] - -mean value: 0.7955831572812705 - -key: test_roc_auc -value: [0.57142857 0.71428571 0.60714286 0.63095238 0.73095238 0.65595238 - 0.77738095 0.78214286 0.63571429 0.75952381] - -mean value: 0.6865476190476191 - -key: train_roc_auc -value: [0.80810811 0.80540541 0.79257483 0.78455391 0.7925603 0.80870387 - 0.77619878 0.80045045 0.79766056 0.78965417] - -mean value: 0.7955870386515548 - -key: test_jcc -value: [0.4 0.47826087 0.38461538 0.4 0.56 0.44 - 0.67857143 0.625 0.44444444 0.56521739] - -mean value: 0.4976109518500823 - -key: train_jcc -value: [0.66976744 0.66511628 0.64351852 0.62441315 0.64516129 0.6712963 - 0.62443439 0.65740741 0.64285714 0.640553 ] - -mean value: 0.6484524906404061 - -key: TN -value: 156 - -mean value: 156.0 - -key: FP -value: 79 - -mean value: 79.0 - -key: FN -value: 50 - -mean value: 50.0 - -key: TP -value: 127 - -mean value: 127.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.31 - -Accuracy on Blind test: 0.65 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - [0.01748967 0.01689219 0.0167439 0.01735997 0.0168469 0.01769972 - 0.01706409 0.01706934 0.01685452 0.01693988] - -mean value: 0.01709601879119873 - -key: score_time -value: [0.0109098 0.01085544 0.01085448 0.01093626 0.01070666 0.01160288 - 0.01079202 0.01068521 0.01070261 0.01083326] - -mean value: 0.010887861251831055 - -key: test_mcc -value: [0.62187434 0.76980036 0.51966679 0.65952381 0.60952381 0.7098505 - 0.8047619 0.71121921 0.7633652 0.78072006] - -mean value: 0.6950305975867748 - -key: train_mcc -value: [0.77930111 0.78419633 0.81230044 0.77400792 0.80592851 0.76820692 - 0.77427749 0.76318023 0.78148627 0.78453492] - -mean value: 0.7827420129069609 - -key: test_fscore -value: [0.81818182 0.87179487 0.72222222 0.82926829 0.8 0.84210526 - 0.9047619 0.85 0.88888889 0.86486486] - -mean value: 0.8392088126555393 - -key: train_fscore -value: [0.88642659 0.89010989 0.90358127 0.8852459 0.90322581 0.88409704 - 0.8839779 0.87912088 0.88450704 0.89071038] - -mean value: 0.8891002697697242 - -key: test_precision -value: [0.7826087 0.94444444 0.8125 0.80952381 0.8 0.88888889 - 0.9047619 0.89473684 0.83333333 1. ] - -mean value: 0.8670797918709818 - -key: train_precision -value: [0.90909091 0.90502793 0.92655367 0.9 0.90322581 0.88648649 - 0.9039548 0.89385475 0.92352941 0.90055249] - -mean value: 0.9052276256122077 - -key: test_recall -value: [0.85714286 0.80952381 0.65 0.85 0.8 0.8 - 0.9047619 0.80952381 0.95238095 0.76190476] - -mean value: 0.8195238095238097 - -key: train_recall -value: [0.86486486 0.87567568 0.88172043 0.87096774 0.90322581 0.88172043 - 0.86486486 0.86486486 0.84864865 0.88108108] - -mean value: 0.8737634408602151 - -key: test_accuracy -value: [0.80952381 0.88095238 0.75609756 0.82926829 0.80487805 0.85365854 - 0.90243902 0.85365854 0.87804878 0.87804878] - -mean value: 0.8446573751451801 - -key: train_accuracy -value: [0.88918919 0.89189189 0.90566038 0.88679245 0.90296496 0.88409704 - 0.88679245 0.88140162 0.88948787 0.89218329] - -mean value: 0.8910461134989438 - -key: test_roc_auc -value: [0.80952381 0.88095238 0.75357143 0.8297619 0.8047619 0.85238095 - 0.90238095 0.8547619 0.87619048 0.88095238] - -mean value: 0.8445238095238097 - -key: train_roc_auc -value: [0.88918919 0.89189189 0.90572508 0.88683522 0.90296425 0.88410346 - 0.88673351 0.88135716 0.88937809 0.89215344] - -mean value: 0.8910331299040977 - -key: test_jcc -value: [0.69230769 0.77272727 0.56521739 0.70833333 0.66666667 0.72727273 - 0.82608696 0.73913043 0.8 0.76190476] - -mean value: 0.725964723682115 - -key: train_jcc -value: [0.7960199 0.8019802 0.8241206 0.79411765 0.82352941 0.79227053 - 0.79207921 0.78431373 0.79292929 0.80295567] - -mean value: 0.8004316183121798 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 37 - -mean value: 37.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 169 - -mean value: 169.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.81 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.4426558 1.25770402 1.43716693 1.26745296 1.44111037 1.38606858 - 1.27516031 1.38661623 1.26520538 1.44973159] - -mean value: 1.3608872175216675 - -key: score_time -value: [0.0147295 0.01381016 0.0137701 0.01382208 0.01384902 0.01415205 - 0.01397204 0.01392603 0.01390982 0.01387882] - -mean value: 0.013981962203979492 - -key: test_mcc -value: [0.80952381 0.90889326 0.90649828 0.8547619 0.80907152 0.8047619 - 0.90692382 0.90692382 0.90649828 1. ] - -mean value: 0.8813856601490313 - -key: train_mcc -value: [1. 1. 1. 0.9946235 1. 0.9946235 - 1. 0.99462366 1. 1. ] - -mean value: 0.9983870656085617 - -key: test_fscore -value: [0.9047619 0.95 0.94736842 0.92682927 0.9047619 0.9 - 0.95 0.95 0.95454545 1. ] - -mean value: 0.9388266953414579 - -key: train_fscore -value: [1. 1. 1. 0.99731903 1. 0.99731903 - 1. 0.99730458 1. 1. ] - -mean value: 0.9991942651915335 - -key: test_precision -value: [0.9047619 1. 1. 0.9047619 0.86363636 0.9 - 1. 1. 0.91304348 1. ] - -mean value: 0.9486203651421043 - -key: train_precision -value: [1. 1. 1. 0.99465241 1. 0.99465241 - 1. 0.99462366 1. 1. ] - -mean value: 0.9983928468748203 - -key: test_recall -value: [0.9047619 0.9047619 0.9 0.95 0.95 0.9 0.9047619 - 0.9047619 1. 1. ] - -mean value: 0.931904761904762 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9047619 0.95238095 0.95121951 0.92682927 0.90243902 0.90243902 - 0.95121951 0.95121951 0.95121951 1. ] - -mean value: 0.9393728222996515 - -key: train_accuracy -value: [1. 1. 1. 0.99730458 1. 0.99730458 - 1. 0.99730458 1. 1. ] - -mean value: 0.9991913746630727 - -key: test_roc_auc -value: [0.9047619 0.95238095 0.95 0.92738095 0.90357143 0.90238095 - 0.95238095 0.95238095 0.95 1. ] - -mean value: 0.9395238095238094 - -key: train_roc_auc -value: [1. 1. 1. 0.9972973 1. 0.9972973 - 1. 0.99731183 1. 1. ] - -mean value: 0.9991906422551583 - -key: test_jcc -value: [0.82608696 0.9047619 0.9 0.86363636 0.82608696 0.81818182 - 0.9047619 0.9047619 0.91304348 1. ] - -mean value: 0.8861321287408244 - -key: train_jcc -value: [1. 1. 1. 0.99465241 1. 0.99465241 - 1. 0.99462366 1. 1. ] - -mean value: 0.9983928468748203 - -key: TN -value: 195 - -mean value: 195.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 192 - -mean value: 192.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.8 - -Accuracy on Blind test: 0.91 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.02328062 0.01698422 0.01714063 0.01603866 0.01591897 0.01552367 - 0.01692295 0.0148356 0.01414895 0.01521873] - -mean value: 0.016601300239562987 - -key: score_time -value: [0.01345897 0.00929999 0.00878119 0.00894523 0.00869012 0.00870967 - 0.00864792 0.00867152 0.00922465 0.00865269] - -mean value: 0.009308195114135743 - -key: test_mcc -value: [0.95346259 0.90889326 1. 1. 0.95238095 0.90238095 - 0.90692382 0.90692382 0.95227002 0.90692382] - -mean value: 0.9390159240291451 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97560976 0.95 1. 1. 0.97560976 0.95 - 0.95 0.95 0.97674419 0.95 ] - -mean value: 0.9677963698241634 - -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. 1. 0.95238095 0.95 - 1. 1. 0.95454545 1. ] - -mean value: 0.9856926406926407 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 0.9047619 1. 1. 1. 0.95 - 0.9047619 0.9047619 1. 0.9047619 ] - -mean value: 0.9521428571428572 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97619048 0.95238095 1. 1. 0.97560976 0.95121951 - 0.95121951 0.95121951 0.97560976 0.95121951] - -mean value: 0.9684668989547036 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97619048 0.95238095 1. 1. 0.97619048 0.95119048 - 0.95238095 0.95238095 0.975 0.95238095] - -mean value: 0.9688095238095238 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95238095 0.9047619 1. 1. 0.95238095 0.9047619 - 0.9047619 0.9047619 0.95454545 0.9047619 ] - -mean value: 0.9383116883116884 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 203 - -mean value: 203.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 196 - -mean value: 196.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.10815501 0.10895181 0.1093998 0.10929322 0.10891581 0.10842085 - 0.10859251 0.10859179 0.1095922 0.10897279] - -mean value: 0.10888857841491699 - -key: score_time -value: [0.01763892 0.01770115 0.01765752 0.01774645 0.01755905 0.01760721 - 0.01772714 0.01766253 0.0177381 0.01773667] - -mean value: 0.01767747402191162 - -key: test_mcc -value: [0.81322028 0.90889326 0.7565654 1. 0.7098505 0.8047619 - 0.90238095 0.8547619 0.80817439 0.8213423 ] - -mean value: 0.8379950902937058 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.95 0.87179487 1. 0.84210526 0.9 - 0.95238095 0.92682927 0.90909091 0.89473684] - -mean value: 0.9156029015913483 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.86956522 1. 0.89473684 1. 0.88888889 0.9 - 0.95238095 0.95 0.86956522 1. ] - -mean value: 0.9325137118157713 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 0.9047619 0.85 1. 0.8 0.9 - 0.95238095 0.9047619 0.95238095 0.80952381] - -mean value: 0.9026190476190477 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9047619 0.95238095 0.87804878 1. 0.85365854 0.90243902 - 0.95121951 0.92682927 0.90243902 0.90243902] - -mean value: 0.9174216027874564 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9047619 0.95238095 0.87738095 1. 0.85238095 0.90238095 - 0.95119048 0.92738095 0.90119048 0.9047619 ] - -mean value: 0.9173809523809524 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.9047619 0.77272727 1. 0.72727273 0.81818182 - 0.90909091 0.86363636 0.83333333 0.80952381] - -mean value: 0.8471861471861472 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 192 - -mean value: 192.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 186 - -mean value: 186.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.54 - -Accuracy on Blind test: 0.8 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.00962472 0.00962687 0.00960588 0.00952983 0.00948834 0.00954628 - 0.00967455 0.0095439 0.00956202 0.00961089] - -mean value: 0.009581327438354492 - -key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -value: [0.00869465 0.00862598 0.008672 0.00865126 0.00859308 0.00861502 - 0.00867414 0.00862432 0.00860453 0.00868845] - -mean value: 0.008644342422485352 - -key: test_mcc -value: [0.82462113 0.68640647 0.44466675 0.80907152 0.80817439 0.37309549 - 0.66668392 0.6133669 0.71121921 0.72229808] - -mean value: 0.6659603856937134 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.89473684 0.81081081 0.625 0.9047619 0.89473684 0.62857143 - 0.82051282 0.8 0.85 0.84210526] - -mean value: 0.8071235912025386 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.9375 0.83333333 0.86363636 0.94444444 0.73333333 - 0.88888889 0.84210526 0.89473684 0.94117647] - -mean value: 0.8879154939487757 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.80952381 0.71428571 0.5 0.95 0.85 0.55 - 0.76190476 0.76190476 0.80952381 0.76190476] - -mean value: 0.7469047619047618 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9047619 0.83333333 0.70731707 0.90243902 0.90243902 0.68292683 - 0.82926829 0.80487805 0.85365854 0.85365854] - -mean value: 0.8274680603948896 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9047619 0.83333333 0.70238095 0.90357143 0.90119048 0.6797619 - 0.83095238 0.80595238 0.8547619 0.85595238] - -mean value: 0.8272619047619048 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.80952381 0.68181818 0.45454545 0.82608696 0.80952381 0.45833333 - 0.69565217 0.66666667 0.73913043 0.72727273] - -mean value: 0.6868553547901375 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 187 - -mean value: 187.0 - -key: FP -value: 52 - -mean value: 52.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 154 - -mean value: 154.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.41 - -Accuracy on Blind test: 0.73 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.52321553 1.51518893 1.51075912 1.50979471 1.50419736 1.51972842 - 1.55826187 1.5144372 1.5101018 1.51376414] - -mean value: 1.51794490814209 - -key: score_time -value: [0.09260964 0.09168124 0.09199357 0.09185886 0.09356117 0.09131193 - 0.09143853 0.09412074 0.09189868 0.09186673] - -mean value: 0.09223411083221436 - -key: test_mcc -value: [0.81322028 1. 0.8047619 1. 0.80907152 0.8547619 - 0.95238095 0.95227002 0.90649828 1. ] - -mean value: 0.9092964857581508 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 1. 0.9 1. 0.9047619 0.92682927 - 0.97560976 0.97674419 0.95454545 1. ] - -mean value: 0.9547581478835024 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.86956522 1. 0.9 1. 0.86363636 0.9047619 - 1. 0.95454545 0.91304348 1. ] - -mean value: 0.9405552418595896 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 1. 0.9 1. 0.95 0.95 - 0.95238095 1. 1. 1. ] - -mean value: 0.9704761904761904 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9047619 1. 0.90243902 1. 0.90243902 0.92682927 - 0.97560976 0.97560976 0.95121951 1. ] - -mean value: 0.9538908246225318 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9047619 1. 0.90238095 1. 0.90357143 0.92738095 - 0.97619048 0.975 0.95 1. ] - -mean value: 0.9539285714285712 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 1. 0.81818182 1. 0.82608696 0.86363636 - 0.95238095 0.95454545 0.91304348 1. ] - -mean value: 0.9161208356860531 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 193 - -mean value: 193.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 200 - -mean value: 200.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.94 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.89944077 0.96595263 0.91089082 0.95778012 0.93107462 1.00435877 - 0.92148137 0.93732476 0.98079419 0.89986396] - -mean value: 0.9408962011337281 - -key: score_time -value: [0.22168422 0.22878671 0.21265125 0.23230767 0.20985246 0.24188519 - 0.21738148 0.22345281 0.21452069 0.20086122] - -mean value: 0.22033836841583251 - -key: test_mcc -value: [0.76980036 0.95346259 0.65952381 0.95238095 0.76500781 0.8047619 - 0.95238095 0.95227002 0.86240942 0.95227002] - -mean value: 0.8624267820275844 - -key: train_mcc -value: [0.98391316 0.98391316 0.97339739 0.97866283 0.98395537 0.98927544 - 0.98927606 0.98395676 0.98395676 0.978494 ] - -mean value: 0.9828800926027311 - -key: test_fscore -value: [0.88888889 0.97560976 0.82926829 0.97560976 0.88372093 0.9 - 0.97560976 0.97674419 0.93333333 0.97674419] - -mean value: 0.9315529085523414 - -key: train_fscore -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( -[0.9919571 0.9919571 0.9867374 0.9893617 0.992 0.99465241 - 0.99462366 0.9919571 0.9919571 0.98924731] - -mean value: 0.9914450895047775 - -key: test_precision -value: [0.83333333 1. 0.80952381 0.95238095 0.82608696 0.9 - 1. 0.95454545 0.875 0.95454545] - -mean value: 0.9105415960850743 - -key: train_precision -value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617 - 0.98930481 0.98404255 0.98404255 0.98395722] - -mean value: 0.983569028905601 - -key: test_recall -value: [0.95238095 0.95238095 0.85 1. 0.95 0.9 - 0.95238095 1. 1. 1. ] - -mean value: 0.9557142857142857 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 1. 1. 0.99459459] - -mean value: 0.9994594594594595 - -key: test_accuracy -value: [0.88095238 0.97619048 0.82926829 0.97560976 0.87804878 0.90243902 - 0.97560976 0.97560976 0.92682927 0.97560976] - -mean value: 0.9296167247386758 - -key: train_accuracy -value: [0.99189189 0.99189189 0.98652291 0.98921833 0.99191375 0.99460916 - 0.99460916 0.99191375 0.99191375 0.98921833] - -mean value: 0.991370292125009 - -key: test_roc_auc -value: [0.88095238 0.97619048 0.8297619 0.97619048 0.8797619 0.90238095 - 0.97619048 0.975 0.925 0.975 ] - -mean value: 0.9296428571428571 - -key: train_roc_auc -value: [0.99189189 0.99189189 0.98648649 0.98918919 0.99189189 0.99459459 - 0.99462366 0.99193548 0.99193548 0.98923278] - -mean value: 0.9913673350770125 - -key: test_jcc -value: [0.8 0.95238095 0.70833333 0.95238095 0.79166667 0.81818182 - 0.95238095 0.95454545 0.875 0.95454545] - -mean value: 0.8759415584415585 - -key: train_jcc -value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617 - 0.98930481 0.98404255 0.98404255 0.9787234 ] - -mean value: 0.9830456474059993 - -key: TN -value: 186 - -mean value: 186.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.9 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.07526803 0.05560231 0.05945444 0.09954143 0.0577004 0.06429505 - 0.05793118 0.05943251 0.05828786 0.0576067 ] - -mean value: 0.06451199054718018 - -key: score_time -value: [0.01105881 0.01142812 0.01062226 0.01171064 0.01137996 0.01111913 - 0.01055002 0.01063538 0.01095271 0.01062679] - -mean value: 0.011008381843566895 - -key: test_mcc -value: [0.95346259 0.95346259 0.95227002 1. 0.95238095 1. - 0.95238095 0.95238095 0.95227002 1. ] - -mean value: 0.9668608066001669 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97560976 0.97560976 0.97435897 1. 0.97560976 1. - 0.97560976 0.97560976 0.97674419 1. ] - -mean value: 0.9829151940893291 - -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. 1. 0.95238095 1. - 1. 1. 0.95454545 1. ] - -mean value: 0.9906926406926407 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 0.95238095 0.95 1. 1. 1. - 0.95238095 0.95238095 1. 1. ] - -mean value: 0.9759523809523809 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97619048 0.97619048 0.97560976 1. 0.97560976 1. - 0.97560976 0.97560976 0.97560976 1. ] - -mean value: 0.9830429732868758 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97619048 0.97619048 0.975 1. 0.97619048 1. - 0.97619048 0.97619048 0.975 1. ] - -mean value: 0.983095238095238 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95238095 0.95238095 0.95 1. 0.95238095 1. - 0.95238095 0.95238095 0.95454545 1. ] - -mean value: 0.9666450216450215 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 204 - -mean value: 204.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 2 - -mean value: 2.0 - -key: TP -value: 201 - -mean value: 201.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03443933 0.05179811 0.06322622 0.05100846 0.06835747 0.07097554 - 0.07047963 0.03397155 0.03488135 0.03616333] - -mean value: 0.051530098915100096 - -key: score_time -value: [0.02279735 0.0122478 0.01230407 0.02061558 0.02173162 0.0191772 - 0.01230192 0.01241112 0.01224446 0.01825356] - -mean value: 0.016408467292785646 - -key: test_mcc -value: [0.85811633 0.8660254 0.86240942 1. 0.90692382 0.80817439 - 0.80907152 0.86333169 0.8547619 0.8547619 ] - -mean value: 0.8683576386302903 - -key: train_mcc -value: [0.97837838 0.98379816 0.98384144 0.98384144 0.98384144 0.98384144 - 0.9784365 0.9784365 0.98921825 0.9784365 ] - -mean value: 0.9822070037330393 - -key: test_fscore -value: [0.92682927 0.92307692 0.91891892 1. 0.95238095 0.89473684 - 0.9 0.92307692 0.92682927 0.92682927] - -mean value: 0.9292678364437027 - -key: train_fscore -value: [0.98918919 0.99191375 0.9919571 0.9919571 0.9919571 0.9919571 - 0.98918919 0.98918919 0.99459459 0.98918919] - -mean value: 0.9911093516212641 - -key: test_precision -value: [0.95 1. 1. 1. 0.90909091 0.94444444 - 0.94736842 1. 0.95 0.95 ] - -mean value: 0.9650903774587984 - -key: train_precision -value: [0.98918919 0.98924731 0.98930481 0.98930481 0.98930481 0.98930481 - 0.98918919 0.98918919 0.99459459 0.98918919] - -mean value: 0.9897817914516207 - -key: test_recall -value: [0.9047619 0.85714286 0.85 1. 1. 0.85 - 0.85714286 0.85714286 0.9047619 0.9047619 ] - -mean value: 0.8985714285714286 - -key: train_recall -value: [0.98918919 0.99459459 0.99462366 0.99462366 0.99462366 0.99462366 - 0.98918919 0.98918919 0.99459459 0.98918919] - -mean value: 0.992444056960186 - -key: test_accuracy -value: [0.92857143 0.92857143 0.92682927 1. 0.95121951 0.90243902 - 0.90243902 0.92682927 0.92682927 0.92682927] - -mean value: 0.9320557491289201 - -key: train_accuracy -value: [0.98918919 0.99189189 0.99191375 0.99191375 0.99191375 0.99191375 - 0.98921833 0.98921833 0.99460916 0.98921833] - -mean value: 0.9911000218547388 - -key: test_roc_auc -value: [0.92857143 0.92857143 0.925 1. 0.95238095 0.90119048 - 0.90357143 0.92857143 0.92738095 0.92738095] - -mean value: 0.9322619047619047 - -key: train_roc_auc -value: [0.98918919 0.99189189 0.99190642 0.99190642 0.99190642 0.99190642 - 0.98921825 0.98921825 0.99460913 0.98921825] - -mean value: 0.9910970648067421 - -key: test_jcc -value: [0.86363636 0.85714286 0.85 1. 0.90909091 0.80952381 - 0.81818182 0.85714286 0.86363636 0.86363636] - -mean value: 0.8691991341991342 - -key: train_jcc -value: [0.97860963 0.98395722 0.98404255 0.98404255 0.98404255 0.98404255 - 0.97860963 0.97860963 0.98924731 0.97860963] - -mean value: 0.9823813246519049 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 185 - -mean value: 185.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.86 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.0135808 0.01309872 0.00985193 0.00955606 0.00936365 0.01058984 - 0.00950861 0.00956726 0.00948119 0.00925517] - -mean value: 0.01038532257080078 - -key: score_time -value: [0.01231837 0.0104692 0.00897026 0.00873733 0.00892115 0.00917673 - 0.00902939 0.00907087 0.00861287 0.00889134] - -mean value: 0.009419751167297364 - -key: test_mcc -value: [0.28603878 0.43052839 0.08051176 0.41428571 0.17142857 0.31666667 - 0.37171226 0.47003614 0.41487884 0.46300848] - -mean value: 0.3419095594012099 - -key: train_mcc -value: [0.44330153 0.36757294 0.3857278 0.39622203 0.41262884 0.41778553 - 0.37478201 0.40162743 0.4503185 0.37478201] - -mean value: 0.40247486105604446 - -key: test_fscore -value: [0.63414634 0.72727273 0.57777778 0.7 0.58536585 0.65 - 0.66666667 0.71794872 0.72727273 0.74418605] - -mean value: 0.6730636858572197 - -key: train_fscore -value: [0.72386059 0.68463612 0.7 0.69892473 0.71240106 0.70967742 - 0.68983957 0.70080863 0.71978022 0.68983957] - -mean value: 0.7029767903859494 - -key: test_precision -value: [0.65 0.69565217 0.52 0.7 0.57142857 0.65 - 0.72222222 0.77777778 0.69565217 0.72727273] - -mean value: 0.6710005646527386 - -key: train_precision -value: [0.71808511 0.6827957 0.68556701 0.69892473 0.69948187 0.70967742 - 0.68253968 0.69892473 0.73184358 0.68253968] - -mean value: 0.6990379503120753 - -key: test_recall -value: [0.61904762 0.76190476 0.65 0.7 0.6 0.65 - 0.61904762 0.66666667 0.76190476 0.76190476] - -mean value: 0.679047619047619 - -key: train_recall -value: [0.72972973 0.68648649 0.71505376 0.69892473 0.72580645 0.70967742 - 0.6972973 0.7027027 0.70810811 0.6972973 ] - -mean value: 0.7071083987213018 - -key: test_accuracy -value: [0.64285714 0.71428571 0.53658537 0.70731707 0.58536585 0.65853659 - 0.68292683 0.73170732 0.70731707 0.73170732] - -mean value: 0.6698606271777002 - -key: train_accuracy -value: [0.72162162 0.68378378 0.69272237 0.69811321 0.70619946 0.70889488 - 0.68733154 0.70080863 0.72506739 0.68733154] - -mean value: 0.7011874408100823 - -key: test_roc_auc -value: [0.64285714 0.71428571 0.53928571 0.70714286 0.58571429 0.65833333 - 0.68452381 0.73333333 0.70595238 0.73095238] - -mean value: 0.6702380952380953 - -key: train_roc_auc -value: [0.72162162 0.68378378 0.69266202 0.69811101 0.70614647 0.70889276 - 0.68735833 0.70081372 0.7250218 0.68735833] - -mean value: 0.701176983435048 - -key: test_jcc -value: [0.46428571 0.57142857 0.40625 0.53846154 0.4137931 0.48148148 - 0.5 0.56 0.57142857 0.59259259] - -mean value: 0.5099721573126745 - -key: train_jcc -value: [0.56722689 0.5204918 0.53846154 0.53719008 0.55327869 0.55 - 0.52653061 0.53941909 0.56223176 0.52653061] - -mean value: 0.5421361074949125 - -key: TN -value: 136 - -mean value: 136.0 - -key: FP -value: 66 - -mean value: 66.0 - -key: FN -value: 70 - -mean value: 70.0 - -key: TP -value: 140 - -mean value: 140.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.68 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01755261 0.0244925 0.02322912 0.02689171 0.02338552 0.02113056 - 0.02441788 0.02576876 0.02323055 0.02049685] - -mean value: 0.023059606552124023 - -key: score_time -value: [0.00870085 0.01136637 0.01221657 0.01218843 0.01224065 0.01185799 - 0.01182604 0.0121398 0.01182389 0.01190352] - -mean value: 0.011626410484313964 - -key: test_mcc -value: [0.81322028 1. 0.90649828 1. 0.95238095 0.8547619 - 0.90692382 1. 0.8547619 1. ] - -mean value: 0.9288547149722348 - -key: train_mcc -value: [0.95765257 0.98379816 0.9784365 0.97866283 0.9946235 0.97306016 - 0.98384144 0.97866529 0.9681586 0.98384191] - -mean value: 0.9780740950182457 - -key: test_fscore -value: [0.90909091 1. 0.94736842 1. 0.97560976 0.92682927 - 0.95 1. 0.92682927 1. ] - -mean value: 0.9635727622826469 - -key: train_fscore -value: [0.97883598 0.99191375 0.98924731 0.9893617 0.99731903 0.98652291 - 0.99186992 0.98930481 0.98351648 0.99191375] - -mean value: 0.9889805647006705 - -key: test_precision -value: [0.86956522 1. 1. 1. 0.95238095 0.9047619 - 1. 1. 0.95 1. ] - -mean value: 0.9676708074534162 - -key: train_precision -value: [0.95854922 0.98924731 0.98924731 0.97894737 0.99465241 0.98918919 - 0.99456522 0.97883598 1. 0.98924731] - -mean value: 0.9862481318536436 - -key: test_recall -value: [0.95238095 1. 0.9 1. 1. 0.95 - 0.9047619 1. 0.9047619 1. ] - -mean value: 0.961190476190476 - -key: train_recall -value: [1. 0.99459459 0.98924731 1. 1. 0.98387097 - 0.98918919 1. 0.96756757 0.99459459] - -mean value: 0.9919064225515839 - -key: test_accuracy -value: [0.9047619 1. 0.95121951 1. 0.97560976 0.92682927 - 0.95121951 1. 0.92682927 1. ] - -mean value: 0.9636469221835077 - -key: train_accuracy -value: [0.97837838 0.99189189 0.98921833 0.98921833 0.99730458 0.98652291 - 0.99191375 0.98921833 0.98382749 0.99191375] - -mean value: 0.9889407736577548 - -key: test_roc_auc -value: [0.9047619 1. 0.95 1. 0.97619048 0.92738095 - 0.95238095 1. 0.92738095 1. ] - -mean value: 0.9638095238095238 - -key: train_roc_auc -value: [0.97837838 0.99189189 0.98921825 0.98918919 0.9972973 0.98653008 - 0.99190642 0.98924731 0.98378378 0.99192095] - -mean value: 0.9889363557105494 - -key: test_jcc -value: [0.83333333 1. 0.9 1. 0.95238095 0.86363636 - 0.9047619 1. 0.86363636 1. ] - -mean value: 0.9317748917748917 - -key: train_jcc -value: [0.95854922 0.98395722 0.9787234 0.97894737 0.99465241 0.97340426 - 0.98387097 0.97883598 0.96756757 0.98395722] - -mean value: 0.9782465609858717 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 198 - -mean value: 198.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.82 - -Accuracy on Blind test: 0.92 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01616383 0.01703405 0.01742053 0.01710224 0.01646566 0.01644945 - 0.0160799 0.01625156 0.01585746 0.01778984] - -mean value: 0.016661453247070312 - -key: score_time -value: [0.01153302 0.01175213 0.0117445 0.01178885 0.0118649 0.0118494 - 0.01189089 0.01182055 0.01171756 0.01178432] - -mean value: 0.011774611473083497 - -key: test_mcc -value: [0.8660254 0.8660254 0.85441771 0.8547619 0.7633652 0.70272837 - 0.698212 0.86240942 0.85441771 1. ] - -mean value: 0.8322363111639266 - -key: train_mcc -value: [0.88528142 0.92567765 0.9681586 0.98927544 0.91221469 0.73643388 - 0.73681663 0.79883884 0.95160448 0.93156865] - -mean value: 0.8835870293159503 - -key: test_fscore -value: [0.92307692 0.92307692 0.92307692 0.92682927 0.86486486 0.85106383 - 0.85714286 0.93333333 0.93023256 1. ] - -mean value: 0.9132697480791275 - -key: train_fscore -value: [0.9375 0.96111111 0.98412698 0.99465241 0.95211268 0.87119438 - 0.87058824 0.90024331 0.97547684 0.96587927] - -mean value: 0.9412885205728119 - -key: test_precision -value: [1. 1. 0.94736842 0.9047619 0.94117647 0.74074074 - 0.75 0.875 0.90909091 1. ] - -mean value: 0.9068138446234422 - -key: train_precision -value: [0.98802395 0.98857143 0.96875 0.9893617 1. 0.77178423 - 0.77083333 0.81858407 0.98351648 0.93877551] - -mean value: 0.92182007130104 - -key: test_recall -value: [0.85714286 0.85714286 0.9 0.95 0.8 1. - 1. 1. 0.95238095 1. ] - -mean value: 0.9316666666666666 - -key: train_recall -value: [0.89189189 0.93513514 1. 1. 0.90860215 1. - 1. 1. 0.96756757 0.99459459] - -mean value: 0.9697791339726823 - -key: test_accuracy -value: [0.92857143 0.92857143 0.92682927 0.92682927 0.87804878 0.82926829 - 0.82926829 0.92682927 0.92682927 1. ] - -mean value: 0.9101045296167248 - -key: train_accuracy -value: [0.94054054 0.96216216 0.98382749 0.99460916 0.9541779 0.85175202 - 0.85175202 0.88948787 0.97574124 0.96495957] - -mean value: 0.9369009980330736 - -key: test_roc_auc -value: [0.92857143 0.92857143 0.92619048 0.92738095 0.87619048 0.83333333 - 0.825 0.925 0.92619048 1. ] - -mean value: 0.9096428571428572 - -key: train_roc_auc -value: [0.94054054 0.96216216 0.98378378 0.99459459 0.95430108 0.85135135 - 0.85215054 0.88978495 0.97571927 0.96503923] - -mean value: 0.9369427492008138 - -key: test_jcc -value: [0.85714286 0.85714286 0.85714286 0.86363636 0.76190476 0.74074074 - 0.75 0.875 0.86956522 1. ] - -mean value: 0.8432275655101742 - -key: train_jcc -value: [0.88235294 0.92513369 0.96875 0.9893617 0.90860215 0.77178423 - 0.77083333 0.81858407 0.95212766 0.93401015] - -mean value: 0.8921539932035006 - -key: TN -value: 183 - -mean value: 183.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 192 - -mean value: 192.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.81 - -Accuracy on Blind test: 0.92 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.16330028 0.14592481 0.14636064 0.14555335 0.1465106 0.14619303 - 0.14668226 0.14667153 0.14667702 0.14632678] - -mean value: 0.14802002906799316 - -key: score_time -value: [0.01503825 0.01503277 0.01506281 0.01544476 0.01512909 0.01512289 - 0.01505589 0.01523805 0.01561737 0.01514649] - -mean value: 0.015188837051391601 - -key: test_mcc -value: [0.95346259 0.95346259 1. 0.95238095 0.90692382 1. - 0.95238095 1. 0.95227002 1. ] - -mean value: 0.9670880922421489 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97560976 0.97560976 1. 0.97560976 0.95238095 1. - 0.97560976 1. 0.97674419 1. ] - -mean value: 0.9831564162817708 - -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. 0.95238095 0.90909091 1. - 1. 1. 0.95454545 1. ] - -mean value: 0.9816017316017316 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.95238095 0.95238095 1. 1. 1. 1. - 0.95238095 1. 1. 1. ] - -mean value: 0.9857142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97619048 0.97619048 1. 0.97560976 0.95121951 1. - 0.97560976 1. 0.97560976 1. ] - -mean value: 0.9830429732868758 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97619048 0.97619048 1. 0.97619048 0.95238095 1. - 0.97619048 1. 0.975 1. ] - -mean value: 0.9832142857142857 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95238095 0.95238095 1. 0.95238095 0.90909091 1. - 0.95238095 1. 0.95454545 1. ] - -mean value: 0.9673160173160174 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 202 - -mean value: 202.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 4 - -mean value: 4.0 - -key: TP -value: 203 - -mean value: 203.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.93 - -Accuracy on Blind test: 0.97 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.0419879 0.05815363 0.05153394 0.05133629 0.06185651 0.04853463 - 0.04930449 0.05603623 0.04445434 0.05075979] - -mean value: 0.05139577388763428 - -key: score_time -value: [0.02328753 0.02999187 0.02305889 0.02622795 0.02008605 0.01859808 - 0.0239203 0.03557754 0.02351022 0.02400208] - -mean value: 0.0248260498046875 - -key: test_mcc -value: [0.8660254 0.95346259 0.85441771 1. 0.8547619 0.95227002 - 0.95238095 0.90692382 0.90649828 0.90692382] - -mean value: 0.9153664500518636 - -key: train_mcc -value: [0.989247 1. 0.99462366 0.99462366 0.99462366 0.98921825 - 1. 1. 0.99462366 0.97866283] - -mean value: 0.9935622705535619 - -key: test_fscore -value: [0.92307692 0.97560976 0.92307692 1. 0.92682927 0.97435897 - 0.97560976 0.95 0.95454545 0.95 ] - -mean value: 0.9553107055546078 - -key: train_fscore -value: [0.99456522 1. 0.99730458 0.99730458 0.99730458 0.99462366 - 1. 1. 0.99730458 0.98907104] - -mean value: 0.9967478240397618 - -key: test_precision -value: [1. 1. 0.94736842 1. 0.9047619 1. - 1. 1. 0.91304348 1. ] - -mean value: 0.9765173804075407 - -key: train_precision -value: [1. 1. 1. 1. 1. 0.99462366 - 1. 1. 0.99462366 1. ] - -mean value: 0.9989247311827956 - -key: test_recall -value: [0.85714286 0.95238095 0.9 1. 0.95 0.95 - 0.95238095 0.9047619 1. 0.9047619 ] - -mean value: 0.9371428571428572 - -key: train_recall -value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.99462366 - 1. 1. 1. 0.97837838] - -mean value: 0.9946062191223481 - -key: test_accuracy -value: [0.92857143 0.97619048 0.92682927 1. 0.92682927 0.97560976 - 0.97560976 0.95121951 0.95121951 0.95121951] - -mean value: 0.9563298490127758 - -key: train_accuracy -value: [0.99459459 1. 0.99730458 0.99730458 0.99730458 0.99460916 - 1. 1. 0.99730458 0.98921833] - -mean value: 0.9967640416697019 - -key: test_roc_auc -value: [0.92857143 0.97619048 0.92619048 1. 0.92738095 0.975 - 0.97619048 0.95238095 0.95 0.95238095] - -mean value: 0.9564285714285713 - -key: train_roc_auc -value: [0.99459459 1. 0.99731183 0.99731183 0.99731183 0.99460913 - 1. 1. 0.99731183 0.98918919] - -mean value: 0.9967640220866028 - -key: test_jcc -value: [0.85714286 0.95238095 0.85714286 1. 0.86363636 0.95 - 0.95238095 0.9047619 0.91304348 0.9047619 ] - -mean value: 0.9155251270468663 - -key: train_jcc -value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.98930481 - 1. 1. 0.99462366 0.97837838] - -mean value: 0.9935367004057707 - -key: TN -value: 201 - -mean value: 201.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 193 - -mean value: 193.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.09415078 0.11422873 0.14302111 0.11281705 0.11488152 0.11356688 - 0.11255336 0.11312151 0.11276913 0.12180877] - -mean value: 0.1152918815612793 - -key: score_time -value: [0.02256703 0.0243187 0.02233911 0.02280259 0.02246737 0.02313232 - 0.028368 0.02239347 0.02279854 0.02269793] - -mean value: 0.02338850498199463 - -key: test_mcc -value: [0.62187434 0.63059263 0.48849265 0.61152662 0.56527676 0.66432098 - 0.65871309 0.66668392 0.4373371 0.74124932] - -mean value: 0.6086067388065854 - -key: train_mcc -value: [0.90460775 0.92000825 0.92539732 0.90381625 0.93618785 0.91980011 - 0.90379406 0.92538015 0.90994228 0.9361732 ] - -mean value: 0.9185107229970159 - -key: test_fscore -value: [0.8 0.78947368 0.66666667 0.78947368 0.75675676 0.81081081 - 0.8372093 0.82051282 0.66666667 0.83333333] - -mean value: 0.7770903725493689 - -key: train_fscore -value: [0.94972067 0.95844875 0.96153846 0.95054945 0.96703297 0.95890411 - 0.95027624 0.96132597 0.95264624 0.96685083] - -mean value: 0.9577293690791937 - -key: test_precision -value: [0.84210526 0.88235294 0.84615385 0.83333333 0.82352941 0.88235294 - 0.81818182 0.88888889 0.8 1. ] - -mean value: 0.8616898443833427 - -key: train_precision -value: [0.98265896 0.98295455 0.98314607 0.97191011 0.98876404 0.97765363 - 0.97175141 0.98305085 0.98275862 0.98870056] - -mean value: 0.9813348806544546 - -key: test_recall -value: [0.76190476 0.71428571 0.55 0.75 0.7 0.75 - 0.85714286 0.76190476 0.57142857 0.71428571] - -mean value: 0.713095238095238 - -key: train_recall -value: [0.91891892 0.93513514 0.94086022 0.93010753 0.94623656 0.94086022 - 0.92972973 0.94054054 0.92432432 0.94594595] - -mean value: 0.9352659110723627 - -key: test_accuracy -value: [0.80952381 0.80952381 0.73170732 0.80487805 0.7804878 0.82926829 - 0.82926829 0.82926829 0.70731707 0.85365854] - -mean value: 0.7984901277584204 - -key: train_accuracy -value: [0.95135135 0.95945946 0.96226415 0.95148248 0.96765499 0.95956873 - 0.95148248 0.96226415 0.9541779 0.96765499] - -mean value: 0.9587360676039921 - -key: test_roc_auc -value: [0.80952381 0.80952381 0.72738095 0.80357143 0.77857143 0.82738095 - 0.82857143 0.83095238 0.71071429 0.85714286] - -mean value: 0.7983333333333335 - -key: train_roc_auc -value: [0.95135135 0.95945946 0.962322 0.95154025 0.96771287 0.9596193 - 0.951424 0.96220575 0.95409765 0.96759663] - -mean value: 0.958732926474862 - -key: test_jcc -value: [0.66666667 0.65217391 0.5 0.65217391 0.60869565 0.68181818 - 0.72 0.69565217 0.5 0.71428571] - -mean value: 0.6391466214944476 - -key: train_jcc -value: [0.90425532 0.92021277 0.92592593 0.90575916 0.93617021 0.92105263 - 0.90526316 0.92553191 0.90957447 0.93582888] - -mean value: 0.9189574435559686 - -key: TN -value: 182 - -mean value: 182.0 - -key: FP -value: 59 - -mean value: 59.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 147 - -mean value: 147.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.74 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.5280242 0.51709318 0.52253103 0.52089429 0.53239608 0.52510905 - 0.51605487 0.51583099 0.51282382 0.51904821] - -mean value: 0.5209805727005005 - -key: score_time -value: [0.00917411 0.00927401 0.00938559 0.00967669 0.00932646 0.00964832 - 0.00938344 0.0091908 0.00919986 0.00924921] - -mean value: 0.009350848197937012 - -key: test_mcc -value: [1. 0.95346259 1. 1. 0.95238095 1. - 0.95238095 0.95238095 0.95227002 1. ] - -mean value: 0.9762875461572262 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97560976 1. 1. 0.97560976 1. - 0.97560976 0.97560976 0.97674419 1. ] - -mean value: 0.9879183210436755 - -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. 1. 0.95238095 1. - 1. 1. 0.95454545 1. ] - -mean value: 0.9906926406926407 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.95238095 1. 1. 1. 1. - 0.95238095 0.95238095 1. 1. ] - -mean value: 0.9857142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97619048 1. 1. 0.97560976 1. - 0.97560976 0.97560976 0.97560976 1. ] - -mean value: 0.987862950058072 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97619048 1. 1. 0.97619048 1. - 0.97619048 0.97619048 0.975 1. ] - -mean value: 0.9879761904761905 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.95238095 1. 1. 0.95238095 1. - 0.95238095 0.95238095 0.95454545 1. ] - -mean value: 0.9764069264069264 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 204 - -mean value: 204.0 - -key: FP -value: 3 - -mean value: 3.0 - -key: FN -value: 2 - -mean value: 2.0 - -key: TP -value: 203 - -mean value: 203.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.91 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis() - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02632236 0.02660441 0.03398514 0.02744603 0.02748156 0.02773428 - 0.02761316 0.02803922 0.02759814 0.02734256] - -mean value: 0.02801668643951416 - -key: score_time -value: [0.01254487 0.01330376 0.01410699 0.01402807 0.01527119 0.01543665 - 0.01530933 0.01407933 0.01403427 0.01432133] - -mean value: 0.014243578910827637 - -key: test_mcc -value: [0.81322028 0.76980036 0.86333169 0.74124932 0.74124932 0.6133669 - 0.7565654 0.65871309 0.70714286 0.80907152] - -mean value: 0.7473710740902314 - -key: train_mcc -value: [0.93710863 1. 0.97866283 0.99462366 1. 0.96787795 - 0.94746155 0.99462366 0.98927544 0.98395537] - -mean value: 0.9793589089718562 - -key: test_fscore -value: [0.90909091 0.88888889 0.93023256 0.86956522 0.86956522 0.80952381 - 0.88372093 0.8372093 0.85714286 0.9 ] - -mean value: 0.8754939690126747 - -key: train_fscore -value: [0.96648045 1. 0.9893617 0.99730458 1. 0.98404255 - 0.97222222 0.99730458 0.99456522 0.99182561] - -mean value: 0.9893106919359556 - -key: test_precision -value: [0.86956522 0.83333333 0.86956522 0.76923077 0.76923077 0.77272727 - 0.86363636 0.81818182 0.85714286 0.94736842] - -mean value: 0.8369982039318424 - -key: train_precision -value: [1. 1. 0.97894737 1. 1. 0.97368421 - 1. 0.99462366 1. 1. ] - -mean value: 0.9947255234861346 - -key: test_recall -value: [0.95238095 0.95238095 1. 1. 1. 0.85 - 0.9047619 0.85714286 0.85714286 0.85714286] - -mean value: 0.9230952380952381 - -key: train_recall -value: [0.93513514 1. 1. 0.99462366 1. 0.99462366 - 0.94594595 1. 0.98918919 0.98378378] - -mean value: 0.9843301365882011 - -key: test_accuracy -value: [0.9047619 0.88095238 0.92682927 0.85365854 0.85365854 0.80487805 - 0.87804878 0.82926829 0.85365854 0.90243902] - -mean value: 0.868815331010453 - -key: train_accuracy -value: [0.96756757 1. 0.98921833 0.99730458 1. 0.98382749 - 0.97304582 0.99730458 0.99460916 0.99191375] - -mean value: 0.9894791287244116 - -key: test_roc_auc -value: [0.9047619 0.88095238 0.92857143 0.85714286 0.85714286 0.80595238 - 0.87738095 0.82857143 0.85357143 0.90357143] - -mean value: 0.8697619047619047 - -key: train_roc_auc -value: [0.96756757 1. 0.98918919 0.99731183 1. 0.98379831 - 0.97297297 0.99731183 0.99459459 0.99189189] - -mean value: 0.9894638186573671 - -key: test_jcc -value: [0.83333333 0.8 0.86956522 0.76923077 0.76923077 0.68 - 0.79166667 0.72 0.75 0.81818182] - -mean value: 0.780120857403466 - -key: train_jcc -value: [0.93513514 1. 0.97894737 0.99462366 1. 0.96858639 - 0.94594595 0.99462366 0.98918919 0.98378378] - -mean value: 0.9790835121737619 - -key: TN -value: 169 - -mean value: 169.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 37 - -mean value: 37.0 - -key: TP -value: 189 - -mean value: 189.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.7 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('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: [0.01563764 0.01484275 0.01484203 0.01481032 0.02531528 0.03736687 - 0.03732777 0.03908205 0.03567171 0.05811954] - -mean value: 0.02930159568786621 - -key: score_time -value: [0.01234174 0.01208162 0.01213408 0.01210332 0.02343178 0.02073622 - 0.02129221 0.0238409 0.02119112 0.02207351] - -mean value: 0.018122649192810057 - -key: test_mcc -value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095 - 0.90692382 1. 0.90649828 1. ] - -mean value: 0.9250201911273045 - -key: train_mcc -value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194 - 0.9784365 0.97305937 0.97305937 0.96238811] - -mean value: 0.9676667677298388 - -key: test_fscore -value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976 - 0.95 1. 0.95454545 1. ] - -mean value: 0.9605429935917741 - -key: train_fscore -value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378 - 0.98918919 0.98644986 0.98644986 0.98092643] - -mean value: 0.9837776750546631 - -key: test_precision -value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095 - 1. 1. 0.91304348 1. ] - -mean value: 0.9674264713166314 - -key: train_precision -value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043 - 0.98918919 0.98913043 0.98913043 0.98901099] - -mean value: 0.9864566944686158 - -key: test_recall -value: [0.95238095 0.85714286 0.9 0.95 1. 1. - 0.9047619 1. 1. 1. ] - -mean value: 0.9564285714285713 - -key: train_recall -value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462 - 0.98918919 0.98378378 0.98378378 0.97297297] - -mean value: 0.9811275791920954 - -key: test_accuracy -value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976 - 0.95121951 1. 0.95121951 1. ] - -mean value: 0.9612659698025551 - -key: train_accuracy -value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749 - 0.98921833 0.98652291 0.98652291 0.98113208] - -mean value: 0.9838194798572157 - -key: test_roc_auc -value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048 - 0.95238095 1. 0.95 1. ] - -mean value: 0.9613095238095237 - -key: train_roc_auc -value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191 - 0.98921825 0.98651555 0.98651555 0.98111014] - -mean value: 0.9838186573670447 - -key: test_jcc -value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095 - 0.9047619 1. 0.91304348 1. ] - -mean value: 0.9252653867871258 - -key: train_jcc -value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511 - 0.97860963 0.97326203 0.97326203 0.96256684] - -mean value: 0.9680953442378909 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: 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 - rouC_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: 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 - rouC_CV['Resampling'] = rs_rouC -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: 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 - rouC_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: 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 - rouC_BT['Resampling'] = rs_rouC -blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=168)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.32478094 0.20739317 0.20109296 0.32834768 0.2527113 0.1824615 - 0.26740456 0.40533066 0.39677286 0.23047733] - -mean value: 0.2796772956848145 - -key: score_time -value: [0.01243234 0.01315236 0.02611136 0.01238418 0.01246881 0.01612306 - 0.02184033 0.02457547 0.01257014 0.01241994] - -mean value: 0.01640779972076416 - -key: test_mcc -value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095 - 0.90692382 1. 0.90649828 1. ] - -mean value: 0.9250201911273045 - -key: train_mcc -value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194 - 0.9784365 0.97305937 0.97305937 0.96238811] - -mean value: 0.9676667677298388 - -key: test_fscore -value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976 - 0.95 1. 0.95454545 1. ] - -mean value: 0.9605429935917741 - -key: train_fscore -value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378 - 0.98918919 0.98644986 0.98644986 0.98092643] - -mean value: 0.9837776750546631 - -key: test_precision -value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095 - 1. 1. 0.91304348 1. ] - -mean value: 0.9674264713166314 - -key: train_precision -value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043 - 0.98918919 0.98913043 0.98913043 0.98901099] - -mean value: 0.9864566944686158 - -key: test_recall -value: [0.95238095 0.85714286 0.9 0.95 1. 1. - 0.9047619 1. 1. 1. ] - -mean value: 0.9564285714285713 - -key: train_recall -value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462 - 0.98918919 0.98378378 0.98378378 0.97297297] - -mean value: 0.9811275791920954 - -key: test_accuracy -value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976 - 0.95121951 1. 0.95121951 1. ] - -mean value: 0.9612659698025551 - -key: train_accuracy -value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749 - 0.98921833 0.98652291 0.98652291 0.98113208] - -mean value: 0.9838194798572157 - -key: test_roc_auc -value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048 - 0.95238095 1. 0.95 1. ] - -mean value: 0.9613095238095237 - -key: train_roc_auc -value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191 - 0.98921825 0.98651555 0.98651555 0.98111014] - -mean value: 0.9838186573670447 - -key: test_jcc -value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095 - 0.9047619 1. 0.91304348 1. ] - -mean value: 0.9252653867871258 - -key: train_jcc -value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511 - 0.97860963 0.97326203 0.97326203 0.96256684] - -mean value: 0.9680953442378909 - -key: TN -value: 199 - -mean value: 199.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 197 - -mean value: 197.0 - -key: trainingY_neg -value: 206 - -mean value: 206.0 - -key: trainingY_pos -value: 206 - -mean value: 206.0 - -key: blindY_neg -value: 52 - -mean value: 52.0 - -key: blindY_pos -value: 103 - -mean value: 103.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -PASS: sorting df by score that is mapped onto the order I want - -Concatenating dfs with different resampling methods [WF]: 70/30 -No. of dfs combining: 10 -The sampling methods are: - Resampling -Logistic Regression none -Logistic Regression smnc -Logistic Regression ros -Logistic Regression rus -Logistic Regression rouC - -PASS: 10 dfs successfully combined -nrows in combined_df_wf: 240 -ncols in combined_df_wf: 9 - -Concatenating dfs with different resampling methods: 70/30 -No. of dfs combining: 5 -The sampling methods are: - Resampling training_size -0 none 312 -24 smnc 412 -48 ros 412 -72 rus 212 -96 rouC 412 - -PASS: 5 dfs successfully combined -nrows in combined_df: 120 -ncols in combined_df: 32 - -File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_7030.csv - -File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_ext_7030.csv diff --git a/scripts/ml/log_pnca_7030.txt b/scripts/ml/log_pnca_7030.txt deleted file mode 100644 index e346572..0000000 --- a/scripts/ml/log_pnca_7030.txt +++ /dev/null @@ -1,25034 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -/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/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -1.22.4 -1.4.1 - -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: 265 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 172 - -PASS: ML data with input features, training and test generated... - -Total no. of input features: 172 ---------No. of numerical features: 166 ---------No. of categorical features: 6 - -Total no. of evolutionary features: 3 - -Total no. of stability features: 28 ---------Common stabilty cols: 5 ---------Foldx cols: 23 - -Total no. of affinity features: 3 ---------Common affinity cols: 3 ---------Gene specific affinity cols: 0 - -Total no. of residue level features: 132 ---------AA index cols: 123 ---------Residue Prop cols: 3 ---------AA change Prop cols: 6 - -Total no. of genomic features: 6 ---------MAF+OR cols: 2 ---------Lineage cols: 4 ---------Other cols: 0 - -------------------------------------------------------------- -Successfully split data: ALL features -actual values: training set -imputed values: blind test set - -Total data size: 185 - -Train data size: (123, 172) -y_train numbers: Counter({1: 76, 0: 47}) - -Test data size: (62, 172) -y_test_numbers: Counter({1: 38, 0: 24}) - -y_train ratio: 0.618421052631579 -y_test ratio: 0.631578947368421 -------------------------------------------------------------- - -index: 0 -ind: 1 - -Mask count check: True -Original Data - Counter({1: 76, 0: 47}) Data dim: (123, 172) -Simple Random OverSampling - Counter({0: 76, 1: 76}) -(152, 172) -Simple Random UnderSampling - Counter({0: 47, 1: 47}) -(94, 172) -Simple Combined Over and UnderSampling - Counter({0: 76, 1: 76}) -(152, 172) - -SMOTE_NC OverSampling - Counter({0: 76, 1: 76}) -(152, 172) - -##################################################################### - -Running ML analysis: feature groups -Gene name: pncA -Drug name: pyrazinamide - -Output directory: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/ - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -key: fit_time -value: [0.02845812 0.03014469 0.02878141 0.0267849 0.02630472 0.02681899 - 0.02665186 0.06223321 0.04837632 0.02695227] - -mean value: 0.033150649070739745 - -key: score_time -value: [0.01202369 0.0115416 0.01154542 0.01147985 0.01147604 0.01139736 - 0.0114212 0.01349187 0.012362 0.01165318] - -mean value: 0.01183922290802002 - -key: test_mcc -value: [ 0.35 0.50069396 -0.05773503 0.29277002 0.16903085 -0.09759001 - 0.29277002 0.40824829 0.25 0.47809144] - -mean value: 0.25862795575586206 - -key: train_mcc -value: [0.8456883 0.84516165 0.84761434 0.82751593 0.86547483 0.84833359 - 0.86551251 0.81075196 0.84998189 0.88578234] - -mean value: 0.849181735147331 - -key: test_fscore -value: [0.75 0.82352941 0.66666667 0.75 0.61538462 0.625 - 0.75 0.82352941 0.75 0.8 ] - -mean value: 0.7354110105580693 - -key: train_fscore -value: [0.94285714 0.94202899 0.94366197 0.93706294 0.94964029 0.94444444 - 0.95035461 0.92957746 0.94366197 0.95652174] - -mean value: 0.9439811555151773 - -key: test_precision -value: [0.75 0.77777778 0.6 0.66666667 0.66666667 0.55555556 - 0.66666667 0.77777778 0.75 0.85714286] - -mean value: 0.7068253968253968 - -key: train_precision -value: [0.91666667 0.92857143 0.90540541 0.90540541 0.94285714 0.90666667 - 0.93055556 0.89189189 0.90540541 0.94285714] - -mean value: 0.9176282711282712 - -key: test_recall -value: [0.75 0.875 0.75 0.85714286 0.57142857 0.71428571 - 0.85714286 0.875 0.75 0.75 ] - -mean value: 0.775 - -key: train_recall -value: [0.97058824 0.95588235 0.98529412 0.97101449 0.95652174 0.98550725 - 0.97101449 0.97058824 0.98529412 0.97058824] - -mean value: 0.9722293265132139 - -key: test_accuracy -value: [0.69230769 0.76923077 0.53846154 0.66666667 0.58333333 0.5 - 0.66666667 0.75 0.66666667 0.75 ] - -mean value: 0.6583333333333334 - -key: train_accuracy -value: [0.92727273 0.92727273 0.92727273 0.91891892 0.93693694 0.92792793 - 0.93693694 0.90990991 0.92792793 0.94594595] - -mean value: 0.9286322686322686 - -key: test_roc_auc -value: [0.675 0.7375 0.475 0.62857143 0.58571429 0.45714286 - 0.62857143 0.6875 0.625 0.75 ] - -mean value: 0.625 - -key: train_roc_auc -value: [0.91386555 0.91841737 0.90931373 0.90217391 0.93064182 0.90942029 - 0.92598344 0.89227086 0.91125171 0.93878249] - -mean value: 0.9152121161912694 - -key: test_jcc -value: [0.6 0.7 0.5 0.6 0.44444444 0.45454545 - 0.6 0.7 0.6 0.66666667] - -mean value: 0.5865656565656565 - -key: train_jcc -value: [0.89189189 0.89041096 0.89333333 0.88157895 0.90410959 0.89473684 - 0.90540541 0.86842105 0.89333333 0.91666667] - -mean value: 0.8939888020681099 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.78238106 0.58068442 0.60166001 0.70356202 0.56532955 0.60268497 - 0.59112096 0.69404554 0.58551407 0.58897471] - -mean value: 0.6295957326889038 - -key: score_time -value: [0.01313138 0.01312971 0.01340914 0.01305056 0.01307988 0.0131216 - 0.01315808 0.01324868 0.01174593 0.01343203] - -mean value: 0.013050699234008789 - -key: test_mcc -value: [0.35 0.84327404 0.41475753 0.83666003 0.31428571 0.52915026 - 0.47809144 0. 0.70710678 0.625 ] - -mean value: 0.5098325801667707 - -key: train_mcc -value: [0.98100984 1. 0.98100984 0.98111593 1. 1. - 1. 1. 0.92552101 1. ] - -mean value: 0.9868656621244465 - -key: test_fscore -value: [0.75 0.94117647 0.71428571 0.93333333 0.71428571 0.82352941 - 0.8 0.70588235 0.85714286 0.875 ] - -mean value: 0.8114635854341735 - -key: train_fscore -value: [0.99259259 1. 0.99259259 0.99270073 1. 1. - 1. 1. 0.97142857 1. ] - -mean value: 0.9949314486540765 - -key: test_precision -value: [0.75 0.88888889 0.83333333 0.875 0.71428571 0.7 - 0.75 0.66666667 1. 0.875 ] - -mean value: 0.8053174603174604 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 1. 0.94444444 1. ] - -mean value: 0.9944444444444445 - -key: test_recall -value: [0.75 1. 0.625 1. 0.71428571 1. - 0.85714286 0.75 0.75 0.875 ] - -mean value: 0.8321428571428571 - -key: train_recall -value: [0.98529412 1. 0.98529412 0.98550725 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9956095481670928 - -key: test_accuracy -value: [0.69230769 0.92307692 0.69230769 0.91666667 0.66666667 0.75 - 0.75 0.58333333 0.83333333 0.83333333] - -mean value: 0.764102564102564 - -key: train_accuracy -value: [0.99090909 1. 0.99090909 0.99099099 1. 1. - 1. 1. 0.96396396 1. ] - -mean value: 0.9936773136773137 - -key: test_roc_auc -value: [0.675 0.9 0.7125 0.9 0.65714286 0.7 - 0.72857143 0.5 0.875 0.8125 ] - -mean value: 0.7460714285714285 - -key: train_roc_auc -value: [0.99264706 1. 0.99264706 0.99275362 1. 1. - 1. 1. 0.95348837 1. ] - -mean value: 0.9931536112928487 - -key: test_jcc -value: [0.6 0.88888889 0.55555556 0.875 0.55555556 0.7 - 0.66666667 0.54545455 0.75 0.77777778] - -mean value: 0.691489898989899 - -key: train_jcc -value: [0.98529412 1. 0.98529412 0.98550725 1. 1. - 1. 1. 0.94444444 1. ] - -mean value: 0.9900539926115373 - -key: TN -value: 31 - -mean value: 31.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 63 - -mean value: 63.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01180434 0.01156044 0.00869417 0.00840425 0.00925708 0.00883102 - 0.00905108 0.00831056 0.00855541 0.00827074] - -mean value: 0.009273910522460937 - -key: score_time -value: [0.01157045 0.01013303 0.00877929 0.0085907 0.00855446 0.00904465 - 0.00895548 0.00870943 0.00923038 0.00833988] - -mean value: 0.009190773963928223 - -key: test_mcc -value: [ 0.53674504 0.21957752 0.05773503 0.50709255 0.02857143 0.02857143 - -0.2548236 0. -0.23904572 0.15811388] - -mean value: 0.10425375588567705 - -key: train_mcc -value: [0.46345451 0.44053439 0.32281987 0.44236993 0.48145494 0.51886112 - 0.46689953 0.46947169 0.49865546 0.55055954] - -mean value: 0.46550809707958835 - -key: test_fscore -value: [0.8 0.66666667 0.36363636 0.76923077 0.5 0.5 - 0.66666667 0.36363636 0.53333333 0.77777778] - -mean value: 0.594094794094794 - -key: train_fscore -value: [0.75806452 0.752 0.63063063 0.69026549 0.77165354 0.8030303 - 0.82352941 0.7751938 0.77777778 0.80314961] - -mean value: 0.7585295074114025 - -key: test_precision -value: [0.85714286 0.71428571 0.66666667 0.83333333 0.6 0.6 - 0.54545455 0.66666667 0.57142857 0.7 ] - -mean value: 0.6754978354978355 - -key: train_precision -value: [0.83928571 0.8245614 0.81395349 0.88636364 0.84482759 0.84126984 - 0.75 0.81967213 0.84482759 0.86440678] - -mean value: 0.8329168167022407 - -key: test_recall -value: [0.75 0.625 0.25 0.71428571 0.42857143 0.42857143 - 0.85714286 0.25 0.5 0.875 ] - -mean value: 0.5678571428571428 - -key: train_recall -value: [0.69117647 0.69117647 0.51470588 0.56521739 0.71014493 0.76811594 - 0.91304348 0.73529412 0.72058824 0.75 ] - -mean value: 0.7059462915601022 - -key: test_accuracy -value: [0.76923077 0.61538462 0.46153846 0.75 0.5 0.5 - 0.5 0.41666667 0.41666667 0.66666667] - -mean value: 0.5596153846153846 - -key: train_accuracy -value: [0.72727273 0.71818182 0.62727273 0.68468468 0.73873874 0.76576577 - 0.75675676 0.73873874 0.74774775 0.77477477] - -mean value: 0.727993447993448 - -key: test_roc_auc -value: [0.775 0.6125 0.525 0.75714286 0.51428571 0.51428571 - 0.42857143 0.5 0.375 0.5625 ] - -mean value: 0.5564285714285715 - -key: train_roc_auc -value: [0.73844538 0.72654062 0.66211485 0.72308489 0.74792961 0.76501035 - 0.70652174 0.73974008 0.75564295 0.78197674] - -mean value: 0.7347007205308816 - -key: test_jcc -value: [0.66666667 0.5 0.22222222 0.625 0.33333333 0.33333333 - 0.5 0.22222222 0.36363636 0.63636364] - -mean value: 0.44027777777777777 - -key: train_jcc -value: [0.61038961 0.6025641 0.46052632 0.52702703 0.62820513 0.67088608 - 0.7 0.63291139 0.63636364 0.67105263] - -mean value: 0.6139925920272356 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.32 - -Accuracy on Blind test: 0.66 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00860023 0.00946689 0.00877619 0.00849962 0.0084548 0.00853848 - 0.00842428 0.00853515 0.00877523 0.00882339] - -mean value: 0.008689427375793457 - -key: score_time -value: [0.0086565 0.00921583 0.00864005 0.00907683 0.00884461 0.00913835 - 0.00865912 0.00881648 0.00896597 0.00869155] - -mean value: 0.008870530128479003 - -key: test_mcc -value: [ 0.73192505 -0.05773503 -0.1844662 0.29277002 0.11952286 -0.37142857 - -0.65714286 -0.125 -0.23904572 0. ] - -mean value: -0.04906004366710262 - -key: train_mcc -value: [0.32909498 0.36744292 0.47177844 0.40914183 0.38716356 0.47639996 - 0.38168559 0.46853625 0.45724863 0.46429355] - -mean value: 0.4212785715783386 - -key: test_fscore -value: [0.85714286 0.66666667 0.58823529 0.75 0.66666667 0.42857143 - 0.16666667 0.625 0.53333333 0.70588235] - -mean value: 0.5988165266106443 - -key: train_fscore -value: [0.74074074 0.75555556 0.78787879 0.77372263 0.76811594 0.80851064 - 0.77142857 0.79411765 0.78195489 0.79710145] - -mean value: 0.777912684721997 - -key: test_precision -value: [1. 0.6 0.55555556 0.66666667 0.625 0.42857143 - 0.2 0.625 0.57142857 0.66666667] - -mean value: 0.5938888888888889 - -key: train_precision -value: [0.74626866 0.76119403 0.8125 0.77941176 0.76811594 0.79166667 - 0.76056338 0.79411765 0.8 0.78571429] - -mean value: 0.7799552373023497 - -key: test_recall -value: [0.75 0.75 0.625 0.85714286 0.71428571 0.42857143 - 0.14285714 0.625 0.5 0.75 ] - -mean value: 0.6142857142857142 - -key: train_recall -value: [0.73529412 0.75 0.76470588 0.76811594 0.76811594 0.82608696 - 0.7826087 0.79411765 0.76470588 0.80882353] - -mean value: 0.7762574595055413 - -key: test_accuracy -value: [0.84615385 0.53846154 0.46153846 0.66666667 0.58333333 0.33333333 - 0.16666667 0.5 0.41666667 0.58333333] - -mean value: 0.5096153846153847 - -key: train_accuracy -value: [0.68181818 0.7 0.74545455 0.72072072 0.71171171 0.75675676 - 0.71171171 0.74774775 0.73873874 0.74774775] - -mean value: 0.7262407862407863 - -key: test_roc_auc -value: [0.875 0.475 0.4125 0.62857143 0.55714286 0.31428571 - 0.17142857 0.4375 0.375 0.5 ] - -mean value: 0.4746428571428572 - -key: train_roc_auc -value: [0.66526611 0.68452381 0.7394958 0.70548654 0.69358178 0.73447205 - 0.6889234 0.73426813 0.73119015 0.72999316] - -mean value: 0.7107200918790164 - -key: test_jcc -value: [0.75 0.5 0.41666667 0.6 0.5 0.27272727 - 0.09090909 0.45454545 0.36363636 0.54545455] - -mean value: 0.4493939393939394 - -key: train_jcc -value: [0.58823529 0.60714286 0.65 0.63095238 0.62352941 0.67857143 - 0.62790698 0.65853659 0.64197531 0.6626506 ] - -mean value: 0.6369500845710674 - -key: TN -value: 16 - -mean value: 16.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 31 - -mean value: 31.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.38 - -Accuracy on Blind test: 0.71 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00872731 0.0109899 0.00829268 0.00792122 0.00795484 0.00792432 - 0.00792336 0.00785565 0.00792909 0.00805664] - -mean value: 0.008357501029968262 - -key: score_time -value: [0.04392147 0.0374155 0.0092802 0.00911021 0.00910616 0.00900841 - 0.00935888 0.00906873 0.00905585 0.00903249] - -mean value: 0.015435791015625 - -key: test_mcc -value: [ 0.21957752 0. -0.05773503 0.07559289 -0.37796447 -0.23904572 - -0.2548236 -0.125 -0.31622777 0.40824829] - -mean value: -0.06673778820515909 - -key: train_mcc -value: [0.26021759 0.24010945 0.28480141 0.31220998 0.29084942 0.31220998 - 0.40186078 0.32350485 0.30458868 0.27692451] - -mean value: 0.30072766582878036 - -key: test_fscore -value: [0.66666667 0.76190476 0.66666667 0.70588235 0.58823529 0.53333333 - 0.66666667 0.625 0.66666667 0.82352941] - -mean value: 0.6704551820728291 - -key: train_fscore -value: [0.7721519 0.76129032 0.77707006 0.78205128 0.77419355 0.78205128 - 0.80519481 0.78205128 0.76821192 0.7721519 ] - -mean value: 0.7776418304008816 - -key: test_precision -value: [0.71428571 0.61538462 0.6 0.6 0.5 0.5 - 0.54545455 0.625 0.6 0.77777778] - -mean value: 0.6077902652902651 - -key: train_precision -value: [0.67777778 0.67816092 0.68539326 0.70114943 0.69767442 0.70114943 - 0.72941176 0.69318182 0.69879518 0.67777778] - -mean value: 0.6940471766312707 - -key: test_recall -value: [0.625 1. 0.75 0.85714286 0.71428571 0.57142857 - 0.85714286 0.625 0.75 0.875 ] - -mean value: 0.7625 - -key: train_recall -value: [0.89705882 0.86764706 0.89705882 0.88405797 0.86956522 0.88405797 - 0.89855072 0.89705882 0.85294118 0.89705882] - -mean value: 0.8845055413469736 - -key: test_accuracy -value: [0.61538462 0.61538462 0.53846154 0.58333333 0.41666667 0.41666667 - 0.5 0.5 0.5 0.75 ] - -mean value: 0.5435897435897437 - -key: train_accuracy -value: [0.67272727 0.66363636 0.68181818 0.69369369 0.68468468 0.69369369 - 0.72972973 0.69369369 0.68468468 0.67567568] - -mean value: 0.6874037674037674 - -key: test_roc_auc -value: [0.6125 0.5 0.475 0.52857143 0.35714286 0.38571429 - 0.42857143 0.4375 0.375 0.6875 ] - -mean value: 0.47875000000000006 - -key: train_roc_auc -value: [0.60329132 0.6004902 0.61519608 0.63250518 0.6252588 0.63250518 - 0.67546584 0.63457592 0.63577291 0.61132011] - -mean value: 0.6266381527332874 - -key: test_jcc -value: [0.5 0.61538462 0.5 0.54545455 0.41666667 0.36363636 - 0.5 0.45454545 0.5 0.7 ] - -mean value: 0.5095687645687647 - -key: train_jcc -value: [0.62886598 0.61458333 0.63541667 0.64210526 0.63157895 0.64210526 - 0.67391304 0.64210526 0.62365591 0.62886598] - -mean value: 0.6363195653061748 - -key: TN -value: 9 - -mean value: 9.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 38 - -mean value: 38.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.61 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00947833 0.00908899 0.00898314 0.0089705 0.00936913 0.00897479 - 0.00902367 0.00896096 0.0091517 0.00906444] - -mean value: 0.00910656452178955 - -key: score_time -value: [0.00871348 0.00849056 0.00855875 0.00846887 0.00866127 0.00845981 - 0.00851274 0.00849438 0.0084672 0.00849605] - -mean value: 0.008532309532165527 - -key: test_mcc -value: [ 0.53935989 0.36514837 -0.05773503 0. -0.2548236 -0.2548236 - -0.2548236 -0.21320072 -0.21320072 0. ] - -mean value: -0.03440989851459174 - -key: train_mcc -value: [0.40237391 0.44525356 0.56282341 0.44624455 0.46682976 0.60088893 - 0.48694291 0.52238476 0.5347869 0.41707732] - -mean value: 0.4885606008648235 - -key: test_fscore -value: [0.84210526 0.8 0.66666667 0.73684211 0.66666667 0.66666667 - 0.66666667 0.73684211 0.73684211 0.8 ] - -mean value: 0.7319298245614034 - -key: train_fscore -value: [0.80952381 0.81927711 0.85 0.82142857 0.82634731 0.8625 - 0.8313253 0.8375 0.83950617 0.80952381] - -mean value: 0.8306932078343474 - -key: test_precision -value: [0.72727273 0.66666667 0.6 0.58333333 0.54545455 0.54545455 - 0.54545455 0.63636364 0.63636364 0.66666667] - -mean value: 0.6153030303030304 - -key: train_precision -value: [0.68 0.69387755 0.73913043 0.6969697 0.70408163 0.75824176 - 0.71134021 0.72826087 0.72340426 0.68 ] - -mean value: 0.7115306404737467 - -key: test_recall -value: [1. 1. 0.75 1. 0.85714286 0.85714286 - 0.85714286 0.875 0.875 1. ] - -mean value: 0.9071428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. - 1. 0.98529412 1. 1. ] - -mean value: 0.9985294117647058 - -key: test_accuracy -value: [0.76923077 0.69230769 0.53846154 0.58333333 0.5 0.5 - 0.5 0.58333333 0.58333333 0.66666667] - -mean value: 0.5916666666666667 - -key: train_accuracy -value: [0.70909091 0.72727273 0.78181818 0.72972973 0.73873874 0.8018018 - 0.74774775 0.76576577 0.76576577 0.71171171] - -mean value: 0.747944307944308 - -key: test_roc_auc -value: [0.7 0.6 0.475 0.5 0.42857143 0.42857143 - 0.42857143 0.4375 0.4375 0.5 ] - -mean value: 0.4935714285714285 - -key: train_roc_auc -value: [0.61904762 0.64285714 0.71428571 0.64285714 0.6547619 0.73809524 - 0.66666667 0.70194938 0.69767442 0.62790698] - -mean value: 0.6706102208325191 - -key: test_jcc -value: [0.72727273 0.66666667 0.5 0.58333333 0.5 0.5 - 0.5 0.58333333 0.58333333 0.66666667] - -mean value: 0.581060606060606 - -key: train_jcc -value: [0.68 0.69387755 0.73913043 0.6969697 0.70408163 0.75824176 - 0.71134021 0.72043011 0.72340426 0.68 ] - -mean value: 0.7107475642699131 - -key: TN -value: 4 - -mean value: 4.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.2 - -Accuracy on Blind test: 0.65 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.49486423 0.51623654 0.47505784 0.67788649 0.49271965 0.52207398 - 0.52219319 0.61589813 0.51179814 0.54275203] - -mean value: 0.5371480226516724 - -key: score_time -value: [0.01192236 0.01202393 0.01187253 0.01178312 0.01180911 0.01181746 - 0.01180387 0.01184916 0.01177979 0.01179743] - -mean value: 0.011845874786376952 - -key: test_mcc -value: [ 0.15811388 0.21957752 0.35 0.52915026 -0.37142857 -0.09759001 - 0.47809144 0.40824829 -0.125 0.35355339] - -mean value: 0.19027162077022267 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.70588235 0.66666667 0.75 0.82352941 0.42857143 0.625 - 0.8 0.82352941 0.625 0.71428571] - -mean value: 0.6962464985994398 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.71428571 0.75 0.7 0.42857143 0.55555556 - 0.75 0.77777778 0.625 0.83333333] - -mean value: 0.6801190476190475 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.625 0.75 1. 0.42857143 0.71428571 - 0.85714286 0.875 0.625 0.625 ] - -mean value: 0.725 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.61538462 0.61538462 0.69230769 0.75 0.33333333 0.5 - 0.75 0.75 0.5 0.66666667] - -mean value: 0.6173076923076923 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.575 0.6125 0.675 0.7 0.31428571 0.45714286 - 0.72857143 0.6875 0.4375 0.6875 ] - -mean value: 0.5875 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.54545455 0.5 0.6 0.7 0.27272727 0.45454545 - 0.66666667 0.7 0.45454545 0.55555556] - -mean value: 0.5449494949494949 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 21 - -mean value: 21.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 55 - -mean value: 55.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.79 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01489496 0.01408672 0.01056743 0.01056862 0.01049972 0.01067162 - 0.01093578 0.01076221 0.01055264 0.01062989] - -mean value: 0.011416959762573241 - -key: score_time -value: [0.01156378 0.00994086 0.00843978 0.00833082 0.00827193 0.0083282 - 0.00828743 0.00835609 0.00836277 0.00829649] - -mean value: 0.008817815780639648 - -key: test_mcc -value: [0.85391256 0.675 0.85391256 0.84515425 0.65714286 0.83666003 - 0.65714286 0.625 1. 0.83666003] - -mean value: 0.7840585149742315 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93333333 0.875 0.93333333 0.92307692 0.85714286 0.93333333 - 0.85714286 0.875 1. 0.93333333] - -mean value: 0.9120695970695971 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.875 1. 1. 0.85714286 0.875 - 0.85714286 0.875 1. 1. ] - -mean value: 0.9339285714285716 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.875 0.875 0.85714286 0.85714286 1. - 0.85714286 0.875 1. 0.875 ] - -mean value: 0.8946428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92307692 0.84615385 0.92307692 0.91666667 0.83333333 0.91666667 - 0.83333333 0.83333333 1. 0.91666667] - -mean value: 0.8942307692307692 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.8375 0.9375 0.92857143 0.82857143 0.9 - 0.82857143 0.8125 1. 0.9375 ] - -mean value: 0.8948214285714287 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.875 0.77777778 0.875 0.85714286 0.75 0.875 - 0.75 0.77777778 1. 0.875 ] - -mean value: 0.8412698412698413 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 68 - -mean value: 68.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.9 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.08687162 0.08488417 0.08471727 0.08456445 0.08452892 0.08550549 - 0.08529496 0.08598661 0.08636308 0.08553123] - -mean value: 0.08542478084564209 - -key: score_time -value: [0.01668167 0.01676798 0.01683354 0.01664472 0.01674986 0.01668262 - 0.01673627 0.01670599 0.0166409 0.01674151] - -mean value: 0.016718506813049316 - -key: test_mcc -value: [ 0.31754265 0.21957752 0.21957752 0.29277002 -0.48795004 -0.09759001 - 0.07559289 0.25 0.15811388 0.47809144] - -mean value: 0.1425725880340596 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.77777778 0.66666667 0.66666667 0.75 0.5 0.625 - 0.70588235 0.75 0.77777778 0.8 ] - -mean value: 0.7019771241830065 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.7 0.71428571 0.71428571 0.66666667 0.44444444 0.55555556 - 0.6 0.75 0.7 0.85714286] - -mean value: 0.6702380952380953 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.625 0.625 0.85714286 0.57142857 0.71428571 - 0.85714286 0.75 0.875 0.75 ] - -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: [0.69230769 0.61538462 0.61538462 0.66666667 0.33333333 0.5 - 0.58333333 0.66666667 0.66666667 0.75 ] - -mean value: 0.6089743589743589 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6375 0.6125 0.6125 0.62857143 0.28571429 0.45714286 - 0.52857143 0.625 0.5625 0.75 ] - -mean value: 0.5700000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.63636364 0.5 0.5 0.6 0.33333333 0.45454545 - 0.54545455 0.6 0.63636364 0.66666667] - -mean value: 0.5472727272727274 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 18 - -mean value: 18.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 29 - -mean value: 29.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.45 - -Accuracy on Blind test: 0.74 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00829029 0.00823355 0.00817823 0.00814819 0.00815415 0.00818539 - 0.00818944 0.00816822 0.00817585 0.00820303] - -mean value: 0.008192634582519532 - -key: score_time -value: [0.00814843 0.00815177 0.00821733 0.00820231 0.00818133 0.00818706 - 0.0082128 0.0082233 0.00823975 0.00820589] - -mean value: 0.00819699764251709 - -key: test_mcc -value: [-0.21957752 -0.05773503 0.35 0.37142857 0.11952286 -0.5976143 - -0.02857143 0.47809144 0.15811388 0. ] - -mean value: 0.05736584825331793 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.42857143 0.66666667 0.75 0.66666667 0.66666667 0.4 - 0.57142857 0.8 0.77777778 0.36363636] - -mean value: 0.6091414141414141 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.5 0.6 0.75 0.8 0.625 0.375 - 0.57142857 0.85714286 0.7 0.66666667] - -mean value: 0.6445238095238096 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.375 0.75 0.75 0.57142857 0.71428571 0.42857143 - 0.57142857 0.75 0.875 0.25 ] - -mean value: 0.6035714285714285 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.38461538 0.53846154 0.69230769 0.66666667 0.58333333 0.25 - 0.5 0.75 0.66666667 0.41666667] - -mean value: 0.5448717948717949 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.3875 0.475 0.675 0.68571429 0.55714286 0.21428571 - 0.48571429 0.75 0.5625 0.5 ] - -mean value: 0.5292857142857142 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.27272727 0.5 0.6 0.5 0.5 0.25 - 0.4 0.66666667 0.63636364 0.22222222] - -mean value: 0.45479797979797987 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 21 - -mean value: 21.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 46 - -mean value: 46.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.05 - -Accuracy on Blind test: 0.55 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.09546304 1.08743787 1.08374643 1.14095879 1.08425879 1.08838081 - 1.08451486 1.08428717 1.08518529 1.09441781]/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - - -mean value: 1.0928650856018067 - -key: score_time -value: [0.09162807 0.09519958 0.08684635 0.08672667 0.08648539 0.08686352 - 0.09133315 0.08665919 0.08701229 0.08668303] - -mean value: 0.08854372501373291 - -key: test_mcc -value: [0.50069396 0.35 0.53674504 0.68313005 0.47809144 0.29277002 - 0.29277002 0.47809144 0.625 0.40824829] - -mean value: 0.4645540275746158 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.82352941 0.75 0.8 0.875 0.8 0.75 - 0.75 0.8 0.875 0.82352941] - -mean value: 0.8047058823529412 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.77777778 0.75 0.85714286 0.77777778 0.75 0.66666667 - 0.66666667 0.85714286 0.875 0.77777778] - -mean value: 0.775595238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.75 0.75 1. 0.85714286 0.85714286 - 0.85714286 0.75 0.875 0.875 ] - -mean value: 0.8446428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.76923077 0.69230769 0.76923077 0.83333333 0.75 0.66666667 - 0.66666667 0.75 0.83333333 0.75 ] - -mean value: 0.7480769230769231 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.7375 0.675 0.775 0.8 0.72857143 0.62857143 - 0.62857143 0.75 0.8125 0.6875 ] - -mean value: 0.7223214285714287 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.7 0.6 0.66666667 0.77777778 0.66666667 0.6 - 0.6 0.66666667 0.77777778 0.7 ] - -mean value: 0.6755555555555556 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 28 - -mean value: 28.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 64 - -mean value: 64.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.69 - -Accuracy on Blind test: 0.85 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.81459498 0.85142756 0.83559656 0.90151811 0.86936331 0.8753283 - 0.84840512 0.85588837 0.8444097 0.84958768] - -mean value: 0.8546119689941406 - -key: score_time -value: [0.19565916 0.17850089 0.1850121 0.1829555 0.17323041 0.14045858 - 0.14689827 0.17946649 0.17438769 0.16761422] - -mean value: 0.17241833209991456 - -key: test_mcc -value: [0.50069396 0.69282032 0.35 0.68313005 0.47809144 0.52915026 - 0.07559289 0.625 0.15811388 0.63245553] - -mean value: 0.47250483525421333 - -key: train_mcc -value: [0.82905263 0.84761434 0.86922699 0.86692145 0.86692145 0.86692145 - 0.82983637 0.8683248 0.88678314 0.88678314] - -mean value: 0.8618385753871097 - -key: test_fscore -value: [0.82352941 0.88888889 0.75 0.875 0.8 0.82352941 - 0.70588235 0.875 0.77777778 0.88888889] - -mean value: 0.8208496732026143 - -key: train_fscore -value: [0.93706294 0.94366197 0.95104895 0.95104895 0.95104895 0.95104895 - 0.93793103 0.95035461 0.95714286 0.95714286] - -mean value: 0.9487492071787278 - -key: test_precision -value: [0.77777778 0.8 0.75 0.77777778 0.75 0.7 - 0.6 0.875 0.7 0.8 ] - -mean value: 0.7530555555555555 - -key: train_precision -value: [0.89333333 0.90540541 0.90666667 0.91891892 0.91891892 0.91891892 - 0.89473684 0.91780822 0.93055556 0.93055556] - -mean value: 0.9135818334556619 - -key: test_recall -value: [0.875 1. 0.75 1. 0.85714286 1. - 0.85714286 0.875 0.875 1. ] - -mean value: 0.9089285714285715 - -key: train_recall -value: [0.98529412 0.98529412 1. 0.98550725 0.98550725 0.98550725 - 0.98550725 0.98529412 0.98529412 0.98529412] - -mean value: 0.986849957374254 - -key: test_accuracy -value: [0.76923077 0.84615385 0.69230769 0.83333333 0.75 0.75 - 0.58333333 0.83333333 0.66666667 0.83333333] - -mean value: 0.7557692307692309 - -key: train_accuracy -value: [0.91818182 0.92727273 0.93636364 0.93693694 0.93693694 0.93693694 - 0.91891892 0.93693694 0.94594595 0.94594595] - -mean value: 0.934037674037674 - -key: test_roc_auc -value: [0.7375 0.8 0.675 0.8 0.72857143 0.7 - 0.52857143 0.8125 0.5625 0.75 ] - -mean value: 0.7094642857142858 - -key: train_roc_auc -value: [0.89740896 0.90931373 0.91666667 0.92132505 0.92132505 0.92132505 - 0.89751553 0.92287962 0.93450752 0.93450752] - -mean value: 0.9176774703814793 - -key: test_jcc -value: [0.7 0.8 0.6 0.77777778 0.66666667 0.7 - 0.54545455 0.77777778 0.63636364 0.8 ] - -mean value: 0.7004040404040404 - -key: train_jcc -value: [0.88157895 0.89333333 0.90666667 0.90666667 0.90666667 0.90666667 - 0.88311688 0.90540541 0.91780822 0.91780822] - -mean value: 0.9025717674246874 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 13 -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=None, 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/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 -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.09464025 0.04113626 0.04390836 0.04294944 0.18263888 0.03982615 - 0.0420804 0.04239774 0.04127336 0.04275012] - -mean value: 0.06136009693145752 - -key: score_time -value: [0.01062655 0.01001501 0.01005936 0.00997829 0.01059461 0.01022339 - 0.01007247 0.0101881 0.01008344 0.01005411] - -mean value: 0.010189533233642578 - -key: test_mcc -value: [1. 0.50069396 0.53674504 0.84515425 0.65714286 0.83666003 - 0.83666003 0.625 0.625 0.81649658] - -mean value: 0.7279552748848938 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.82352941 0.8 0.92307692 0.85714286 0.93333333 - 0.93333333 0.875 0.875 0.94117647] - -mean value: 0.8961592329239387 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.77777778 0.85714286 1. 0.85714286 0.875 - 0.875 0.875 0.875 0.88888889] - -mean value: 0.8880952380952382 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.875 0.75 0.85714286 0.85714286 1. - 1. 0.875 0.875 1. ] - -mean value: 0.9089285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.76923077 0.76923077 0.91666667 0.83333333 0.91666667 - 0.91666667 0.83333333 0.83333333 0.91666667] - -mean value: 0.8705128205128204 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.7375 0.775 0.92857143 0.82857143 0.9 - 0.9 0.8125 0.8125 0.875 ] - -mean value: 0.8569642857142858 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.7 0.66666667 0.85714286 0.75 0.875 - 0.875 0.77777778 0.77777778 0.88888889] - -mean value: 0.8168253968253968 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03773665 0.03072977 0.01915598 0.01995564 0.01927376 0.04759216 - 0.04356813 0.06778336 0.06304049 0.04747105] - -mean value: 0.039630699157714847 - -key: score_time -value: [0.01988006 0.0116334 0.01142693 0.01161718 0.01167965 0.02221656 - 0.01711965 0.01969266 0.02830029 0.0115099 ] - -mean value: 0.016507625579833984 - -key: test_mcc -value: [ 0.35 0.41475753 0.625 0.23904572 -0.29277002 -0.23904572 - 0.16903085 0.625 0.23904572 0.47809144] - -mean value: 0.2608155525664906 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.75 0.71428571 0.76923077 0.54545455 0.2 0.53333333 - 0.61538462 0.875 0.61538462 0.8 ] - -mean value: 0.6418073593073592 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.75 0.83333333 1. 0.75 0.33333333 0.5 - 0.66666667 0.875 0.8 0.85714286] - -mean value: 0.736547619047619 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.625 0.625 0.42857143 0.14285714 0.57142857 - 0.57142857 0.875 0.5 0.75 ] - -mean value: 0.5839285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.69230769 0.69230769 0.76923077 0.58333333 0.33333333 0.41666667 - 0.58333333 0.83333333 0.58333333 0.75 ] - -mean value: 0.6237179487179487 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.675 0.7125 0.8125 0.61428571 0.37142857 0.38571429 - 0.58571429 0.8125 0.625 0.75 ] - -mean value: 0.6344642857142857 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.6 0.55555556 0.625 0.375 0.11111111 0.36363636 - 0.44444444 0.77777778 0.44444444 0.66666667] - -mean value: 0.4963636363636364 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 32 - -mean value: 32.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.46 - -Accuracy on Blind test: 0.73 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02157807 0.00869703 0.00844336 0.00849652 0.00846434 0.008219 - 0.00820565 0.00873017 0.00826836 0.00818968] - -mean value: 0.009729218482971192 - -key: score_time -value: [0.0115695 0.00851297 0.00859666 0.00849891 0.00861049 0.00815916 - 0.00822258 0.0087266 0.00830817 0.0088129 ] - -mean value: 0.008801794052124024 - -key: test_mcc -value: [ 0.50069396 0.50069396 0.21957752 0.35675303 -0.23904572 -0.09759001 - 0.07559289 -0.21320072 0.15811388 0.40824829] - -mean value: 0.16698370986965294 - -key: train_mcc -value: [0.38383017 0.3811422 0.44420485 0.34242672 0.40566227 0.42728475 - 0.42728475 0.31408433 0.33243128 0.38975075] - -mean value: 0.38481020774814817 - -key: test_fscore -value: [0.82352941 0.82352941 0.66666667 0.77777778 0.53333333 0.625 - 0.70588235 0.73684211 0.77777778 0.82352941] - -mean value: 0.7293868249054007 - -key: train_fscore -value: [0.7862069 0.78911565 0.80821918 0.77852349 0.7972973 0.80272109 - 0.80272109 0.75862069 0.76712329 0.79470199] - -mean value: 0.7885250649074724 - -key: test_precision -value: [0.77777778 0.77777778 0.71428571 0.63636364 0.5 0.55555556 - 0.6 0.63636364 0.7 0.77777778] - -mean value: 0.6675901875901876 - -key: train_precision -value: [0.74025974 0.73417722 0.75641026 0.725 0.74683544 0.75641026 - 0.75641026 0.71428571 0.71794872 0.72289157] - -mean value: 0.737062916621785 - -key: test_recall -value: [0.875 0.875 0.625 1. 0.57142857 0.71428571 - 0.85714286 0.875 0.875 0.875 ] - -mean value: 0.8142857142857143 - -key: train_recall -value: [0.83823529 0.85294118 0.86764706 0.84057971 0.85507246 0.85507246 - 0.85507246 0.80882353 0.82352941 0.88235294] - -mean value: 0.8479326513213981 - -key: test_accuracy -value: [0.76923077 0.76923077 0.61538462 0.66666667 0.41666667 0.5 - 0.58333333 0.58333333 0.66666667 0.75 ] - -mean value: 0.632051282051282 - -key: train_accuracy -value: [0.71818182 0.71818182 0.74545455 0.7027027 0.72972973 0.73873874 - 0.73873874 0.68468468 0.69369369 0.72072072] - -mean value: 0.7190827190827191 - -key: test_roc_auc -value: [0.7375 0.7375 0.6125 0.6 0.38571429 0.45714286 - 0.52857143 0.4375 0.5625 0.6875 ] - -mean value: 0.5746428571428572 - -key: train_roc_auc -value: [0.68102241 0.67647059 0.70763305 0.65838509 0.68944099 0.70134576 - 0.70134576 0.64859781 0.65595075 0.67373461] - -mean value: 0.6793926822498463 - -key: test_jcc -value: [0.7 0.7 0.5 0.63636364 0.36363636 0.45454545 - 0.54545455 0.58333333 0.63636364 0.7 ] - -mean value: 0.581969696969697 - -key: train_jcc -value: [0.64772727 0.65168539 0.67816092 0.63736264 0.66292135 0.67045455 - 0.67045455 0.61111111 0.62222222 0.65934066] - -mean value: 0.6511440654786257 - -key: TN -value: 16 - -mean value: 16.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 31 - -mean value: 31.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.69 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00946546 0.01361704 0.01318765 0.01225996 0.01225066 0.01285219 - 0.01456738 0.0147326 0.01424146 0.01396871] - -mean value: 0.013114309310913086 - -key: score_time -value: [0.00867009 0.01097059 0.0109055 0.01113343 0.01116776 0.01117802 - 0.01155496 0.01120424 0.01120448 0.01112294] - -mean value: 0.010911202430725098 - -key: test_mcc -value: [ 0.53674504 0.53935989 0.10112998 0.23904572 -0.09759001 0.31428571 - 0.68313005 0.47809144 0.40824829 0.35355339] - -mean value: 0.35559995141743805 - -key: train_mcc -value: [0.90393035 0.81499979 0.6542355 0.57420392 0.73821371 0.81429778 - 0.76224042 0.86252225 0.94388927 0.92537599] - -mean value: 0.7993908985251966 - -key: test_fscore -value: [0.8 0.84210526 0.73684211 0.54545455 0.625 0.71428571 - 0.875 0.8 0.82352941 0.71428571] - -mean value: 0.7476502754211732 - -key: train_fscore -value: [0.96402878 0.93150685 0.87741935 0.72222222 0.90666667 0.92537313 - 0.91390728 0.9375 0.97841727 0.97014925] - -mean value: 0.9127190809036048 - -key: test_precision -value: [0.85714286 0.72727273 0.63636364 0.75 0.55555556 0.71428571 - 0.77777778 0.85714286 0.77777778 0.83333333] - -mean value: 0.7486652236652236 - -key: train_precision -value: [0.94366197 0.87179487 0.7816092 1. 0.83950617 0.95384615 - 0.84146341 1. 0.95774648 0.98484848] - -mean value: 0.9174476744069686 - -key: test_recall -value: [0.75 1. 0.875 0.42857143 0.71428571 0.71428571 - 1. 0.75 0.875 0.625 ] - -mean value: 0.7732142857142857 - -key: train_recall -value: [0.98529412 1. 1. 0.56521739 0.98550725 0.89855072 - 1. 0.88235294 1. 0.95588235] - -mean value: 0.9272804774083545 - -key: test_accuracy -value: [0.76923077 0.76923077 0.61538462 0.58333333 0.5 0.66666667 - 0.83333333 0.75 0.75 0.66666667] - -mean value: 0.6903846153846154 - -key: train_accuracy -value: [0.95454545 0.90909091 0.82727273 0.72972973 0.87387387 0.90990991 - 0.88288288 0.92792793 0.97297297 0.96396396] - -mean value: 0.8952170352170352 - -key: test_roc_auc -value: [0.775 0.7 0.5375 0.61428571 0.45714286 0.65714286 - 0.8 0.75 0.6875 0.6875 ] - -mean value: 0.666607142857143 - -key: train_roc_auc -value: [0.94502801 0.88095238 0.77380952 0.7826087 0.83799172 0.91356108 - 0.8452381 0.94117647 0.96511628 0.96631327] - -mean value: 0.8851795521039559 - -key: test_jcc -value: [0.66666667 0.72727273 0.58333333 0.375 0.45454545 0.55555556 - 0.77777778 0.66666667 0.7 0.55555556] - -mean value: 0.6062373737373737 - -key: train_jcc -value: [0.93055556 0.87179487 0.7816092 0.56521739 0.82926829 0.86111111 - 0.84146341 0.88235294 0.95774648 0.94202899] - -mean value: 0.8463148238042214 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -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)) -76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.81 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.0125587 0.01264143 0.01278758 0.01199651 0.01312399 0.01274061 - 0.01246476 0.01312518 0.01257873 0.01276875] - -mean value: 0.01267862319946289 - -key: score_time -value: [0.01114702 0.01114774 0.01115823 0.01123714 0.01115966 0.01120853 - 0.0119431 0.01129484 0.01114678 0.01116776] - -mean value: 0.011261081695556641 - -key: test_mcc -value: [0.35 0.69282032 0.36514837 0.35675303 0.11952286 0.07559289 - 0. 0.625 0.5 0.15811388] - -mean value: 0.3242951367247703 - -key: train_mcc -value: [0.8510645 0.88503928 0.67218096 0.52601495 0.69106783 0.90396349 - 0.38789701 0.80998026 0.92794199 0.80035317] - -mean value: 0.7455503461752209 - -key: test_fscore -value: [0.75 0.88888889 0.8 0.77777778 0.66666667 0.70588235 - 0. 0.875 0.66666667 0.77777778] - -mean value: 0.6908660130718955 - -key: train_fscore -value: [0.94444444 0.95714286 0.88311688 0.84146341 0.89032258 0.96402878 - 0.48351648 0.91338583 0.96969697 0.92517007] - -mean value: 0.8772288304974227 - -key: test_precision -value: [0.75 0.8 0.66666667 0.63636364 0.625 0.6 - 0. 0.875 1. 0.7 ] - -mean value: 0.6653030303030303 - -key: train_precision -value: [0.89473684 0.93055556 0.79069767 0.72631579 0.80232558 0.95714286 - 1. 0.98305085 1. 0.86075949] - -mean value: 0.8945584641219826 - -key: test_recall -value: [0.75 1. 1. 1. 0.71428571 0.85714286 - 0. 0.875 0.5 0.875 ] - -mean value: 0.7571428571428571 - -key: train_recall -value: [1. 0.98529412 1. 1. 1. 0.97101449 - 0.31884058 0.85294118 0.94117647 1. ] - -mean value: 0.9069266837169649 - -key: test_accuracy -value: [0.69230769 0.84615385 0.69230769 0.66666667 0.58333333 0.58333333 - 0.41666667 0.83333333 0.66666667 0.66666667] - -mean value: 0.6647435897435898 - -key: train_accuracy -value: [0.92727273 0.94545455 0.83636364 0.76576577 0.84684685 0.95495495 - 0.57657658 0.9009009 0.96396396 0.9009009 ] - -mean value: 0.8619000819000819 - -key: test_roc_auc -value: [0.675 0.8 0.6 0.6 0.55714286 0.52857143 - 0.5 0.8125 0.75 0.5625 ] - -mean value: 0.6385714285714286 - -key: train_roc_auc -value: [0.9047619 0.93312325 0.78571429 0.69047619 0.79761905 0.94979296 - 0.65942029 0.91484268 0.97058824 0.87209302] - -mean value: 0.8478431868197228 - -key: test_jcc -value: [0.6 0.8 0.66666667 0.63636364 0.5 0.54545455 - 0. 0.77777778 0.5 0.63636364] - -mean value: 0.5662626262626264 - -key: train_jcc -value: [0.89473684 0.91780822 0.79069767 0.72631579 0.80232558 0.93055556 - 0.31884058 0.84057971 0.94117647 0.86075949] - -mean value: 0.8023795916240731 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.84 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.10628152 0.08904624 0.089674 0.0911634 0.09197474 0.09089756 - 0.0883739 0.08940411 0.08909869 0.08851576] - -mean value: 0.09144299030303955 - -key: score_time -value: [0.01433253 0.0145371 0.01444006 0.01587653 0.01531577 0.01451087 - 0.01486683 0.01439857 0.01451421 0.01443386] - -mean value: 0.014722633361816406 - -key: test_mcc -value: [0.73192505 0.69282032 0.53674504 0.71428571 0.65714286 0.83666003 - 0.65714286 0.81649658 0.625 0.63245553] - -mean value: 0.690067398592755 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.85714286 0.88888889 0.8 0.83333333 0.85714286 0.93333333 - 0.85714286 0.94117647 0.875 0.88888889] - -mean value: 0.8732049486461252 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.8 0.85714286 1. 0.85714286 0.875 - 0.85714286 0.88888889 0.875 0.8 ] - -mean value: 0.8810317460317462 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 0.75 0.71428571 0.85714286 1. - 0.85714286 1. 0.875 1. ] - -mean value: 0.8803571428571428 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.84615385 0.84615385 0.76923077 0.83333333 0.83333333 0.91666667 - 0.83333333 0.91666667 0.83333333 0.83333333] - -mean value: 0.8461538461538461 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.8 0.775 0.85714286 0.82857143 0.9 - 0.82857143 0.875 0.8125 0.75 ] - -mean value: 0.8301785714285714 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.75 0.8 0.66666667 0.71428571 0.75 0.875 - 0.75 0.88888889 0.77777778 0.8 ] - -mean value: 0.7772619047619047 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03100371 0.03074861 0.04495049 0.04121494 0.0335474 0.0405786 - 0.04685497 0.0346806 0.04328895 0.03637171] - -mean value: 0.03832399845123291 - -key: score_time -value: [0.01783442 0.01984453 0.02553606 0.02212143 0.02536345 0.03404522 - 0.01622009 0.02240157 0.02442431 0.02128291] - -mean value: 0.022907400131225587 - -key: test_mcc -value: [0.85391256 0.50069396 0.675 0.84515425 0.65714286 0.83666003 - 0.65714286 0.47809144 0.625 0.625 ] - -mean value: 0.6753797965972024 - -key: train_mcc -value: [0.96253342 1. 0.96148459 0.98111593 0.98094082 0.98094082 - 0.96169772 1. 0.98111593 0.92537599] - -mean value: 0.9735205243744222 - -key: test_fscore -value: [0.93333333 0.82352941 0.875 0.92307692 0.85714286 0.93333333 - 0.85714286 0.8 0.875 0.875 ] - -mean value: 0.875255871579401 - -key: train_fscore -value: [0.98507463 1. 0.98529412 0.99270073 0.99280576 0.99280576 - 0.98550725 1. 0.99270073 0.97014925] - -mean value: 0.9897038215266267 - -key: test_precision -value: [1. 0.77777778 0.875 1. 0.85714286 0.875 - 0.85714286 0.85714286 0.875 0.875 ] - -mean value: 0.8849206349206348 - -key: train_precision -value: [1. 1. 0.98529412 1. 0.98571429 0.98571429 - 0.98550725 1. 0.98550725 0.98484848] - -mean value: 0.9912585666677739 - -key: test_recall -value: [0.875 0.875 0.875 0.85714286 0.85714286 1. - 0.85714286 0.75 0.875 0.875 ] - -mean value: 0.8696428571428572 - -key: train_recall -value: [0.97058824 1. 0.98529412 0.98550725 1. 1. - 0.98550725 1. 1. 0.95588235] - -mean value: 0.9882779198635975 - -key: test_accuracy -value: [0.92307692 0.76923077 0.84615385 0.91666667 0.83333333 0.91666667 - 0.83333333 0.75 0.83333333 0.83333333] - -mean value: 0.8455128205128204 - -key: train_accuracy -value: [0.98181818 1. 0.98181818 0.99099099 0.99099099 0.99099099 - 0.98198198 1. 0.99099099 0.96396396] - -mean value: 0.9873546273546273 - -key: test_roc_auc -value: [0.9375 0.7375 0.8375 0.92857143 0.82857143 0.9 - 0.82857143 0.75 0.8125 0.8125 ] - -mean value: 0.8373214285714287 - -key: train_roc_auc -value: [0.98529412 1. 0.9807423 0.99275362 0.98809524 0.98809524 - 0.98084886 1. 0.98837209 0.96631327] - -mean value: 0.987051473774545 - -key: test_jcc -value: [0.875 0.7 0.77777778 0.85714286 0.75 0.875 - 0.75 0.66666667 0.77777778 0.77777778] - -mean value: 0.7807142857142857 - -key: train_jcc -value: [0.97058824 1. 0.97101449 0.98550725 0.98571429 0.98571429 - 0.97142857 1. 0.98550725 0.94202899] - -mean value: 0.9797503349165753 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 66 - -mean value: 66.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.0334475 0.04036093 0.03545952 0.03805256 0.0399847 0.04043794 - 0.04135227 0.04058337 0.04076147 0.03606129] - -mean value: 0.03865015506744385 - -key: score_time -value: [0.02255535 0.02062464 0.0196178 0.01720858 0.024055 0.01250672 - 0.02217317 0.02099752 0.02161336 0.0183444 ] - -mean value: 0.019969654083251954 - -key: test_mcc -value: [ 0.025 0.15811388 -0.09759001 0.11952286 -0.48795004 -0.09759001 - -0.48795004 -0.125 -0.31622777 0.25 ] - -mean value: -0.10596711096132197 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.625 0.70588235 0.53333333 0.66666667 0.5 0.625 - 0.5 0.625 0.66666667 0.75 ] - -mean value: 0.6197549019607843 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.625 0.66666667 0.57142857 0.625 0.44444444 0.55555556 - 0.44444444 0.625 0.6 0.75 ] - -mean value: 0.5907539682539682 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.75 0.5 0.71428571 0.57142857 0.71428571 - 0.57142857 0.625 0.75 0.75 ] - -mean value: 0.6571428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.53846154 0.61538462 0.46153846 0.58333333 0.33333333 0.5 - 0.33333333 0.5 0.5 0.66666667] - -mean value: 0.5032051282051282 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.5125 0.575 0.45 0.55714286 0.28571429 0.45714286 - 0.28571429 0.4375 0.375 0.625 ] - -mean value: 0.4560714285714285 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.45454545 0.54545455 0.36363636 0.5 0.33333333 0.45454545 - 0.33333333 0.45454545 0.5 0.6 ] - -mean value: 0.4539393939393939 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 12 - -mean value: 12.0 - -key: FP -value: 26 - -mean value: 26.0 - -key: FN -value: 35 - -mean value: 35.0 - -key: TP -value: 50 - -mean value: 50.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.08 - -Accuracy on Blind test: 0.58 - -Running classifier: 21 -Model_name: Gradient Boosting -Model func: GradientBoostingClassifier(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.25328517 0.22863054 0.22370648 0.22962642 0.22986698 0.22808623 - 0.2266736 0.22830272 0.23198032 0.22613072] - -mean value: 0.23062891960144044 - -key: score_time -value: [0.00900102 0.00898838 0.00899267 0.00883532 0.00903273 0.00897455 - 0.00888777 0.00897026 0.00880384 0.00959563] - -mean value: 0.009008216857910156 - -key: test_mcc -value: [0.85391256 0.50069396 0.85391256 1. 0.65714286 0.83666003 - 0.83666003 0.47809144 0.81649658 0.81649658] - -mean value: 0.7650066606320145 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93333333 0.82352941 0.93333333 1. 0.85714286 0.93333333 - 0.93333333 0.8 0.94117647 0.94117647] - -mean value: 0.9096358543417367 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.77777778 1. 1. 0.85714286 0.875 - 0.875 0.85714286 0.88888889 0.88888889] - -mean value: 0.9019841269841271 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.875 0.875 1. 0.85714286 1. - 1. 0.75 1. 1. ] - -mean value: 0.9232142857142858 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92307692 0.76923077 0.92307692 1. 0.83333333 0.91666667 - 0.91666667 0.75 0.91666667 0.91666667] - -mean value: 0.8865384615384617 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.7375 0.9375 1. 0.82857143 0.9 - 0.9 0.75 0.875 0.875 ] - -mean value: 0.8741071428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.875 0.7 0.875 1. 0.75 0.875 - 0.875 0.66666667 0.88888889 0.88888889] - -mean value: 0.8394444444444445 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 39 - -mean value: 39.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 70 - -mean value: 70.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01242208 0.01676083 0.02331352 0.01530957 0.01565695 0.01562834 - 0.01548266 0.01578426 0.01571488 0.01702189] - -mean value: 0.016309499740600586 - -key: score_time -value: [0.0116725 0.01181579 0.01174021 0.0117867 0.01330113 0.01283383 - 0.01169133 0.01326752 0.0130136 0.01296425] - -mean value: 0.012408685684204102 - -key: test_mcc -value: [ 0.36514837 0.10112998 0.025 0.29277002 -0.50709255 0.47809144 - -0.09759001 -0.125 0.25 0.47809144] - -mean value: 0.12605487002597085 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.73684211 0.625 0.75 0.30769231 0.8 - 0.625 0.625 0.75 0.8 ] - -mean value: 0.6819534412955466 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.63636364 0.625 0.66666667 0.33333333 0.75 - 0.55555556 0.625 0.75 0.85714286] - -mean value: 0.6465728715728715 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.875 0.625 0.85714286 0.28571429 0.85714286 - 0.71428571 0.625 0.75 0.75 ] - -mean value: 0.7339285714285715 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.69230769 0.61538462 0.53846154 0.66666667 0.25 0.75 - 0.5 0.5 0.66666667 0.75 ] - -mean value: 0.592948717948718 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6 0.5375 0.5125 0.62857143 0.24285714 0.72857143 - 0.45714286 0.4375 0.625 0.75 ] - -mean value: 0.5519642857142857 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.58333333 0.45454545 0.6 0.18181818 0.66666667 - 0.45454545 0.45454545 0.6 0.66666667] - -mean value: 0.532878787878788 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 18 - -mean value: 18.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 29 - -mean value: 29.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: -0.08 - -Accuracy on Blind test: 0.5 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.04491043 0.01445651 0.029953 0.02912736 0.0282557 0.02754736 - 0.02681708 0.02655959 0.02590156 0.03225064] - -mean value: 0.02857792377471924 - -key: score_time -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: 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 - baseline_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: 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 - baseline_CV['Resampling'] = rs_none -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: 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 - baseline_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: 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 - baseline_BT['Resampling'] = rs_none -[0.0209837 0.02096486 0.02190661 0.02278996 0.02107406 0.02101135 - 0.02097893 0.02094507 0.02091599 0.02093077] - -mean value: 0.021250128746032715 - -key: test_mcc -value: [0.35 0.675 0.53674504 0.65714286 0.37142857 0.29277002 - 0.65714286 0.81649658 0.5976143 0.47809144] - -mean value: 0.5432431677049219 - -key: train_mcc -value: [0.90344027 0.90393035 0.94213903 0.96169772 0.98094082 0.94333502 - 0.94333502 0.94298433 0.96241667 0.94346837] - -mean value: 0.9427687593329607 - -key: test_fscore -value: [0.75 0.875 0.8 0.85714286 0.66666667 0.75 - 0.85714286 0.94117647 0.76923077 0.8 ] - -mean value: 0.8066359620771385 - -key: train_fscore -value: [0.96350365 0.96402878 0.97810219 0.98550725 0.99280576 0.9787234 - 0.9787234 0.97810219 0.98550725 0.97777778] - -mean value: 0.978278164061322 - -key: test_precision -value: [0.75 0.875 0.85714286 0.85714286 0.8 0.66666667 - 0.85714286 0.88888889 1. 0.85714286] - -mean value: 0.8409126984126984 - -key: train_precision -value: [0.95652174 0.94366197 0.97101449 0.98550725 0.98571429 0.95833333 - 0.95833333 0.97101449 0.97142857 0.98507463] - -mean value: 0.9686604093520673 - -key: test_recall -value: [0.75 0.875 0.75 0.85714286 0.57142857 0.85714286 - 0.85714286 1. 0.625 0.75 ] - -mean value: 0.7892857142857143 - -key: train_recall -value: [0.97058824 0.98529412 0.98529412 0.98550725 1. 1. - 1. 0.98529412 1. 0.97058824] - -mean value: 0.9882566069906223 - -key: test_accuracy -value: [0.69230769 0.84615385 0.76923077 0.83333333 0.66666667 0.66666667 - 0.83333333 0.91666667 0.75 0.75 ] - -mean value: 0.7724358974358975 - -key: train_accuracy -value: [0.95454545 0.95454545 0.97272727 0.98198198 0.99099099 0.97297297 - 0.97297297 0.97297297 0.98198198 0.97297297] - -mean value: 0.9728665028665029 - -key: test_roc_auc -value: [0.675 0.8375 0.775 0.82857143 0.68571429 0.62857143 - 0.82857143 0.875 0.8125 0.75 ] - -mean value: 0.7696428571428572 - -key: train_roc_auc -value: [0.94957983 0.94502801 0.96883754 0.98084886 0.98809524 0.96428571 - 0.96428571 0.96939124 0.97674419 0.97366621] - -mean value: 0.9680762547688438 - -key: test_jcc -value: [0.6 0.77777778 0.66666667 0.75 0.5 0.6 - 0.75 0.88888889 0.625 0.66666667] - -mean value: 0.6825 - -key: train_jcc -value: [0.92957746 0.93055556 0.95714286 0.97142857 0.98571429 0.95833333 - 0.95833333 0.95714286 0.97142857 0.95652174] - -mean value: 0.9576178568998532 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 60 - -mean value: 60.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.21082497 0.18535781 0.18963909 0.25137472 0.18987632 0.18474865 - 0.18461823 0.18464971 0.1901927 0.18669987] - -mean value: 0.1957982063293457 - -key: score_time -value: [0.02206898 0.01987553 0.02346182 0.02101779 0.02000642 0.02148461 - 0.02291036 0.02201867 0.02046514 0.01848912] - -mean value: 0.021179842948913574 - -key: test_mcc -value: [0.35 1. 0.53674504 0.83666003 0.37142857 0.29277002 - 1. 0.81649658 0.5976143 0.625 ] - -mean value: 0.6426714545563822 - -key: train_mcc -value: [0.98100984 0.96148459 0.94213903 0.98111593 0.98094082 0.94333502 - 0.98094082 0.94298433 1. 1. ] - -mean value: 0.9713950385172282 - -key: test_fscore -value: [0.75 1. 0.8 0.93333333 0.66666667 0.75 - 1. 0.94117647 0.76923077 0.875 ] - -mean value: 0.8485407239819004 - -key: train_fscore -value: [0.99259259 0.98529412 0.97810219 0.99270073 0.99280576 0.9787234 - 0.99280576 0.97810219 1. 1. ] - -mean value: 0.9891126734775388 - -key: test_precision -value: [0.75 1. 0.85714286 0.875 0.8 0.66666667 - 1. 0.88888889 1. 0.875 ] - -mean value: 0.8712698412698412 - -key: train_precision -value: [1. 0.98529412 0.97101449 1. 0.98571429 0.95833333 - 0.98571429 0.97101449 1. 1. ] - -mean value: 0.985708500791621 - -key: test_recall -value: [0.75 1. 0.75 1. 0.57142857 0.85714286 - 1. 1. 0.625 0.875 ] - -mean value: 0.8428571428571429 - -key: train_recall -value: [0.98529412 0.98529412 0.98529412 0.98550725 1. 1. - 1. 0.98529412 1. 1. ] - -mean value: 0.9926683716965048 - -key: test_accuracy -value: [0.69230769 1. 0.76923077 0.91666667 0.66666667 0.66666667 - 1. 0.91666667 0.75 0.83333333] - -mean value: 0.8211538461538461 - -key: train_accuracy -value: [0.99090909 0.98181818 0.97272727 0.99099099 0.99099099 0.97297297 - 0.99099099 0.97297297 1. 1. ] - -mean value: 0.9864373464373465 - -key: test_roc_auc -value: [0.675 1. 0.775 0.9 0.68571429 0.62857143 - 1. 0.875 0.8125 0.8125 ] - -mean value: 0.8164285714285715 - -key: train_roc_auc -value: [0.99264706 0.9807423 0.96883754 0.99275362 0.98809524 0.96428571 - 0.98809524 0.96939124 1. 1. ] - -mean value: 0.984484794929094 - -key: test_jcc -value: [0.6 1. 0.66666667 0.875 0.5 0.6 - 1. 0.88888889 0.625 0.77777778] - -mean value: 0.7533333333333333 - -key: train_jcc -value: [0.98529412 0.97101449 0.95714286 0.98550725 0.98571429 0.95833333 - 0.98571429 0.95714286 1. 1. ] - -mean value: 0.9785863475825114 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 64 - -mean value: 64.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.82 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - random_state=42)) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.02417588 0.02643847 0.02383518 0.02756286 0.04330134 0.04405832 - 0.02802396 0.05000472 0.04649234 0.02764344] - -mean value: 0.03415365219116211 - -key: score_time -value: [0.01154518 0.01132321 0.01134753 0.01136136 0.011343 0.01144052 - 0.02787018 0.01187468 0.01160598 0.01137733] - -mean value: 0.01310889720916748 - -key: test_mcc -value: [0.51639778 0.37796447 0.60714286 0.26189246 0.07142857 0.6000992 - 1. 0.76376262 1. 0.73214286] - -mean value: 0.5930830815501035 - -key: train_mcc -value: [0.84271225 0.88273483 0.84026462 0.82629176 0.8555278 0.87099729 - 0.82788248 0.84156943 0.85540562 0.812277 ] - -mean value: 0.8455663091863196 - -key: test_fscore -value: [0.71428571 0.66666667 0.8 0.66666667 0.53333333 0.76923077 - 1. 0.85714286 1. 0.875 ] - -mean value: 0.7882326007326007 - -key: train_fscore -value: [0.91472868 0.94029851 0.91851852 0.91044776 0.92537313 0.93233083 - 0.90769231 0.91603053 0.92424242 0.90076336] - -mean value: 0.9190426055806308 - -key: test_precision -value: [0.83333333 0.71428571 0.75 0.54545455 0.5 0.83333333 - 1. 1. 1. 0.875 ] - -mean value: 0.8051406926406927 - -key: train_precision -value: [0.96721311 0.95454545 0.93939394 0.93846154 0.95384615 0.96875 - 0.9516129 0.95238095 0.953125 0.93650794] - -mean value: 0.9515836993115879 - -key: test_recall -value: [0.625 0.625 0.85714286 0.85714286 0.57142857 0.71428571 - 1. 0.75 1. 0.875 ] - -mean value: 0.7875 - -key: train_recall -value: [0.86764706 0.92647059 0.89855072 0.88405797 0.89855072 0.89855072 - 0.86764706 0.88235294 0.89705882 0.86764706] - -mean value: 0.88885336743393 - -key: test_accuracy -value: [0.75 0.6875 0.8 0.6 0.53333333 0.8 - 1. 0.86666667 1. 0.86666667] - -mean value: 0.7904166666666667 - -key: train_accuracy -value: [0.91911765 0.94117647 0.91970803 0.91240876 0.9270073 0.93430657 - 0.91240876 0.91970803 0.9270073 0.90510949] - -mean value: 0.9217958351223701 - -key: test_roc_auc -value: [0.75 0.6875 0.80357143 0.61607143 0.53571429 0.79464286 - 1. 0.875 1. 0.86607143] - -mean value: 0.7928571428571429 - -key: train_roc_auc -value: [0.91911765 0.94117647 0.9198636 0.91261722 0.92721654 0.93456948 - 0.9120844 0.91943734 0.92679028 0.90483802] - -mean value: 0.9217710997442456 - -key: test_jcc -value: [0.55555556 0.5 0.66666667 0.5 0.36363636 0.625 - 1. 0.75 1. 0.77777778] - -mean value: 0.6738636363636364 - -key: train_jcc -value: [0.84285714 0.88732394 0.84931507 0.83561644 0.86111111 0.87323944 - 0.83098592 0.84507042 0.85915493 0.81944444] - -mean value: 0.8504118853149338 - -key: TN -value: 60 - -mean value: 60.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 60 - -mean value: 60.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.61739421 0.60889077 0.72704625 0.63255167 0.60625839 0.61627579 - 0.74641204 0.62438607 0.62583709 0.99010992] - -mean value: 0.6795162200927735 - -key: score_time -value: [0.01502943 0.01426482 0.01314068 0.01309299 0.01303506 0.01354313 - 0.01315236 0.01328683 0.01351619 0.01387477] - -mean value: 0.013593626022338868 - -key: test_mcc -value: [0.62994079 0.5 0.6000992 0.60714286 0.07142857 0.46428571 - 1. 0.87287156 0.875 0.60714286] - -mean value: 0.622791154744166 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 0.98550418 1. 1. 1. ] - -mean value: 0.9985504179288428 - -key: test_fscore -value: [0.8 0.75 0.76923077 0.8 0.53333333 0.71428571 - 1. 0.94117647 0.93333333 0.8 ] - -mean value: 0.8041359620771387 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 0.99259259 1. 1. 1. ] - -mean value: 0.9992592592592592 - -key: test_precision -value: [0.85714286 0.75 0.83333333 0.75 0.5 0.71428571 - 1. 0.88888889 1. 0.85714286] - -mean value: 0.8150793650793652 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.71428571 0.85714286 0.57142857 0.71428571 - 1. 1. 0.875 0.75 ] - -mean value: 0.7982142857142857 - -key: train_recall -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. - _warn_prf(average, modifier, msg_start, len(result)) -[1. 1. 1. 1. 1. 1. - 0.98529412 1. 1. 1. ] - -mean value: 0.9985294117647058 - -key: test_accuracy -value: [0.8125 0.75 0.8 0.8 0.53333333 0.73333333 - 1. 0.93333333 0.93333333 0.8 ] - -mean value: 0.8095833333333335 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 0.99270073 1. 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_roc_auc -value: [0.8125 0.75 0.79464286 0.80357143 0.53571429 0.73214286 - 1. 0.92857143 0.9375 0.80357143] - -mean value: 0.8098214285714285 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 0.99264706 1. 1. 1. ] - -mean value: 0.9992647058823529 - -key: test_jcc -value: [0.66666667 0.6 0.625 0.66666667 0.36363636 0.55555556 - 1. 0.88888889 0.875 0.66666667] - -mean value: 0.6908080808080809 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 0.98529412 1. 1. 1. ] - -mean value: 0.9985294117647058 - -key: TN -value: 62 - -mean value: 62.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01203299 0.0114522 0.00865173 0.00863814 0.00827718 0.00840831 - 0.0082643 0.00831389 0.00874424 0.00846148] - -mean value: 0.009124445915222167 - -key: score_time -value: [0.01137662 0.0107646 0.00874591 0.0085361 0.00905752 0.00831676 - 0.00850463 0.00908828 0.00901866 0.00841832] - -mean value: 0.0091827392578125 - -key: test_mcc -value: [-0.12598816 0.5 0.47245559 0.33928571 0.19642857 0.49099025 - 0.49099025 0.66143783 0.60714286 0.49099025] - -mean value: 0.4123733163308031 - -key: train_mcc -value: [0.50349655 0.53158595 0.60764769 0.52321152 0.56363445 0.5360985 - 0.49281415 0.54996703 0.50855421 0.52059257] - -mean value: 0.5337602617879365 - -key: test_fscore -value: [0.4 0.75 0.66666667 0.66666667 0.57142857 0.75 - 0.71428571 0.76923077 0.8 0.71428571] - -mean value: 0.6802564102564103 - -key: train_fscore -value: [0.734375 0.72268908 0.79699248 0.74418605 0.72413793 0.78082192 - 0.72440945 0.75968992 0.73015873 0.74418605] - -mean value: 0.7461646600157464 - -key: test_precision -value: [0.42857143 0.75 0.8 0.625 0.57142857 0.66666667 - 0.83333333 1. 0.85714286 0.83333333] - -mean value: 0.736547619047619 - -key: train_precision -value: [0.78333333 0.84313725 0.828125 0.8 0.89361702 0.74025974 - 0.77966102 0.80327869 0.79310345 0.78688525] - -mean value: 0.8051400749422875 - -key: test_recall -value: [0.375 0.75 0.57142857 0.71428571 0.57142857 0.85714286 - 0.625 0.625 0.75 0.625 ] - -mean value: 0.6464285714285714 - -key: train_recall -value: [0.69117647 0.63235294 0.76811594 0.69565217 0.60869565 0.82608696 - 0.67647059 0.72058824 0.67647059 0.70588235] - -mean value: 0.700149190110827 - -key: test_accuracy -value: [0.4375 0.75 0.73333333 0.66666667 0.6 0.73333333 - 0.73333333 0.8 0.8 0.73333333] - -mean value: 0.6987499999999999 - -key: train_accuracy -value: [0.75 0.75735294 0.80291971 0.75912409 0.76642336 0.76642336 - 0.74452555 0.77372263 0.75182482 0.75912409] - -mean value: 0.7631440532417345 - -key: test_roc_auc -value: [0.4375 0.75 0.72321429 0.66964286 0.59821429 0.74107143 - 0.74107143 0.8125 0.80357143 0.74107143] - -mean value: 0.7017857142857145 - -key: train_roc_auc -value: [0.75 0.75735294 0.80317562 0.75959079 0.76758312 0.76598465 - 0.7440324 0.7733376 0.75127877 0.75873828] - -mean value: 0.7631074168797956 - -key: test_jcc -value: [0.25 0.6 0.5 0.5 0.4 0.6 - 0.55555556 0.625 0.66666667 0.55555556] - -mean value: 0.5252777777777777 - -key: train_jcc -value: [0.58024691 0.56578947 0.6625 0.59259259 0.56756757 0.64044944 - 0.56790123 0.6125 0.575 0.59259259] - -mean value: 0.5957139812787359 - -key: TN -value: 57 - -mean value: 57.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.32 - -Accuracy on Blind test: 0.68 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00864267 0.00851321 0.00859857 0.00837684 0.00854611 0.00839543 - 0.00837374 0.00850129 0.00829649 0.00852156] - -mean value: 0.008476591110229493 - -key: score_time -value: [0.00828528 0.00844479 0.00827122 0.0084486 0.00835276 0.00851703 - 0.00856376 0.00837302 0.00849414 0.00842476] - -mean value: 0.008417534828186034 - -key: test_mcc -value: [ 0.28867513 0.40451992 -0.13363062 -0.07142857 0.18898224 -0.13363062 - 0.26189246 0.46770717 0.26189246 0. ] - -mean value: 0.1534979575199616 - -key: train_mcc -value: [0.47149797 0.43898087 0.47009486 0.51721526 0.49317391 0.45199806 - 0.42270073 0.48120986 0.41808005 0.42843418] - -mean value: 0.45933857270699335 - -key: test_fscore -value: [0.5 0.61538462 0.2 0.42857143 0.5 0.2 - 0.5 0.54545455 0.5 0. ] - -mean value: 0.3989410589410589 - -key: train_fscore -value: [0.63551402 0.61682243 0.64864865 0.65420561 0.62857143 0.61682243 - 0.58252427 0.62857143 0.56 0.60377358] - -mean value: 0.6175453848523136 - -key: test_precision -value: [0.75 0.8 0.33333333 0.42857143 0.6 0.33333333 - 0.75 1. 0.75 0. ] - -mean value: 0.5745238095238095 - -key: train_precision -value: [0.87179487 0.84615385 0.85714286 0.92105263 0.91666667 0.86842105 - 0.85714286 0.89189189 0.875 0.84210526] - -mean value: 0.8747371938161411 - -key: test_recall -value: [0.375 0.5 0.14285714 0.42857143 0.42857143 0.14285714 - 0.375 0.375 0.375 0. ] - -mean value: 0.3142857142857143 - -key: train_recall -value: [0.5 0.48529412 0.52173913 0.50724638 0.47826087 0.47826087 - 0.44117647 0.48529412 0.41176471 0.47058824] - -mean value: 0.47796248934356356 - -key: test_accuracy -value: [0.625 0.6875 0.46666667 0.46666667 0.6 0.46666667 - 0.6 0.66666667 0.6 0.46666667] - -mean value: 0.5645833333333333 - -key: train_accuracy -value: [0.71323529 0.69852941 0.71532847 0.72992701 0.71532847 0.70072993 - 0.68613139 0.71532847 0.67883212 0.69343066] - -mean value: 0.7046801202232718 - -key: test_roc_auc -value: [0.625 0.6875 0.44642857 0.46428571 0.58928571 0.44642857 - 0.61607143 0.6875 0.61607143 0.5 ] - -mean value: 0.5678571428571428 - -key: train_roc_auc -value: [0.71323529 0.69852941 0.71675192 0.73156436 0.71707161 0.70236573 - 0.68435635 0.71366155 0.67689685 0.69181586] - -mean value: 0.7046248934356352 - -key: test_jcc -value: [0.33333333 0.44444444 0.11111111 0.27272727 0.33333333 0.11111111 - 0.33333333 0.375 0.33333333 0. ] - -mean value: 0.26477272727272727 - -key: train_jcc -value: [0.46575342 0.44594595 0.48 0.48611111 0.45833333 0.44594595 - 0.4109589 0.45833333 0.38888889 0.43243243] - -mean value: 0.44727033197581145 - -key: TN -value: 62 - -mean value: 62.0 - -key: FP -value: 52 - -mean value: 52.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 24 - -mean value: 24.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.6 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00809407 0.00833535 0.00804353 0.00800586 0.00811768 0.00831366 - 0.00817037 0.00811768 0.00806737 0.00804043] - -mean value: 0.008130598068237304 - -key: score_time -value: [0.00945091 0.00934529 0.01366639 0.00925326 0.00923061 0.00968885 - 0.00921607 0.00923228 0.00930452 0.00933623] - -mean value: 0.009772443771362304 - -key: test_mcc -value: [ 0. 0.13483997 0.33928571 0.19642857 -0.56407607 0.04029115 - 0.36689969 0.09449112 0.19642857 0.13363062] - -mean value: 0.09382193350801989 - -key: train_mcc -value: [0.47809144 0.45083482 0.45881948 0.48879481 0.51877014 0.4190125 - 0.4870476 0.45582775 0.37256446 0.46759853] - -mean value: 0.4597361547538289 - -key: test_fscore -value: [0.33333333 0.46153846 0.66666667 0.57142857 0. 0.36363636 - 0.4 0.46153846 0.625 0.36363636] - -mean value: 0.42467782217782213 - -key: train_fscore -value: [0.70967742 0.68852459 0.68852459 0.70491803 0.72131148 0.65546218 - 0.7 0.65486726 0.62068966 0.69918699] - -mean value: 0.6843162196432879 - -key: test_precision -value: [0.5 0.6 0.625 0.57142857 0. 0.5 - 1. 0.6 0.625 0.66666667] - -mean value: 0.5688095238095239 - -key: train_precision -value: [0.78571429 0.77777778 0.79245283 0.81132075 0.83018868 0.78 - 0.80769231 0.82222222 0.75 0.78181818] - -mean value: 0.7939187039375719 - -key: test_recall -value: [0.25 0.375 0.71428571 0.57142857 0. 0.28571429 - 0.25 0.375 0.625 0.25 ] - -mean value: 0.3696428571428571 - -key: train_recall -value: [0.64705882 0.61764706 0.60869565 0.62318841 0.63768116 0.56521739 - 0.61764706 0.54411765 0.52941176 0.63235294] - -mean value: 0.6023017902813299 - -key: test_accuracy -value: [0.5 0.5625 0.66666667 0.6 0.26666667 0.53333333 - 0.6 0.53333333 0.6 0.53333333] - -mean value: 0.5395833333333333 - -key: train_accuracy -value: [0.73529412 0.72058824 0.72262774 0.73722628 0.75182482 0.70072993 - 0.73722628 0.71532847 0.67883212 0.72992701] - -mean value: 0.7229604980678402 - -key: test_roc_auc -value: [0.5 0.5625 0.66964286 0.59821429 0.25 0.51785714 - 0.625 0.54464286 0.59821429 0.55357143] - -mean value: 0.5419642857142857 - -key: train_roc_auc -value: [0.73529412 0.72058824 0.72346547 0.73806479 0.75266411 0.70172634 - 0.73635976 0.71408781 0.67774936 0.72921995] - -mean value: 0.7229219948849105 - -key: test_jcc -value: [0.2 0.3 0.5 0.4 0. 0.22222222 - 0.25 0.3 0.45454545 0.22222222] - -mean value: 0.2848989898989899 - -key: train_jcc -value: [0.55 0.525 0.525 0.5443038 0.56410256 0.4875 - 0.53846154 0.48684211 0.45 0.5375 ] - -mean value: 0.5208710005295616 - -key: TN -value: 54 - -mean value: 54.0 - -key: FP -value: 48 - -mean value: 48.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.04 - -Accuracy on Blind test: 0.52 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.0097568 0.00961161 0.00942612 0.00953341 0.00947309 0.00964904 - 0.00985408 0.00970864 0.00972199 0.00953579] - -mean value: 0.009627056121826173 - -key: score_time -value: [0.00866508 0.00860143 0.00880051 0.00888371 0.00882936 0.00949597 - 0.00867581 0.00866055 0.00863814 0.00874805] - -mean value: 0.008799862861633301 - -key: test_mcc -value: [0.37796447 0.51639778 0.6000992 0.09449112 0.07142857 0.6000992 - 0.60714286 0.33928571 0.6000992 0.6000992 ] - -mean value: 0.4407107306208915 - -key: train_mcc -value: [0.72066617 0.78152732 0.72339533 0.66616982 0.75261265 0.73758262 - 0.70910029 0.78298457 0.76762243 0.72266043] - -mean value: 0.7364321631017087 - -key: test_fscore -value: [0.66666667 0.71428571 0.76923077 0.58823529 0.53333333 0.76923077 - 0.8 0.66666667 0.82352941 0.82352941] - -mean value: 0.7154708037060977 - -key: train_fscore -value: [0.85925926 0.88549618 0.85925926 0.82706767 0.87407407 0.86764706 - 0.84848485 0.88549618 0.87878788 0.85925926] - -mean value: 0.8644831673533255 - -key: test_precision -value: [0.71428571 0.83333333 0.83333333 0.5 0.5 0.83333333 - 0.85714286 0.71428571 0.77777778 0.77777778] - -mean value: 0.7341269841269842 - -key: train_precision -value: [0.86567164 0.92063492 0.87878788 0.859375 0.89393939 0.88059701 - 0.875 0.92063492 0.90625 0.86567164] - -mean value: 0.8866562412504576 - -key: test_recall -value: [0.625 0.625 0.71428571 0.71428571 0.57142857 0.71428571 - 0.75 0.625 0.875 0.875 ] - -mean value: 0.7089285714285715 - -key: train_recall -value: [0.85294118 0.85294118 0.84057971 0.79710145 0.85507246 0.85507246 - 0.82352941 0.85294118 0.85294118 0.85294118] - -mean value: 0.8436061381074168 - -key: test_accuracy -value: [0.6875 0.75 0.8 0.53333333 0.53333333 0.8 - 0.8 0.66666667 0.8 0.8 ] - -mean value: 0.7170833333333333 - -key: train_accuracy -value: [0.86029412 0.88970588 0.86131387 0.83211679 0.87591241 0.86861314 - 0.8540146 0.89051095 0.88321168 0.86131387] - -mean value: 0.8677007299270073 - -key: test_roc_auc -value: [0.6875 0.75 0.79464286 0.54464286 0.53571429 0.79464286 - 0.80357143 0.66964286 0.79464286 0.79464286] - -mean value: 0.7169642857142857 - -key: train_roc_auc -value: [0.86029412 0.88970588 0.86146633 0.83237425 0.87606564 0.8687127 - 0.85379369 0.8902387 0.88299233 0.8612532 ] - -mean value: 0.8676896845694799 - -key: test_jcc -value: [0.5 0.55555556 0.625 0.41666667 0.36363636 0.625 - 0.66666667 0.5 0.7 0.7 ] - -mean value: 0.5652525252525253 - -key: train_jcc -value: [0.75324675 0.79452055 0.75324675 0.70512821 0.77631579 0.76623377 - 0.73684211 0.79452055 0.78378378 0.75324675] - -mean value: 0.7617085005513268 - -key: TN -value: 55 - -mean value: 55.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 54 - -mean value: 54.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.69 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.54033351 0.71709991 0.54379225 0.56754971 0.61910152 0.5835712 - 0.58603239 0.52859211 0.69978833 0.53617334] - -mean value: 0.592203426361084 - -key: score_time -value: [0.01193166 0.01185679 0.01190352 0.01194334 0.01216698 0.01190925 - 0.01196671 0.01193714 0.01922536 0.01193929] - -mean value: 0.012678003311157227 - -key: test_mcc -value: [0.25819889 0.12598816 0.75592895 0.46428571 0.09449112 0.18898224 - 0.875 0.33928571 0.6000992 0.49099025] - -mean value: 0.4193250227943669 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.57142857 0.53333333 0.83333333 0.71428571 0.58823529 0.5 - 0.93333333 0.66666667 0.82352941 0.71428571] - -mean value: 0.687843137254902 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.57142857 1. 0.71428571 0.5 0.6 - 1. 0.71428571 0.77777778 0.83333333] - -mean value: 0.7377777777777779 - -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 0.71428571 0.71428571 0.71428571 0.42857143 - 0.875 0.625 0.875 0.625 ] - -mean value: 0.6571428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.625 0.5625 0.86666667 0.73333333 0.53333333 0.6 - 0.93333333 0.66666667 0.8 0.73333333] - -mean value: 0.7054166666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.625 0.5625 0.85714286 0.73214286 0.54464286 0.58928571 - 0.9375 0.66964286 0.79464286 0.74107143] - -mean value: 0.7053571428571429 - -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 0.36363636 0.71428571 0.55555556 0.41666667 0.33333333 - 0.875 0.5 0.7 0.55555556] - -mean value: 0.5414033189033189 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 57 - -mean value: 57.0 - -key: FP -value: 26 - -mean value: 26.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 50 - -mean value: 50.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.51 - -Accuracy on Blind test: 0.77 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01494861 0.01541853 0.01285434 0.0118711 0.01083112 0.01113248 - 0.01136684 0.01161194 0.01165771 0.01203823] - -mean value: 0.012373089790344238 - -key: score_time -value: [0.01136184 0.00968146 0.00878263 0.00828695 0.00837064 0.0083673 - 0.00827742 0.00834441 0.00832987 0.00868535] - -mean value: 0.008848786354064941 - -key: test_mcc -value: [0.62994079 0.75 0.56407607 0.60714286 0.75592895 0.875 - 0.87287156 0.76376262 1. 0.73214286] - -mean value: 0.755086570024059 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.875 0.77777778 0.8 0.83333333 0.93333333 - 0.94117647 0.85714286 1. 0.875 ] - -mean value: 0.8692763772175536 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.875 0.63636364 0.75 1. 0.875 - 0.88888889 1. 1. 0.875 ] - -mean value: 0.8757395382395382 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.875 1. 0.85714286 0.71428571 1. - 1. 0.75 1. 0.875 ] - -mean value: 0.8821428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.875 0.73333333 0.8 0.86666667 0.93333333 - 0.93333333 0.86666667 1. 0.86666667] - -mean value: 0.86875 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.875 0.75 0.80357143 0.85714286 0.9375 - 0.92857143 0.875 1. 0.86607143] - -mean value: 0.8705357142857142 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.77777778 0.63636364 0.66666667 0.71428571 0.875 - 0.88888889 0.75 1. 0.77777778] - -mean value: 0.7753427128427128 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 65 - -mean value: 65.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.8 - -Accuracy on Blind test: 0.9 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.09008145 0.08893013 0.08802629 0.08733678 0.08760571 0.09081817 - 0.08841467 0.08839107 0.0875423 0.08779526] - -mean value: 0.0884941816329956 - -key: score_time -value: [0.01682448 0.01687717 0.01692629 0.01681376 0.01705241 0.07369518 - 0.01688719 0.01667404 0.01678467 0.0176034 ] - -mean value: 0.02261385917663574 - -key: test_mcc -value: [0.62994079 0.5 0.60714286 0.33928571 0.07142857 0.6000992 - 0.73214286 0.33928571 0.6000992 0.47245559] - -mean value: 0.4891880490193918 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.75 0.8 0.66666667 0.53333333 0.76923077 - 0.875 0.66666667 0.82352941 0.77777778] - -mean value: 0.7462204625439919 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.75 0.75 0.625 0.5 0.83333333 - 0.875 0.71428571 0.77777778 0.7 ] - -mean value: 0.7382539682539683 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.57142857 0.71428571 - 0.875 0.625 0.875 0.875 ] - -mean value: 0.7607142857142857 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.75 0.8 0.66666667 0.53333333 0.8 - 0.86666667 0.66666667 0.8 0.73333333] - -mean value: 0.7429166666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.75 0.80357143 0.66964286 0.53571429 0.79464286 - 0.86607143 0.66964286 0.79464286 0.72321429] - -mean value: 0.7419642857142856 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.6 0.66666667 0.5 0.36363636 0.625 - 0.77777778 0.5 0.7 0.63636364] - -mean value: 0.6036111111111111 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 55 - -mean value: 55.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.44 - -Accuracy on Blind test: 0.74 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00934315 0.00889921 0.00850725 0.00913143 0.0084269 0.00897956 - 0.0086236 0.00859261 0.00827956 0.00843573] - -mean value: 0.00872189998626709 - -key: score_time -value: [0.0093441 0.00848818 0.00834203 0.00868416 0.00830793 0.00901246 - 0.00880361 0.00855732 0.00837278 0.00827336] - -mean value: 0.008618593215942383 - -key: test_mcc -value: [ 0.37796447 0.5 -0.05455447 0.6000992 -0.18898224 -0.19642857 - -0.05455447 0.46770717 -0.21821789 -0.26189246] - -mean value: 0.09711407379095267 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.75 0.5 0.76923077 0.47058824 0.4 - 0.42857143 0.54545455 0.47058824 0.52631579] - -mean value: 0.5527415669985329 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.75 0.44444444 0.83333333 0.4 0.375 - 0.5 1. 0.44444444 0.45454545] - -mean value: 0.5916053391053391 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.75 0.57142857 0.71428571 0.57142857 0.42857143 - 0.375 0.375 0.5 0.625 ] - -mean value: 0.5535714285714286 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6875 0.75 0.46666667 0.8 0.4 0.4 - 0.46666667 0.66666667 0.4 0.4 ] - -mean value: 0.5437500000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6875 0.75 0.47321429 0.79464286 0.41071429 0.40178571 - 0.47321429 0.6875 0.39285714 0.38392857] - -mean value: 0.5455357142857143 - -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 0.6 0.33333333 0.625 0.30769231 0.25 - 0.27272727 0.375 0.30769231 0.35714286] - -mean value: 0.3928588078588079 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 41 - -mean value: 41.0 - -key: FP -value: 34 - -mean value: 34.0 - -key: FN -value: 35 - -mean value: 35.0 - -key: TP -value: 42 - -mean value: 42.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.32 - -Accuracy on Blind test: 0.66 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.17358732 1.15520883 1.15909719 1.16517234 1.1578052 1.15858912 - 1.16316414 1.17745185 1.1814363 1.18203735] - -mean value: 1.1673549652099608 - -key: score_time -value: [0.09102702 0.0880537 0.08931446 0.09419155 0.09115577 0.08767653 - 0.08834195 0.08730507 0.09308815 0.09478593] - -mean value: 0.09049401283264161 - -key: test_mcc -value: [0.62994079 0.37796447 0.49099025 0.33928571 0.33928571 0.75592895 - 0.87287156 0.6000992 0.60714286 0.75592895] - -mean value: 0.5769438451233067 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.70588235 0.75 0.66666667 0.66666667 0.83333333 - 0.94117647 0.82352941 0.8 0.88888889] - -mean value: 0.7876143790849673 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.66666667 0.66666667 0.625 0.625 1. - 0.88888889 0.77777778 0.85714286 0.8 ] - -mean value: 0.7764285714285714 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 - 1. 0.875 0.75 1. ] - -mean value: 0.8125 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.6875 0.73333333 0.66666667 0.66666667 0.86666667 - 0.93333333 0.8 0.8 0.86666667] - -mean value: 0.7833333333333332 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.6875 0.74107143 0.66964286 0.66964286 0.85714286 - 0.92857143 0.79464286 0.80357143 0.85714286] - -mean value: 0.7821428571428571 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.54545455 0.6 0.5 0.5 0.71428571 - 0.88888889 0.7 0.66666667 0.8 ] - -mean value: 0.6581962481962481 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 57 - -mean value: 57.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.87456441 0.8517611 0.87047458 0.90091586 0.85709262 0.82753348 - 0.91082954 0.8257854 0.9030807 0.85180211] - -mean value: 0.8673839807510376 - -key: score_time -value: [0.1779182 0.14194345 0.18658233 0.14119387 0.20161152 0.18412566 - 0.13825274 0.17037749 0.23327112 0.18102789] - -mean value: 0.17563042640686036 - -key: test_mcc -value: [0.62994079 0.5 0.60714286 0.33928571 0.33928571 0.75592895 - 0.87287156 0.73214286 0.73214286 0.75592895] - -mean value: 0.626467024132959 - -key: train_mcc -value: [0.91215932 0.95598573 0.89863497 0.94160273 0.94160273 0.92787101 - 0.92791659 0.92710997 0.92791659 0.92791659] - -mean value: 0.928871623370142 - -key: test_fscore -value: [0.8 0.75 0.8 0.66666667 0.66666667 0.83333333 - 0.94117647 0.875 0.875 0.88888889] - -mean value: 0.809673202614379 - -key: train_fscore -value: [0.95652174 0.97810219 0.95035461 0.97101449 0.97101449 0.96453901 - 0.96402878 0.96350365 0.96402878 0.96402878] - -mean value: 0.9647136512010267 - -key: test_precision -value: [0.85714286 0.75 0.75 0.625 0.625 1. - 0.88888889 0.875 0.875 0.8 ] - -mean value: 0.8046031746031745 - -key: train_precision -value: [0.94285714 0.97101449 0.93055556 0.97101449 0.97101449 0.94444444 - 0.94366197 0.95652174 0.94366197 0.94366197] - -mean value: 0.9518408275741406 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 - 1. 0.875 0.875 1. ] - -mean value: 0.825 - -key: train_recall -value: [0.97058824 0.98529412 0.97101449 0.97101449 0.97101449 0.98550725 - 0.98529412 0.97058824 0.98529412 0.98529412] - -mean value: 0.9780903665814151 - -key: test_accuracy -value: [0.8125 0.75 0.8 0.66666667 0.66666667 0.86666667 - 0.93333333 0.86666667 0.86666667 0.86666667] - -mean value: 0.8095833333333333 - -key: train_accuracy -value: [0.95588235 0.97794118 0.94890511 0.97080292 0.97080292 0.96350365 - 0.96350365 0.96350365 0.96350365 0.96350365] - -mean value: 0.9641852726492056 - -key: test_roc_auc -value: [0.8125 0.75 0.80357143 0.66964286 0.66964286 0.85714286 - 0.92857143 0.86607143 0.86607143 0.85714286] - -mean value: 0.8080357142857144 - -key: train_roc_auc -value: [0.95588235 0.97794118 0.94874254 0.97080136 0.97080136 0.96334186 - 0.96366155 0.96355499 0.96366155 0.96366155] - -mean value: 0.9642050298380221 - -key: test_jcc -value: [0.66666667 0.6 0.66666667 0.5 0.5 0.71428571 - 0.88888889 0.77777778 0.77777778 0.8 ] - -mean value: 0.6892063492063492 - -key: train_jcc -value: [0.91666667 0.95714286 0.90540541 0.94366197 0.94366197 0.93150685 - 0.93055556 0.92957746 0.93055556 0.93055556] - -mean value: 0.9319289853647368 - -key: TN -value: 60 - -mean value: 60.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 63 - -mean value: 63.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.04664946 0.04418659 0.04323983 0.04263473 0.04212356 0.04343629 - 0.04374647 0.04389095 0.04435182 0.04395628] - -mean value: 0.0438215970993042 - -key: score_time -value: [0.01153922 0.01023006 0.01027131 0.0100522 0.01002264 0.01016068 - 0.01012945 0.01014566 0.01025224 0.01023006] - -mean value: 0.010303354263305664 - -key: test_mcc -value: [0.62994079 0.8819171 0.49099025 0.46428571 0.6000992 0.87287156 - 0.87287156 0.875 1. 0.87287156] - -mean value: 0.7560847740334494 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.94117647 0.75 0.71428571 0.76923077 0.92307692 - 0.94117647 0.93333333 1. 0.94117647] - -mean value: 0.8713456151691445 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.88888889 0.66666667 0.71428571 0.83333333 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.8738095238095239 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 0.85714286 0.71428571 0.71428571 0.85714286 - 1. 0.875 1. 1. ] - -mean value: 0.8767857142857143 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.9375 0.73333333 0.73333333 0.8 0.93333333 - 0.93333333 0.93333333 1. 0.93333333] - -mean value: 0.875 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.9375 0.74107143 0.73214286 0.79464286 0.92857143 - 0.92857143 0.9375 1. 0.92857143] - -mean value: 0.8741071428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.88888889 0.6 0.55555556 0.625 0.85714286 - 0.88888889 0.875 1. 0.88888889] - -mean value: 0.7846031746031746 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 66 - -mean value: 66.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02408957 0.04614139 0.04688525 0.05445194 0.05765557 0.05993319 - 0.04817176 0.04807353 0.04673409 0.04642868] - -mean value: 0.047856497764587405 - -key: score_time -value: [0.01161027 0.02342272 0.01675224 0.02035213 0.02013803 0.01784778 - 0.02019763 0.02121401 0.01608253 0.02314377] - -mean value: 0.019076108932495117 - -key: test_mcc -value: [ 0.51639778 0.25 0.6000992 0.73214286 0.19642857 0.32732684 - 0.56407607 0.875 0.32732684 -0.04029115] - -mean value: 0.4348507003539204 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_fscore -value: [0.71428571 0.625 0.76923077 0.85714286 0.57142857 0.61538462 - 0.66666667 0.93333333 0.70588235 0.33333333] - -mean value: 0.6791688213747037 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_precision -value: [0.83333333 0.625 0.83333333 0.85714286 0.57142857 0.66666667 - 1. 1. 0.66666667 0.5 ] - -mean value: 0.7553571428571428 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_recall -value: [0.625 0.625 0.71428571 0.85714286 0.57142857 0.57142857 - 0.5 0.875 0.75 0.25 ] - -mean value: 0.6339285714285714 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.625 0.8 0.86666667 0.6 0.66666667 - 0.73333333 0.93333333 0.66666667 0.46666667] - -mean value: 0.7108333333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_roc_auc -value: [0.75 0.625 0.79464286 0.86607143 0.59821429 0.66071429 - 0.75 0.9375 0.66071429 0.48214286] - -mean value: 0.7124999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.99275362 1. 1. ] - -mean value: 0.9992753623188406 - -key: test_jcc -value: [0.55555556 0.45454545 0.625 0.75 0.4 0.44444444 - 0.5 0.875 0.54545455 0.2 ] - -mean value: 0.535 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: TN -value: 60 - -mean value: 60.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.73 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01173639 0.01161218 0.00877666 0.00845313 0.00894594 0.00844526 - 0.00887132 0.00845528 0.00926495 0.00925374] - -mean value: 0.009381484985351563 - -key: score_time -value: [0.01118565 0.00959563 0.00870705 0.00857997 0.00914383 0.00842357 - 0.00890827 0.00826287 0.00876474 0.00837183] - -mean value: 0.008994340896606445 - -key: test_mcc -value: [ 0.25819889 0.5 0.32732684 0.26189246 -0.07142857 0.18898224 - 0.32732684 0.49099025 0.49099025 0.6000992 ] - -mean value: 0.33743783930503735 - -key: train_mcc -value: [0.45593166 0.42762816 0.37278745 0.48902154 0.49060343 0.38686295 - 0.47659001 0.46009427 0.40162218 0.46134236] - -mean value: 0.44224840030981083 - -key: test_fscore -value: [0.57142857 0.75 0.61538462 0.66666667 0.42857143 0.5 - 0.70588235 0.71428571 0.71428571 0.82352941] - -mean value: 0.6490034475328593 - -key: train_fscore -value: [0.72592593 0.72340426 0.68148148 0.74820144 0.73684211 0.7 - 0.74647887 0.72180451 0.69172932 0.73758865] - -mean value: 0.7213456567146808 - -key: test_precision -value: [0.66666667 0.75 0.66666667 0.54545455 0.42857143 0.6 - 0.66666667 0.83333333 0.83333333 0.77777778] - -mean value: 0.6768470418470418 - -key: train_precision -value: [0.73134328 0.69863014 0.6969697 0.74285714 0.765625 0.69014085 - 0.71621622 0.73846154 0.70769231 0.71232877] - -mean value: 0.7200264934959003 - -key: test_recall -value: [0.5 0.75 0.57142857 0.85714286 0.42857143 0.42857143 - 0.75 0.625 0.625 0.875 ] - -mean value: 0.6410714285714285 - -key: train_recall -value: [0.72058824 0.75 0.66666667 0.75362319 0.71014493 0.71014493 - 0.77941176 0.70588235 0.67647059 0.76470588] - -mean value: 0.723763853367434 - -key: test_accuracy -value: [0.625 0.75 0.66666667 0.6 0.46666667 0.6 - 0.66666667 0.73333333 0.73333333 0.8 ] - -mean value: 0.6641666666666667 - -key: train_accuracy -value: [0.72794118 0.71323529 0.68613139 0.74452555 0.74452555 0.69343066 - 0.73722628 0.72992701 0.70072993 0.72992701] - -mean value: 0.7207599828252468 - -key: test_roc_auc -value: [0.625 0.75 0.66071429 0.61607143 0.46428571 0.58928571 - 0.66071429 0.74107143 0.74107143 0.79464286] - -mean value: 0.6642857142857144 - -key: train_roc_auc -value: [0.72794118 0.71323529 0.68627451 0.74445865 0.74477835 0.69330776 - 0.73753197 0.72975277 0.70055413 0.73017903] - -mean value: 0.7208013640238706 - -key: test_jcc -value: [0.4 0.6 0.44444444 0.5 0.27272727 0.33333333 - 0.54545455 0.55555556 0.55555556 0.7 ] - -mean value: 0.4907070707070707 - -key: train_jcc -value: [0.56976744 0.56666667 0.51685393 0.59770115 0.58333333 0.53846154 - 0.59550562 0.56470588 0.52873563 0.58426966] - -mean value: 0.5646000857767286 - -key: TN -value: 52 - -mean value: 52.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.46 - -Accuracy on Blind test: 0.71 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01394415 0.01133895 0.0137763 0.01342082 0.01502132 0.01487899 - 0.01419282 0.01436758 0.01505518 0.01504755] - -mean value: 0.014104366302490234 - -key: score_time -value: [0.00836349 0.00870824 0.01120782 0.01121211 0.01124024 0.01129532 - 0.01127934 0.01132226 0.01124644 0.0112648 ] - -mean value: 0.010714006423950196 - -key: test_mcc -value: [0.37796447 0.51639778 0. 0.26189246 0.19642857 0.41931393 - 1. 0.46770717 0.87287156 0.75592895] - -mean value: 0.4868504902238303 - -key: train_mcc -value: [0.78679579 0.92657079 0.41647809 0.83357169 0.98550418 0.77646542 - 0.91240409 0.60139616 0.88938138 0.76447938] - -mean value: 0.7893046959824239 - -key: test_fscore -value: [0.70588235 0.71428571 0.63636364 0.66666667 0.57142857 0.44444444 - 1. 0.54545455 0.94117647 0.88888889] - -mean value: 0.7114591291061879 - -key: train_fscore -value: [0.89473684 0.96296296 0.74193548 0.91891892 0.99280576 0.85950413 - 0.95588235 0.69230769 0.94444444 0.88311688] - -mean value: 0.8846615468295399 - -key: test_precision -value: [0.66666667 0.83333333 0.46666667 0.54545455 0.57142857 1. - 1. 1. 0.88888889 0.8 ] - -mean value: 0.7772438672438672 - -key: train_precision -value: [0.80952381 0.97014925 0.58974359 0.86075949 0.98571429 1. - 0.95588235 1. 0.89473684 0.79069767] - -mean value: 0.8857207301848959 - -key: test_recall -value: [0.75 0.625 1. 0.85714286 0.57142857 0.28571429 - 1. 0.375 1. 1. ] - -mean value: 0.7464285714285714 - -key: train_recall -value: [1. 0.95588235 1. 0.98550725 1. 0.75362319 - 0.95588235 0.52941176 1. 1. ] - -mean value: 0.9180306905370845 - -key: test_accuracy -value: [0.6875 0.75 0.46666667 0.6 0.6 0.66666667 - 1. 0.66666667 0.93333333 0.86666667] - -mean value: 0.7237500000000001 - -key: train_accuracy -value: [0.88235294 0.96323529 0.64963504 0.91240876 0.99270073 0.87591241 - 0.95620438 0.76642336 0.94160584 0.86861314] - -mean value: 0.8809091884929154 - -key: test_roc_auc -value: [0.6875 0.75 0.5 0.61607143 0.59821429 0.64285714 - 1. 0.6875 0.92857143 0.85714286] - -mean value: 0.7267857142857144 - -key: train_roc_auc -value: [0.88235294 0.96323529 0.64705882 0.91187127 0.99264706 0.87681159 - 0.95620205 0.76470588 0.94202899 0.86956522] - -mean value: 0.8806479113384483 - -key: test_jcc -value: [0.54545455 0.55555556 0.46666667 0.5 0.4 0.28571429 - 1. 0.375 0.88888889 0.8 ] - -mean value: 0.5817279942279943 - -key: train_jcc -value: [0.80952381 0.92857143 0.58974359 0.85 0.98571429 0.75362319 - 0.91549296 0.52941176 0.89473684 0.79069767] - -mean value: 0.804751554093514 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.85 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01267743 0.01273584 0.01261234 0.01352763 0.01351833 0.01287937 - 0.01380897 0.01409888 0.01330662 0.01315832] - -mean value: 0.01323237419128418 - -key: score_time -value: [0.01120472 0.01123285 0.0112319 0.0111711 0.01120114 0.0111866 - 0.01113701 0.01134038 0.01117802 0.0111804 ] - -mean value: 0.011206412315368652 - -key: test_mcc -value: [0.51639778 0.40451992 0.75592895 0.49099025 0.09449112 0.26189246 - 0.64465837 0.875 0.87287156 0.56407607] - -mean value: 0.5480826484564301 - -key: train_mcc -value: [0.85442069 0.69156407 0.82543222 0.92709446 0.82498207 0.73332722 - 0.48045763 0.89863497 0.92709446 0.72794365] - -mean value: 0.7890951434474077 - -key: test_fscore -value: [0.71428571 0.73684211 0.83333333 0.75 0.58823529 0.66666667 - 0.84210526 0.93333333 0.94117647 0.66666667] - -mean value: 0.767264484741265 - -key: train_fscore -value: [0.92857143 0.85 0.896 0.96402878 0.91390728 0.87179487 - 0.75977654 0.94736842 0.96296296 0.8173913 ] - -mean value: 0.8911801586789199 - -key: test_precision -value: [0.83333333 0.63636364 1. 0.66666667 0.5 0.54545455 - 0.72727273 1. 0.88888889 1. ] - -mean value: 0.7797979797979797 - -key: train_precision -value: [0.90277778 0.73913043 1. 0.95714286 0.84146341 0.7816092 - 0.61261261 0.96923077 0.97014925 1. ] - -mean value: 0.8774116315314415 - -key: test_recall -value: [0.625 0.875 0.71428571 0.85714286 0.71428571 0.85714286 - 1. 0.875 1. 0.5 ] - -mean value: 0.8017857142857142 - -key: train_recall -value: [0.95588235 1. 0.8115942 0.97101449 1. 0.98550725 - 1. 0.92647059 0.95588235 0.69117647] - -mean value: 0.9297527706734868 - -key: test_accuracy -value: [0.75 0.6875 0.86666667 0.73333333 0.53333333 0.6 - 0.8 0.93333333 0.93333333 0.73333333] - -mean value: 0.7570833333333333 - -key: train_accuracy -value: [0.92647059 0.82352941 0.90510949 0.96350365 0.90510949 0.8540146 - 0.68613139 0.94890511 0.96350365 0.84671533] - -mean value: 0.8822992700729927 - -key: test_roc_auc -value: [0.75 0.6875 0.85714286 0.74107143 0.54464286 0.61607143 - 0.78571429 0.9375 0.92857143 0.75 ] - -mean value: 0.7598214285714285 - -key: train_roc_auc -value: [0.92647059 0.82352941 0.9057971 0.96344842 0.90441176 0.85304774 - 0.6884058 0.94874254 0.96344842 0.84558824] - -mean value: 0.8822890025575447 - -key: test_jcc -value: [0.55555556 0.58333333 0.71428571 0.6 0.41666667 0.5 - 0.72727273 0.875 0.88888889 0.5 ] - -mean value: 0.6361002886002887 - -key: train_jcc -value: [0.86666667 0.73913043 0.8115942 0.93055556 0.84146341 0.77272727 - 0.61261261 0.9 0.92857143 0.69117647] - -mean value: 0.8094498059037077 - -key: TN -value: 54 - -mean value: 54.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.45 - -Accuracy on Blind test: 0.63 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.12404799 0.10369802 0.10437322 0.10355759 0.10415196 0.10292411 - 0.10479069 0.10424972 0.10479093 0.10378075] - -mean value: 0.10603649616241455 - -key: score_time -value: [0.01572347 0.01438046 0.01443005 0.01451278 0.01436782 0.01480412 - 0.01446128 0.01449037 0.01445508 0.01472044] - -mean value: 0.0146345853805542 - -key: test_mcc -value: [0.62994079 0.8819171 0.66143783 0.76376262 0.73214286 0.75592895 - 1. 0.76376262 1. 0.87287156] - -mean value: 0.8061764315560284 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.94117647 0.82352941 0.875 0.85714286 0.83333333 - 1. 0.85714286 1. 0.94117647] - -mean value: 0.8928501400560224 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.88888889 0.7 0.77777778 0.85714286 1. - 1. 1. 1. 0.88888889] - -mean value: 0.896984126984127 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 1. 1. 0.85714286 0.71428571 - 1. 0.75 1. 1. ] - -mean value: 0.9071428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.9375 0.8 0.86666667 0.86666667 0.86666667 - 1. 0.86666667 1. 0.93333333] - -mean value: 0.8950000000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.9375 0.8125 0.875 0.86607143 0.85714286 - 1. 0.875 1. 0.92857143] - -mean value: 0.8964285714285714 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.88888889 0.7 0.77777778 0.75 0.71428571 - 1. 0.75 1. 0.88888889] - -mean value: 0.8136507936507936 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 67 - -mean value: 67.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02649498 0.04828382 0.0344305 0.04434419 0.03334451 0.03308582 - 0.03616285 0.03474283 0.02912116 0.03139949] - -mean value: 0.03514101505279541 - -key: score_time -value: [0.01983929 0.02085829 0.02037263 0.0226109 0.03466535 0.02397752 - 0.01654959 0.02532196 0.01791954 0.01661181] - -mean value: 0.021872687339782714 - -key: test_mcc -value: [0.62994079 0.77459667 0.56407607 0.6000992 0.87287156 0.64465837 - 0.87287156 0.875 1. 0.73214286] - -mean value: 0.7566257080808041 - -key: train_mcc -value: [0.98540068 0.98540068 0.98550725 0.98550725 0.98550418 1. - 0.97080136 1. 0.97120941 1. ] - -mean value: 0.9869330800393877 - -key: test_fscore -value: [0.8 0.88888889 0.77777778 0.76923077 0.92307692 0.72727273 - 0.94117647 0.93333333 1. 0.875 ] - -mean value: 0.8635756890168654 - -key: train_fscore -value: [0.99259259 0.99270073 0.99270073 0.99270073 0.99280576 1. - 0.98529412 1. 0.98507463 1. ] - -mean value: 0.9933869282282028 - -key: test_precision -value: [0.85714286 0.8 0.63636364 0.83333333 1. 1. - 0.88888889 1. 1. 0.875 ] - -mean value: 0.8890728715728716 - -key: train_precision -value: [1. 0.98550725 1. 1. 0.98571429 1. - 0.98529412 1. 1. 1. ] - -mean value: 0.9956515649738156 - -key: test_recall -value: [0.75 1. 1. 0.71428571 0.85714286 0.57142857 - 1. 0.875 1. 0.875 ] - -mean value: 0.8642857142857142 - -key: train_recall -value: [0.98529412 1. 0.98550725 0.98550725 1. 1. - 0.98529412 1. 0.97058824 1. ] - -mean value: 0.9912190963341858 - -key: test_accuracy -value: [0.8125 0.875 0.73333333 0.8 0.93333333 0.8 - 0.93333333 0.93333333 1. 0.86666667] - -mean value: 0.86875 - -key: train_accuracy -value: [0.99264706 0.99264706 0.99270073 0.99270073 0.99270073 1. - 0.98540146 1. 0.98540146 1. ] - -mean value: 0.993419922713611 - -key: test_roc_auc -value: [0.8125 0.875 0.75 0.79464286 0.92857143 0.78571429 - 0.92857143 0.9375 1. 0.86607143] - -mean value: 0.8678571428571429 - -key: train_roc_auc -value: [0.99264706 0.99264706 0.99275362 0.99275362 0.99264706 1. - 0.98540068 1. 0.98529412 1. ] - -mean value: 0.9934143222506393 - -key: test_jcc -value: [0.66666667 0.8 0.63636364 0.625 0.85714286 0.57142857 - 0.88888889 0.875 1. 0.77777778] - -mean value: 0.7698268398268397 - -key: train_jcc -value: [0.98529412 0.98550725 0.98550725 0.98550725 0.98571429 1. - 0.97101449 1. 0.97058824 1. ] - -mean value: 0.986913287053952 - -key: TN -value: 66 - -mean value: 66.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 66 - -mean value: 66.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01622009 0.01859188 0.01931643 0.01900101 0.0448873 0.04830527 - 0.04416037 0.04434729 0.04451132 0.04420733] - -mean value: 0.03435482978820801 - -key: score_time -value: [0.01259899 0.01189089 0.01194811 0.0118506 0.02117038 0.02203035 - 0.02284622 0.02364111 0.01663518 0.02374792] - -mean value: 0.01783597469329834 - -key: test_mcc -value: [ 0.28867513 0.40451992 0.46428571 0.19642857 -0.33928571 0.07142857 - 0.46770717 0.09449112 0.33928571 0.07142857] - -mean value: 0.20589647722432364 - -key: train_mcc -value: [1. 1. 0.98550725 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_fscore -value: [0.5 0.61538462 0.71428571 0.57142857 0.28571429 0.53333333 - 0.54545455 0.46153846 0.66666667 0.53333333] - -mean value: 0.5427139527139527 - -key: train_fscore -value: [1. 1. 0.99270073 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_precision -value: [0.75 0.8 0.71428571 0.57142857 0.28571429 0.5 - 1. 0.6 0.71428571 0.57142857] - -mean value: 0.6507142857142857 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.375 0.5 0.71428571 0.57142857 0.28571429 0.57142857 - 0.375 0.375 0.625 0.5 ] - -mean value: 0.4892857142857142 - -key: train_recall -value: [1. 1. 0.98550725 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_accuracy -value: [0.625 0.6875 0.73333333 0.6 0.33333333 0.53333333 - 0.66666667 0.53333333 0.66666667 0.53333333] - -mean value: 0.5912499999999999 - -key: train_accuracy -value: [1. 1. 0.99270073 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_roc_auc -value: [0.625 0.6875 0.73214286 0.59821429 0.33035714 0.53571429 - 0.6875 0.54464286 0.66964286 0.53571429] - -mean value: 0.5946428571428573 - -key: train_roc_auc -value: [1. 1. 0.99275362 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992753623188406 - -key: test_jcc -value: [0.33333333 0.44444444 0.55555556 0.4 0.16666667 0.36363636 - 0.375 0.3 0.5 0.36363636] - -mean value: 0.38022727272727275 - -key: train_jcc -value: [1. 1. 0.98550725 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 39 - -mean value: 39.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/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: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.22 - -Accuracy on Blind test: 0.61 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.32300711 0.29841375 0.2976191 0.29853249 0.30251598 0.29544401 - 0.29952025 0.30380344 0.30649114 0.30230904] - -mean value: 0.30276563167572024 - -key: score_time -value: [0.00929809 0.00880408 0.00938535 0.00878072 0.00879741 0.00877094 - 0.00892997 0.00889921 0.00881052 0.00874591] - -mean value: 0.008922219276428223 - -key: test_mcc -value: [0.62994079 0.77459667 0.56407607 0.32732684 0.6000992 0.87287156 - 0.87287156 0.875 1. 0.87287156] - -mean value: 0.7389654248742837 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.88888889 0.77777778 0.61538462 0.76923077 0.92307692 - 0.94117647 0.93333333 1. 0.94117647] - -mean value: 0.8590045248868778 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.8 0.63636364 0.66666667 0.83333333 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.8571284271284272 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 1. 0.57142857 0.71428571 0.85714286 - 1. 0.875 1. 1. ] - -mean value: 0.8767857142857143 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.875 0.73333333 0.66666667 0.8 0.93333333 - 0.93333333 0.93333333 1. 0.93333333] - -mean value: 0.8620833333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.875 0.75 0.66071429 0.79464286 0.92857143 - 0.92857143 0.9375 1. 0.92857143] - -mean value: 0.8616071428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.8 0.63636364 0.44444444 0.625 0.85714286 - 0.88888889 0.875 1. 0.88888889] - -mean value: 0.7682395382395383 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01370049 0.0166471 0.01660919 0.01670527 0.01712155 0.01718688 - 0.01706386 0.01711583 0.01714063 0.0173409 ] - -mean value: 0.016663169860839842 - -key: score_time -value: [0.01170731 0.01148653 0.01205492 0.01254296 0.014395 0.01411581 - 0.01300907 0.01320004 0.01338458 0.01304603] - -mean value: 0.012894225120544434 - -key: test_mcc -value: [0.67419986 0.25819889 0.6000992 0.53452248 0.47245559 0.34247476 - 0.66143783 0.46770717 0.66143783 0.49099025] - -mean value: 0.5163523867066572 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.76923077 0.57142857 0.76923077 0.6 0.66666667 0.54545455 - 0.76923077 0.54545455 0.76923077 0.71428571] - -mean value: 0.6720213120213121 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.66666667 0.83333333 1. 0.8 0.75 - 1. 1. 1. 0.83333333] - -mean value: 0.8883333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.5 0.71428571 0.42857143 0.57142857 0.42857143 - 0.625 0.375 0.625 0.625 ] - -mean value: 0.5517857142857142 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.625 0.8 0.73333333 0.73333333 0.66666667 - 0.8 0.66666667 0.8 0.73333333] - -mean value: 0.7370833333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.625 0.79464286 0.71428571 0.72321429 0.65178571 - 0.8125 0.6875 0.8125 0.74107143] - -mean value: 0.7375 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.625 0.4 0.625 0.42857143 0.5 0.375 - 0.625 0.375 0.625 0.55555556] - -mean value: 0.5134126984126983 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 71 - -mean value: 71.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.61 - -Running classifier: 23 -Model_name: Ridge Classifier -Model func: RidgeClassifier(random_state=42) - -Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: 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 - smnc_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: 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 - smnc_CV['Resampling'] = rs_smnc -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: 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 - smnc_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: 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 - smnc_BT['Resampling'] = rs_smnc -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.04506564 0.0397656 0.03775096 0.02856636 0.02827263 0.03218317 - 0.03213382 0.03201056 0.03272009 0.03276658] - -mean value: 0.03412353992462158 - -key: score_time -value: [0.03038287 0.02150035 0.02133131 0.02151036 0.02205992 0.0203793 - 0.02163863 0.01988029 0.01317859 0.02244115] - -mean value: 0.021430277824401857 - -key: test_mcc -value: [0.51639778 0.51639778 0.6000992 0.60714286 0.6000992 0.6000992 - 1. 0.76376262 0.875 0.60714286] - -mean value: 0.6686141483547269 - -key: train_mcc -value: [0.98540068 0.94158382 0.97122151 0.95630861 0.97080136 0.98550418 - 0.94160273 0.92944673 0.94199209 0.95629932] - -mean value: 0.9580161023288083 - -key: test_fscore -value: [0.71428571 0.71428571 0.76923077 0.8 0.76923077 0.76923077 - 1. 0.85714286 0.93333333 0.8 ] - -mean value: 0.8126739926739928 - -key: train_fscore -value: [0.99259259 0.97014925 0.98529412 0.97810219 0.98550725 0.99280576 - 0.97058824 0.96183206 0.97014925 0.97777778] - -mean value: 0.9784798483396452 - -key: test_precision -value: [0.83333333 0.83333333 0.83333333 0.75 0.83333333 0.83333333 - 1. 1. 1. 0.85714286] - -mean value: 0.8773809523809524 - -key: train_precision -value: [1. 0.98484848 1. 0.98529412 0.98550725 0.98571429 - 0.97058824 1. 0.98484848 0.98507463] - -mean value: 0.9881875481594914 - -key: test_recall -value: [0.625 0.625 0.71428571 0.85714286 0.71428571 0.71428571 - 1. 0.75 0.875 0.75 ] - -mean value: 0.7625 - -key: train_recall -value: [0.98529412 0.95588235 0.97101449 0.97101449 0.98550725 1. - 0.97058824 0.92647059 0.95588235 0.97058824] - -mean value: 0.9692242114236999 - -key: test_accuracy -value: [0.75 0.75 0.8 0.8 0.8 0.8 - 1. 0.86666667 0.93333333 0.8 ] - -mean value: 0.8300000000000001 - -key: train_accuracy -value: [0.99264706 0.97058824 0.98540146 0.97810219 0.98540146 0.99270073 - 0.97080292 0.96350365 0.97080292 0.97810219] - -mean value: 0.9788052812365823 - -key: test_roc_auc -value: [0.75 0.75 0.79464286 0.80357143 0.79464286 0.79464286 - 1. 0.875 0.9375 0.80357143] - -mean value: 0.8303571428571429 - -key: train_roc_auc -value: [0.99264706 0.97058824 0.98550725 0.97815431 0.98540068 0.99264706 - 0.97080136 0.96323529 0.9706948 0.97804774] - -mean value: 0.9787723785166241 - -key: test_jcc -value: [0.55555556 0.55555556 0.625 0.66666667 0.625 0.625 - 1. 0.75 0.875 0.66666667] - -mean value: 0.6944444444444444 - -key: train_jcc -value: [0.98529412 0.94202899 0.97101449 0.95714286 0.97142857 0.98571429 - 0.94285714 0.92647059 0.94202899 0.95652174] - -mean value: 0.9580501765923761 - -key: TN -value: 68 - -mean value: 68.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.85 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.09967899 0.17394996 0.11551142 0.1964004 0.19705701 0.19939947 - 0.19976878 0.33203363 0.41508412 0.18141294] - -mean value: 0.21102967262268066 - -key: score_time -value: [0.01223731 0.01162314 0.02037287 0.02161789 0.0222702 0.02059126 - 0.02248669 0.02017426 0.03288436 0.01790738] - -mean value: 0.020216536521911622 - -key: test_mcc -value: [0.77459667 0.37796447 0.6000992 0.87287156 0.6000992 0.73214286 - 1. 1. 0.46428571 0.73214286] - -mean value: 0.7154202528064066 - -key: train_mcc -value: [1. 0.98540068 0.98550725 0.98550725 0.97080136 1. - 0.97080136 0.98550418 1. 0.98550418] - -mean value: 0.9869026255628051 - -key: test_fscore -value: [0.85714286 0.66666667 0.76923077 0.92307692 0.76923077 0.85714286 - 1. 1. 0.75 0.875 ] - -mean value: 0.8467490842490843 - -key: train_fscore -value: [1. 0.99259259 0.99270073 0.99270073 0.98550725 1. - 0.98529412 0.99259259 1. 0.99259259] - -mean value: 0.9933980601655662 - -key: test_precision -value: [1. 0.71428571 0.83333333 1. 0.83333333 0.85714286 - 1. 1. 0.75 0.875 ] - -mean value: 0.8863095238095239 - -key: train_precision -value: [1. 1. 1. 1. 0.98550725 1. - 0.98529412 1. 1. 1. ] - -mean value: 0.997080136402387 - -key: test_recall -value: [0.75 0.625 0.71428571 0.85714286 0.71428571 0.85714286 - 1. 1. 0.75 0.875 ] - -mean value: 0.8142857142857143 - -key: train_recall -value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 1. - 0.98529412 0.98529412 1. 0.98529412] - -mean value: 0.9897698209718669 - -key: test_accuracy -value: [0.875 0.6875 0.8 0.93333333 0.8 0.86666667 - 1. 1. 0.73333333 0.86666667] - -mean value: 0.85625 - -key: train_accuracy -value: [1. 0.99264706 0.99270073 0.99270073 0.98540146 1. - 0.98540146 0.99270073 1. 0.99270073] - -mean value: 0.9934252898239586 - -key: test_roc_auc -value: [0.875 0.6875 0.79464286 0.92857143 0.79464286 0.86607143 - 1. 1. 0.73214286 0.86607143] - -mean value: 0.8544642857142856 - -key: train_roc_auc -value: [1. 0.99264706 0.99275362 0.99275362 0.98540068 1. - 0.98540068 0.99264706 1. 0.99264706] - -mean value: 0.993424978687127 - -key: test_jcc -value: [0.75 0.5 0.625 0.85714286 0.625 0.75 - 1. 1. 0.6 0.77777778] - -mean value: 0.7484920634920635 - -key: train_jcc -value: [1. 0.98529412 0.98550725 0.98550725 0.97142857 1. - 0.97101449 0.98529412 1. 0.98529412] - -mean value: 0.9869339909876995 - -key: TN -value: 68 - -mean value: 68.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.82 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.02371693 0.03020382 0.02565432 0.02507687 0.0290494 0.03059435 - 0.02941561 0.02647495 0.02864885 0.02442336] - -mean value: 0.02732584476470947 - -key: score_time -value: [0.01136851 0.01143813 0.01154709 0.01141238 0.01138926 0.01261711 - 0.01146507 0.01139593 0.01141238 0.01151299] - -mean value: 0.011555886268615723 - -key: test_mcc -value: [0.37796447 0.51639778 0.66143783 0.60714286 0.19642857 0.53452248 - 1. 0.76376262 1. 0.73214286] - -mean value: 0.6389799465634804 - -key: train_mcc -value: [0.85331034 0.86774089 0.8687127 0.82480818 0.83951407 0.81031543 - 0.81027501 0.81092683 0.81027501 0.83951407] - -mean value: 0.8335392533265381 - -key: test_fscore -value: [0.66666667 0.71428571 0.82352941 0.8 0.57142857 0.6 - 1. 0.85714286 1. 0.875 ] - -mean value: 0.7908053221288516 - -key: train_fscore -value: [0.92753623 0.93430657 0.93430657 0.91304348 0.91970803 0.90510949 - 0.9037037 0.90225564 0.9037037 0.91970803] - -mean value: 0.9163381442781466 - -key: test_precision -value: [0.71428571 0.83333333 0.7 0.75 0.57142857 1. - 1. 1. 1. 0.875 ] - -mean value: 0.844404761904762 - -key: train_precision -value: [0.91428571 0.92753623 0.94117647 0.91304348 0.92647059 0.91176471 - 0.91044776 0.92307692 0.91044776 0.91304348] - -mean value: 0.9191293112862375 - -key: test_recall -value: [0.625 0.625 1. 0.85714286 0.57142857 0.42857143 - 1. 0.75 1. 0.875 ] - -mean value: 0.7732142857142857 - -key: train_recall -value: [0.94117647 0.94117647 0.92753623 0.91304348 0.91304348 0.89855072 - 0.89705882 0.88235294 0.89705882 0.92647059] - -mean value: 0.9137468030690536 - -key: test_accuracy -value: [0.6875 0.75 0.8 0.8 0.6 0.73333333 - 1. 0.86666667 1. 0.86666667] - -mean value: 0.8104166666666668 - -key: train_accuracy -value: [0.92647059 0.93382353 0.93430657 0.91240876 0.91970803 0.90510949 - 0.90510949 0.90510949 0.90510949 0.91970803] - -mean value: 0.9166863460712751 - -key: test_roc_auc -value: [0.6875 0.75 0.8125 0.80357143 0.59821429 0.71428571 - 1. 0.875 1. 0.86607143] - -mean value: 0.8107142857142857 - -key: train_roc_auc -value: [0.92647059 0.93382353 0.93435635 0.91240409 0.91975703 0.90515772 - 0.90505115 0.90494459 0.90505115 0.91975703] - -mean value: 0.9166773231031543 - -key: test_jcc -value: [0.5 0.55555556 0.7 0.66666667 0.4 0.42857143 - 1. 0.75 1. 0.77777778] - -mean value: 0.6778571428571428 - -key: train_jcc -value: [0.86486486 0.87671233 0.87671233 0.84 0.85135135 0.82666667 - 0.82432432 0.82191781 0.82432432 0.85135135] - -mean value: 0.8458225348636308 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.69967294 0.60888672 0.60500884 0.60262251 0.75237751 0.60629177 - 0.61903977 0.69859052 0.62684703 0.60913682] - -mean value: 0.6428474426269531 - -key: score_time -value: [0.01302028 0.014256 0.01426744 0.01292682 0.01295495 0.013134 - 0.01429248 0.01316833 0.01415896 0.01430464] - -mean value: 0.013648390769958496 - -key: test_mcc -value: [0.62994079 0.37796447 0.6000992 0.60714286 0.19642857 0.64465837 - 1. 1. 0.875 0.60714286] - -mean value: 0.6538377116441508 - -key: train_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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[1. 1. 1. 0.89863497 1. 1. - 0.97120941 1. 1. 1. ] - -mean value: 0.9869844378081876 - -key: test_fscore -value: [0.8 0.66666667 0.76923077 0.8 0.57142857 0.72727273 - 1. 1. 0.93333333 0.8 ] - -mean value: 0.8067932067932068 - -key: train_fscore -value: [1. 1. 1. 0.95035461 1. 1. - 0.98507463 1. 1. 1. ] - -mean value: 0.993542923679475 - -key: test_precision -value: [0.85714286 0.71428571 0.83333333 0.75 0.57142857 1. - 1. 1. 1. 0.85714286] - -mean value: 0.8583333333333334 - -key: train_precision -value: [1. 1. 1. 0.93055556 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9930555555555556 - -key: test_recall -value: [0.75 0.625 0.71428571 0.85714286 0.57142857 0.57142857 - 1. 1. 0.875 0.75 ] - -mean value: 0.7714285714285714 - -key: train_recall -value: [1. 1. 1. 0.97101449 1. 1. - 0.97058824 1. 1. 1. ] - -mean value: 0.994160272804774 - -key: test_accuracy -value: [0.8125 0.6875 0.8 0.8 0.6 0.8 - 1. 1. 0.93333333 0.8 ] - -mean value: 0.8233333333333335 - -key: train_accuracy -value: [1. 1. 1. 0.94890511 1. 1. - 0.98540146 1. 1. 1. ] - -mean value: 0.9934306569343067 - -key: test_roc_auc -value: [0.8125 0.6875 0.79464286 0.80357143 0.59821429 0.78571429 - 1. 1. 0.9375 0.80357143] - -mean value: 0.8223214285714284 - -key: train_roc_auc -value: [1. 1. 1. 0.94874254 1. 1. - 0.98529412 1. 1. 1. ] - -mean value: 0.9934036658141517 - -key: test_jcc -value: [0.66666667 0.5 0.625 0.66666667 0.4 0.57142857 - 1. 1. 0.875 0.66666667] - -mean value: 0.6971428571428572 - -key: train_jcc -value: [1. 1. 1. 0.90540541 1. 1. - 0.97058824 1. 1. 1. ] - -mean value: 0.9875993640699523 - -key: TN -value: 66 - -mean value: 66.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01184487 0.01161718 0.00880337 0.00857401 0.00852346 0.00843644 - 0.00820518 0.00820851 0.00827384 0.00852752] - -mean value: 0.009101438522338866 - -key: score_time -value: [0.01136661 0.01089358 0.00871873 0.00862837 0.00832224 0.00832319 - 0.00829744 0.00830674 0.00830483 0.00839972] - -mean value: 0.008956146240234376 - -key: test_mcc -value: [-0.12598816 0.37796447 0.07142857 0.37796447 0.05455447 0.18898224 - 0.60714286 0.66143783 0.49099025 0.46428571] - -mean value: 0.3168762721066597 - -key: train_mcc -value: [0.48661135 0.53311399 0.50469525 0.51832907 0.55776902 0.47754676 - 0.48902154 0.52059257 0.51856637 0.46009427] - -mean value: 0.5066340188686949 - -key: test_fscore -value: [0.4 0.70588235 0.53333333 0.70588235 0.46153846 0.5 - 0.8 0.76923077 0.71428571 0.75 ] - -mean value: 0.6340152984270632 - -key: train_fscore -value: [0.73282443 0.77777778 0.74626866 0.75912409 0.7079646 0.76433121 - 0.74074074 0.74418605 0.7518797 0.72180451] - -mean value: 0.744690175930603 - -key: test_precision -value: [0.42857143 0.66666667 0.5 0.6 0.5 0.6 - 0.85714286 1. 0.83333333 0.75 ] - -mean value: 0.6735714285714286 - -key: train_precision -value: [0.76190476 0.73684211 0.76923077 0.76470588 0.90909091 0.68181818 - 0.74626866 0.78688525 0.76923077 0.73846154] - -mean value: 0.7664438819971086 - -key: test_recall -value: [0.375 0.75 0.57142857 0.85714286 0.42857143 0.42857143 - 0.75 0.625 0.625 0.75 ] - -mean value: 0.6160714285714286 - -key: train_recall -value: [0.70588235 0.82352941 0.72463768 0.75362319 0.57971014 0.86956522 - 0.73529412 0.70588235 0.73529412 0.70588235] - -mean value: 0.7339300937766412 - -key: test_accuracy -value: [0.4375 0.6875 0.53333333 0.66666667 0.53333333 0.6 - 0.8 0.8 0.73333333 0.73333333] - -mean value: 0.6525000000000001 - -key: train_accuracy -value: [0.74264706 0.76470588 0.75182482 0.75912409 0.75912409 0.72992701 - 0.74452555 0.75912409 0.75912409 0.72992701] - -mean value: 0.7500053671103478 - -key: test_roc_auc -value: [0.4375 0.6875 0.53571429 0.67857143 0.52678571 0.58928571 - 0.80357143 0.8125 0.74107143 0.73214286] - -mean value: 0.6544642857142857 - -key: train_roc_auc -value: [0.74264706 0.76470588 0.75202472 0.75916454 0.76044331 0.72890026 - 0.74445865 0.75873828 0.75895141 0.72975277] - -mean value: 0.7499786871270249 - -key: test_jcc -value: [0.25 0.54545455 0.36363636 0.54545455 0.3 0.33333333 - 0.66666667 0.625 0.55555556 0.6 ] - -mean value: 0.478510101010101 - -key: train_jcc -value: [0.57831325 0.63636364 0.5952381 0.61176471 0.54794521 0.6185567 - 0.58823529 0.59259259 0.60240964 0.56470588] - -mean value: 0.5936125004623911 - -key: TN -value: 52 - -mean value: 52.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.41 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00870776 0.00836706 0.00858164 0.00864911 0.00872946 0.00858092 - 0.00840116 0.00910521 0.00868034 0.00934458] - -mean value: 0.008714723587036132 - -key: score_time -value: [0.00839782 0.00851846 0.00833011 0.00887752 0.00852442 0.00829554 - 0.00853753 0.00869346 0.0083499 0.00939226] - -mean value: 0.008591699600219726 - -key: test_mcc -value: [-0.37796447 0.37796447 -0.07142857 0.07142857 -0.21821789 0.53452248 - -0.04029115 0.56407607 0.25 -0.20044593] - -mean value: 0.08896435887710355 - -key: train_mcc -value: [0.34459104 0.48960131 0.42586031 0.42586031 0.40740412 0.4055104 - 0.35292142 0.42688031 0.44946013 0.45151662] - -mean value: 0.41796059682716213 - -key: test_fscore -value: [0.26666667 0.70588235 0.42857143 0.53333333 0.30769231 0.6 - 0.33333333 0.66666667 0.22222222 0.18181818] - -mean value: 0.42461864932453164 - -key: train_fscore -value: [0.63414634 0.72440945 0.67741935 0.67741935 0.67716535 0.68217054 - 0.62184874 0.66666667 0.6984127 0.69354839] - -mean value: 0.6753206888598037 - -key: test_precision -value: [0.28571429 0.66666667 0.42857143 0.5 0.33333333 1. - 0.5 1. 1. 0.33333333] - -mean value: 0.6047619047619046 - -key: train_precision -value: [0.70909091 0.77966102 0.76363636 0.76363636 0.74137931 0.73333333 - 0.7254902 0.76923077 0.75862069 0.76785714] - -mean value: 0.7511936094812467 - -key: test_recall -value: [0.25 0.75 0.42857143 0.57142857 0.28571429 0.42857143 - 0.25 0.5 0.125 0.125 ] - -mean value: 0.37142857142857144 - -key: train_recall -value: [0.57352941 0.67647059 0.60869565 0.60869565 0.62318841 0.63768116 - 0.54411765 0.58823529 0.64705882 0.63235294] - -mean value: 0.614002557544757 - -key: test_accuracy -value: [0.3125 0.6875 0.46666667 0.53333333 0.4 0.73333333 - 0.46666667 0.73333333 0.53333333 0.4 ] - -mean value: 0.5266666666666666 - -key: train_accuracy -value: [0.66911765 0.74264706 0.7080292 0.7080292 0.70072993 0.70072993 - 0.67153285 0.7080292 0.72262774 0.72262774] - -mean value: 0.7054100472305711 - -key: test_roc_auc -value: [0.3125 0.6875 0.46428571 0.53571429 0.39285714 0.71428571 - 0.48214286 0.75 0.5625 0.41964286] - -mean value: 0.5321428571428571 - -key: train_roc_auc -value: [0.66911765 0.74264706 0.70875959 0.70875959 0.70130009 0.70119352 - 0.67060955 0.70716113 0.72208014 0.72197357] - -mean value: 0.7053601875532822 - -key: test_jcc -value: [0.15384615 0.54545455 0.27272727 0.36363636 0.18181818 0.42857143 - 0.2 0.5 0.125 0.1 ] - -mean value: 0.2871053946053946 - -key: train_jcc -value: [0.46428571 0.56790123 0.51219512 0.51219512 0.51190476 0.51764706 - 0.45121951 0.5 0.53658537 0.5308642 ] - -mean value: 0.5104798089063991 - -key: TN -value: 52 - -mean value: 52.0 - -key: FP -value: 48 - -mean value: 48.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 28 - -mean value: 28.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.66 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00825047 0.0082128 0.00805974 0.00811005 0.00801778 0.00804663 - 0.00801182 0.00809121 0.00806355 0.00797033] - -mean value: 0.008083438873291016 - -key: score_time -value: [0.00938988 0.00926828 0.00917339 0.00922346 0.00917268 0.00918603 - 0.0092423 0.00932908 0.00922585 0.00921774] - -mean value: 0.009242868423461914 - -key: test_mcc -value: [ 0. -0.37796447 0.07142857 0.19642857 -0.33928571 0.02620712 - 0.07142857 0.07142857 0.07142857 -0.05455447] - -mean value: -0.026345468179303454 - -key: train_mcc -value: [0.3974458 0.38655567 0.41698711 0.43493568 0.44223491 0.34665985 - 0.40218295 0.46524407 0.34677114 0.41602728] - -mean value: 0.40550444580465417 - -key: test_fscore -value: [0.5 0.26666667 0.53333333 0.57142857 0.28571429 0.22222222 - 0.53333333 0.53333333 0.53333333 0.42857143] - -mean value: 0.44079365079365085 - -key: train_fscore -value: [0.69172932 0.66666667 0.70149254 0.69767442 0.68292683 0.65116279 - 0.6870229 0.704 0.64 0.70588235] - -mean value: 0.6828557819563523 - -key: test_precision -value: [0.5 0.28571429 0.5 0.57142857 0.28571429 0.5 - 0.57142857 0.57142857 0.57142857 0.5 ] - -mean value: 0.48571428571428565 - -key: train_precision -value: [0.70769231 0.72413793 0.72307692 0.75 0.77777778 0.7 - 0.71428571 0.77192982 0.70175439 0.70588235] - -mean value: 0.7276537217334698 - -key: test_recall -value: [0.5 0.25 0.57142857 0.57142857 0.28571429 0.14285714 - 0.5 0.5 0.5 0.375 ] - -mean value: 0.4196428571428571 - -key: train_recall -value: [0.67647059 0.61764706 0.68115942 0.65217391 0.60869565 0.60869565 - 0.66176471 0.64705882 0.58823529 0.70588235] - -mean value: 0.6447783461210571 - -key: test_accuracy -value: [0.5 0.3125 0.53333333 0.6 0.33333333 0.53333333 - 0.53333333 0.53333333 0.53333333 0.46666667] - -mean value: 0.48791666666666667 - -key: train_accuracy -value: [0.69852941 0.69117647 0.7080292 0.71532847 0.71532847 0.67153285 - 0.70072993 0.72992701 0.67153285 0.7080292 ] - -mean value: 0.7010143838557321 - -key: test_roc_auc -value: [0.5 0.3125 0.53571429 0.59821429 0.33035714 0.50892857 - 0.53571429 0.53571429 0.53571429 0.47321429] - -mean value: 0.4866071428571428 - -key: train_roc_auc -value: [0.69852941 0.69117647 0.70822677 0.71579284 0.71611253 0.67199488 - 0.70044757 0.72932651 0.67092924 0.70801364] - -mean value: 0.7010549872122762 - -key: test_jcc -value: [0.33333333 0.15384615 0.36363636 0.4 0.16666667 0.125 - 0.36363636 0.36363636 0.36363636 0.27272727] - -mean value: 0.2906118881118881 - -key: train_jcc -value: [0.52873563 0.5 0.54022989 0.53571429 0.51851852 0.48275862 - 0.52325581 0.54320988 0.47058824 0.54545455] - -mean value: 0.5188465413409199 - -key: TN -value: 42 - -mean value: 42.0 - -key: FP -value: 44 - -mean value: 44.0 - -key: FN -value: 34 - -mean value: 34.0 - -key: TP -value: 32 - -mean value: 32.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.02 - -Accuracy on Blind test: 0.53 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00958729 0.00953531 0.00956225 0.00946403 0.0095284 0.00953674 - 0.01031899 0.0094645 0.00981331 0.00961852] - -mean value: 0.009642934799194336 - -key: score_time -value: [0.00861764 0.00929141 0.00861168 0.00863266 0.00858045 0.00858712 - 0.00863266 0.00867867 0.00924301 0.00912786] - -mean value: 0.008800315856933593 - -key: test_mcc -value: [ 0.12598816 0.40451992 0.19642857 0.49099025 -0.07142857 0.41931393 - 0.60714286 0.66143783 0.49099025 0.60714286] - -mean value: 0.39325260579502824 - -key: train_mcc -value: [0.72129053 0.76770017 0.72266043 0.70801364 0.75261265 0.75369214 - 0.67983923 0.72321869 0.678815 0.69352089] - -mean value: 0.7201363365699368 - -key: test_fscore -value: [0.53333333 0.61538462 0.57142857 0.75 0.42857143 0.44444444 - 0.8 0.76923077 0.71428571 0.8 ] - -mean value: 0.6426678876678877 - -key: train_fscore -value: [0.86330935 0.88732394 0.86330935 0.85507246 0.87407407 0.88111888 - 0.83333333 0.85714286 0.83823529 0.84671533] - -mean value: 0.8599634880720005 - -key: test_precision -value: [0.57142857 0.8 0.57142857 0.66666667 0.42857143 1. - 0.85714286 1. 0.83333333 0.85714286] - -mean value: 0.7585714285714286 - -key: train_precision -value: [0.84507042 0.85135135 0.85714286 0.85507246 0.89393939 0.85135135 - 0.859375 0.87692308 0.83823529 0.84057971] - -mean value: 0.8569040921273933 - -key: test_recall -value: [0.5 0.5 0.57142857 0.85714286 0.42857143 0.28571429 - 0.75 0.625 0.625 0.75 ] - -mean value: 0.5892857142857142 - -key: train_recall -value: [0.88235294 0.92647059 0.86956522 0.85507246 0.85507246 0.91304348 - 0.80882353 0.83823529 0.83823529 0.85294118] - -mean value: 0.8639812446717817 - -key: test_accuracy -value: [0.5625 0.6875 0.6 0.73333333 0.46666667 0.66666667 - 0.8 0.8 0.73333333 0.8 ] - -mean value: 0.6849999999999999 - -key: train_accuracy -value: [0.86029412 0.88235294 0.86131387 0.8540146 0.87591241 0.87591241 - 0.83941606 0.86131387 0.83941606 0.84671533] - -mean value: 0.8596661657363676 - -key: test_roc_auc -value: [0.5625 0.6875 0.59821429 0.74107143 0.46428571 0.64285714 - 0.80357143 0.8125 0.74107143 0.80357143] - -mean value: 0.6857142857142857 - -key: train_roc_auc -value: [0.86029412 0.88235294 0.8612532 0.85400682 0.87606564 0.87563939 - 0.83919437 0.86114663 0.8394075 0.84676044] - -mean value: 0.85961210571185 - -key: test_jcc -value: [0.36363636 0.44444444 0.4 0.6 0.27272727 0.28571429 - 0.66666667 0.625 0.55555556 0.66666667] - -mean value: 0.48804112554112555 - -key: train_jcc -value: [0.75949367 0.79746835 0.75949367 0.74683544 0.77631579 0.7875 - 0.71428571 0.75 0.72151899 0.73417722] - -mean value: 0.7547088845531551 - -key: TN -value: 59 - -mean value: 59.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.68 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.5549593 0.55137181 0.55409098 0.67051911 0.5430727 0.54954982 - 0.56719279 0.6893487 0.55776834 0.52722049] - -mean value: 0.5765094041824341 - -key: score_time -value: [0.01182675 0.01185775 0.01186991 0.01202655 0.01195168 0.01197886 - 0.01199174 0.01196313 0.01197791 0.01193333] - -mean value: 0.011937761306762695 - -key: test_mcc -value: [0.37796447 0.25819889 0.46428571 0.6000992 0.33928571 0.53452248 - 0.875 0.76376262 0.73214286 0.21821789] - -mean value: 0.5163479836506467 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.57142857 0.71428571 0.76923077 0.66666667 0.6 - 0.93333333 0.85714286 0.875 0.57142857] - -mean value: 0.722518315018315 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.66666667 0.71428571 0.83333333 0.625 1. - 1. 1. 0.875 0.66666667] - -mean value: 0.8095238095238095 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.5 0.71428571 0.71428571 0.71428571 0.42857143 - 0.875 0.75 0.875 0.5 ] - -mean value: 0.6696428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6875 0.625 0.73333333 0.8 0.66666667 0.73333333 - 0.93333333 0.86666667 0.86666667 0.6 ] - -mean value: 0.75125 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6875 0.625 0.73214286 0.79464286 0.66964286 0.71428571 - 0.9375 0.875 0.86607143 0.60714286] - -mean value: 0.7508928571428573 - -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 0.4 0.55555556 0.625 0.5 0.42857143 - 0.875 0.75 0.77777778 0.4 ] - -mean value: 0.5811904761904763 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 51 - -mean value: 51.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.79 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01520491 0.01505613 0.0122366 0.01078939 0.01043081 0.01121283 - 0.01168776 0.01130247 0.01135254 0.01261473] - -mean value: 0.01218881607055664 - -key: score_time -value: [0.01149416 0.01020551 0.00834394 0.00830197 0.00831151 0.00833273 - 0.0082345 0.00830793 0.00831676 0.00827813] - -mean value: 0.008812713623046874 - -key: test_mcc -value: [0.75 0.8819171 0.76376262 0.53452248 0.87287156 1. - 0.87287156 1. 1. 0.60714286] - -mean value: 0.8283088182369814 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.875 0.94117647 0.875 0.6 0.92307692 1. - 0.94117647 1. 1. 0.8 ] - -mean value: 0.8955429864253392 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.875 0.88888889 0.77777778 1. 1. 1. - 0.88888889 1. 1. 0.85714286] - -mean value: 0.9287698412698413 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 1. 1. 0.42857143 0.85714286 1. - 1. 1. 1. 0.75 ] - -mean value: 0.8910714285714286 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.9375 0.86666667 0.73333333 0.93333333 1. - 0.93333333 1. 1. 0.8 ] - -mean value: 0.9079166666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.9375 0.875 0.71428571 0.92857143 1. - 0.92857143 1. 1. 0.80357143] - -mean value: 0.90625 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.77777778 0.88888889 0.77777778 0.42857143 0.85714286 1. - 0.88888889 1. 1. 0.66666667] - -mean value: 0.8285714285714285 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 70 - -mean value: 70.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 68 - -mean value: 68.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.08640933 0.08637953 0.08623791 0.08841944 0.08629084 0.08756542 - 0.08693004 0.08604431 0.08667326 0.0857749 ] - -mean value: 0.0866724967956543 - -key: score_time -value: [0.01676607 0.01685476 0.01676655 0.01686096 0.01674843 0.01675296 - 0.01690412 0.0167594 0.01677799 0.01685715] - -mean value: 0.016804838180541994 - -key: test_mcc -value: [0.62994079 0.5 0.49099025 0.6000992 0.07142857 0.75592895 - 0.87287156 0.66143783 0.73214286 0.75592895] - -mean value: 0.6070768948847128 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.75 0.75 0.76923077 0.53333333 0.83333333 - 0.94117647 0.76923077 0.875 0.88888889] - -mean value: 0.7910193564605329 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.75 0.66666667 0.83333333 0.5 1. - 0.88888889 1. 0.875 0.8 ] - -mean value: 0.8171031746031746 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.57142857 0.71428571 - 1. 0.625 0.875 1. ] - -mean value: 0.7857142857142857 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.75 0.73333333 0.8 0.53333333 0.86666667 - 0.93333333 0.8 0.86666667 0.86666667] - -mean value: 0.79625 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.75 0.74107143 0.79464286 0.53571429 0.85714286 - 0.92857143 0.8125 0.86607143 0.85714286] - -mean value: 0.7955357142857142 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.6 0.6 0.625 0.36363636 0.71428571 - 0.88888889 0.625 0.77777778 0.8 ] - -mean value: 0.6661255411255411 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 61 - -mean value: 61.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 60 - -mean value: 60.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.71 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00843382 0.00834179 0.00835443 0.00834417 0.00850677 0.00825882 - 0.00843382 0.00834155 0.00833392 0.00842953] - -mean value: 0.008377861976623536 - -key: score_time -value: [0.00824547 0.00827909 0.00835085 0.00823879 0.00841045 0.00824952 - 0.00834084 0.00825453 0.00825787 0.00822473] - -mean value: 0.008285212516784667 - -key: test_mcc -value: [0.62994079 0.62994079 0.34247476 0.6000992 0.47245559 0.75592895 - 0.21821789 0.76376262 0.60714286 0.46428571] - -mean value: 0.5484249149327715 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.82352941 0.8 0.54545455 0.76923077 0.66666667 0.83333333 - 0.57142857 0.85714286 0.8 0.75 ] - -mean value: 0.7416786155021449 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.77777778 0.85714286 0.75 0.83333333 0.8 1. - 0.66666667 1. 0.85714286 0.75 ] - -mean value: 0.8292063492063491 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.75 0.42857143 0.71428571 0.57142857 0.71428571 - 0.5 0.75 0.75 0.75 ] - -mean value: 0.6803571428571429 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.8125 0.66666667 0.8 0.73333333 0.86666667 - 0.6 0.86666667 0.8 0.73333333] - -mean value: 0.7691666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.8125 0.65178571 0.79464286 0.72321429 0.85714286 - 0.60714286 0.875 0.80357143 0.73214286] - -mean value: 0.7669642857142857 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.7 0.66666667 0.375 0.625 0.5 0.71428571 - 0.4 0.75 0.66666667 0.6 ] - -mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -0.5997619047619047 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 65 - -mean value: 65.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 52 - -mean value: 52.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: -0.17 - -Accuracy on Blind test: 0.45 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.11679173 1.12226534 1.13545108 1.12388873 1.12518239 1.12447619 - 1.13210273 1.13458633 1.15282416 1.13486743] - -mean value: 1.1302436113357544 - -key: score_time -value: [0.0877521 0.08782196 0.09261203 0.08724332 0.08948398 0.08766818 - 0.08709979 0.08709478 0.09637737 0.09251261] - -mean value: 0.08956661224365234 - -key: test_mcc -value: [0.62994079 0.5 0.49099025 0.64465837 0.33928571 0.64465837 - 0.87287156 0.875 0.73214286 0.75592895] - -mean value: 0.6485476862211298 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.75 0.75 0.72727273 0.66666667 0.72727273 - 0.94117647 0.93333333 0.875 0.88888889] - -mean value: 0.8059610814022579 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.75 0.66666667 1. 0.625 1. - 0.88888889 1. 0.875 0.8 ] - -mean value: 0.8462698412698414 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.57142857 0.71428571 0.57142857 - 1. 0.875 0.875 1. ] - -mean value: 0.7964285714285714 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.75 0.73333333 0.8 0.66666667 0.8 - 0.93333333 0.93333333 0.86666667 0.86666667] - -mean value: 0.8162500000000001 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.75 0.74107143 0.78571429 0.66964286 0.78571429 - 0.92857143 0.9375 0.86607143 0.85714286] - -mean value: 0.8133928571428571 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.6 0.6 0.57142857 0.5 0.57142857 - 0.88888889 0.875 0.77777778 0.8 ] - -mean value: 0.6851190476190476 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.69 - -Accuracy on Blind test: 0.85 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.84114528 0.84817362 0.81728435 0.89065385 0.85970116 0.91562819 - 0.90411329 0.85566688 0.83894706 0.84567571] - -mean value: 0.8616989374160766 - -key: score_time -value: [0.19162846 0.17003179 0.22988105 0.15647268 0.11516118 0.18131018 - 0.16762185 0.16671157 0.22042584 0.14845037] - -mean value: 0.17476949691772461 - -key: test_mcc -value: [0.5 0.62994079 0.49099025 0.60714286 0.49099025 0.53452248 - 0.87287156 0.875 0.875 0.73214286] - -mean value: 0.6608601053465211 - -key: train_mcc -value: [0.92737353 0.92737353 0.91392776 0.88654289 0.92944673 0.92787101 - 0.92791659 0.91281179 0.92791659 0.92791659] - -mean value: 0.9209097014971606 - -key: test_fscore -value: [0.75 0.8 0.75 0.8 0.75 0.6 - 0.94117647 0.93333333 0.93333333 0.875 ] - -mean value: 0.8132843137254901 - -key: train_fscore -value: [0.96402878 0.96402878 0.95774648 0.94444444 0.96503497 0.96453901 - 0.96402878 0.95652174 0.96402878 0.96402878] - -mean value: 0.9608430519467367 - -key: test_precision -value: [0.75 0.85714286 0.66666667 0.75 0.66666667 1. - 0.88888889 1. 1. 0.875 ] - -mean value: 0.8454365079365079 - -key: train_precision -value: [0.94366197 0.94366197 0.93150685 0.90666667 0.93243243 0.94444444 - 0.94366197 0.94285714 0.94366197 0.94366197] - -mean value: 0.9376217394870684 - -key: test_recall -value: [0.75 0.75 0.85714286 0.85714286 0.85714286 0.42857143 - 1. 0.875 0.875 0.875 ] - -mean value: 0.8125 - -key: train_recall -value: [0.98529412 0.98529412 0.98550725 0.98550725 1. 0.98550725 - 0.98529412 0.97058824 0.98529412 0.98529412] - -mean value: 0.9853580562659847 - -key: test_accuracy -value: [0.75 0.8125 0.73333333 0.8 0.73333333 0.73333333 - 0.93333333 0.93333333 0.93333333 0.86666667] - -mean value: 0.8229166666666666 - -key: train_accuracy -value: [0.96323529 0.96323529 0.95620438 0.94160584 0.96350365 0.96350365 - 0.96350365 0.95620438 0.96350365 0.96350365] - -mean value: 0.9598003434950624 - -key: test_roc_auc -value: [0.75 0.8125 0.74107143 0.80357143 0.74107143 0.71428571 - 0.92857143 0.9375 0.9375 0.86607143] - -mean value: 0.8232142857142858 - -key: train_roc_auc -value: [0.96323529 0.96323529 0.95598892 0.94128303 0.96323529 0.96334186 - 0.96366155 0.95630861 0.96366155 0.96366155] - -mean value: 0.9597612958226769 - -key: test_jcc -value: [0.6 0.66666667 0.6 0.66666667 0.6 0.42857143 - 0.88888889 0.875 0.875 0.77777778] - -mean value: 0.6978571428571428 - -key: train_jcc -value: [0.93055556 0.93055556 0.91891892 0.89473684 0.93243243 0.93150685 - 0.93055556 0.91666667 0.93055556 0.93055556] - -mean value: 0.9247039487216128 - -key: TN -value: 63 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -mean value: 63.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.04862261 0.04576516 0.04745841 0.04654217 0.04343677 0.04258347 - 0.04332376 0.04684305 0.04593372 0.04790926] - -mean value: 0.04584183692932129 - -key: score_time -value: [0.00996399 0.01001549 0.01016593 0.01011443 0.01044893 0.01053905 - 0.00999618 0.01000643 0.01014352 0.01008415] - -mean value: 0.010147809982299805 - -key: test_mcc -value: [0.62994079 0.8819171 0.60714286 0.87287156 0.6000992 1. - 0.87287156 0.875 1. 0.87287156] - -mean value: 0.8212714630160655 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.94117647 0.8 0.92307692 0.76923077 1. - 0.94117647 0.93333333 1. 0.94117647] - -mean value: 0.9049170437405731 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.88888889 0.75 1. 0.83333333 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.9107142857142858 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 0.85714286 0.85714286 0.71428571 1. - 1. 0.875 1. 1. ] - -mean value: 0.9053571428571429 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.9375 0.8 0.93333333 0.8 1. - 0.93333333 0.93333333 1. 0.93333333] - -mean value: 0.9083333333333334 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.9375 0.80357143 0.92857143 0.79464286 1. - 0.92857143 0.9375 1. 0.92857143] - -mean value: 0.9071428571428571 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.88888889 0.66666667 0.85714286 0.625 1. - 0.88888889 0.875 1. 0.88888889] - -mean value: 0.8357142857142857 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 69 - -mean value: 69.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.0240798 0.03598094 0.02015948 0.02077174 0.02113819 0.02149487 - 0.04542184 0.04655457 0.04637337 0.05047441] - -mean value: 0.0332449197769165 - -key: score_time -value: [0.02137375 0.01155782 0.01181102 0.01181436 0.01163363 0.01168227 - 0.02253079 0.0221858 0.02151966 0.02061629] - -mean value: 0.016672539710998534 - -key: test_mcc -value: [ 0.40451992 0.16012815 0.6000992 0.6000992 0.19642857 0.64465837 - 0.37796447 0.875 0.60714286 -0.18898224] - -mean value: 0.4277058503877337 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_fscore -value: [0.61538462 0.36363636 0.76923077 0.76923077 0.57142857 0.72727273 - 0.61538462 0.93333333 0.8 0.30769231] - -mean value: 0.6472594072594072 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_precision -value: [0.8 0.66666667 0.83333333 0.83333333 0.57142857 1. - 0.8 1. 0.85714286 0.4 ] - -mean value: 0.7761904761904762 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_recall -value: [0.5 0.25 0.71428571 0.71428571 0.57142857 0.57142857 - 0.5 0.875 0.75 0.25 ] - -mean value: 0.5696428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6875 0.5625 0.8 0.8 0.6 0.8 - 0.66666667 0.93333333 0.8 0.4 ] - -mean value: 0.705 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_roc_auc -value: [0.6875 0.5625 0.79464286 0.79464286 0.59821429 0.78571429 - 0.67857143 0.9375 0.80357143 0.41071429] - -mean value: 0.7053571428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. - 1. 0.99275362 1. 1. ] - -mean value: 0.9992753623188406 - -key: test_jcc -value: [0.44444444 0.22222222 0.625 0.625 0.4 0.57142857 - 0.44444444 0.875 0.66666667 0.18181818] - -mean value: 0.5056024531024531 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9985507246376812 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.71 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01895881 0.00925493 0.00860476 0.00828052 0.00832629 0.00839877 - 0.00822759 0.00912642 0.0082283 0.00829959] - -mean value: 0.009570598602294922 - -key: score_time -value: [0.01123643 0.00862384 0.00847077 0.00826454 0.00827861 0.00830722 - 0.00817847 0.00884295 0.00857377 0.00845981] - -mean value: 0.008723640441894531 - -key: test_mcc -value: [ 0.12598816 0.5 0.07142857 0.26189246 -0.18898224 0.20044593 - 0.46428571 0.66143783 0.76376262 0.19642857] - -mean value: 0.3056687616642674 - -key: train_mcc -value: [0.5002164 0.51520752 0.51832907 0.51856637 0.50427283 0.35764563 - 0.44729758 0.48933032 0.50362319 0.50362319] - -mean value: 0.48581120859299487 - -key: test_fscore -value: [0.53333333 0.75 0.53333333 0.66666667 0.47058824 0.4 - 0.75 0.76923077 0.85714286 0.625 ] - -mean value: 0.6355295195001076 - -key: train_fscore -value: [0.74626866 0.76258993 0.75912409 0.76595745 0.76056338 0.68571429 - 0.73239437 0.73684211 0.75 0.75 ] - -mean value: 0.7449454256630041 - -key: test_precision -value: [0.57142857 0.75 0.5 0.54545455 0.4 0.66666667 - 0.75 1. 1. 0.625 ] - -mean value: 0.6808549783549783 - -key: train_precision -value: [0.75757576 0.74647887 0.76470588 0.75 0.73972603 0.67605634 - 0.7027027 0.75384615 0.75 0.75 ] - -mean value: 0.7391091735142421 - -key: test_recall -value: [0.5 0.75 0.57142857 0.85714286 0.57142857 0.28571429 - 0.75 0.625 0.75 0.625 ] - -mean value: 0.6285714285714286 - -key: train_recall -value: [0.73529412 0.77941176 0.75362319 0.7826087 0.7826087 0.69565217 - 0.76470588 0.72058824 0.75 0.75 ] - -mean value: 0.7514492753623189 - -key: test_accuracy -value: [0.5625 0.75 0.53333333 0.6 0.4 0.6 - 0.73333333 0.8 0.86666667 0.6 ] - -mean value: 0.6445833333333333 - -key: train_accuracy -value: [0.75 0.75735294 0.75912409 0.75912409 0.75182482 0.67883212 - 0.72262774 0.74452555 0.75182482 0.75182482] - -mean value: 0.7427060970373549 - -key: test_roc_auc -value: [0.5625 0.75 0.53571429 0.61607143 0.41071429 0.58035714 - 0.73214286 0.8125 0.875 0.59821429] - -mean value: 0.6473214285714286 - -key: train_roc_auc -value: [0.75 0.75735294 0.75916454 0.75895141 0.75159847 0.67870844 - 0.72293265 0.74435209 0.75181159 0.75181159] - -mean value: 0.7426683716965046 - -key: test_jcc -value: [0.36363636 0.6 0.36363636 0.5 0.30769231 0.25 - 0.6 0.625 0.75 0.45454545] - -mean value: 0.48145104895104895 - -key: train_jcc -value: [0.5952381 0.61627907 0.61176471 0.62068966 0.61363636 0.52173913 - 0.57777778 0.58333333 0.6 0.6 ] - -mean value: 0.594045813124256 - -key: TN -value: 50 - -mean value: 50.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 48 - -mean value: 48.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.68 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01021361 0.01337481 0.01588941 0.01346612 0.01493478 0.01573634 - 0.01412511 0.01390338 0.01494479 0.01496625] - -mean value: 0.0141554594039917 - -key: score_time -value: [0.00817657 0.01119995 0.01120806 0.01116014 0.01124048 0.01114297 - 0.01117563 0.01107979 0.01117134 0.0111742 ] - -mean value: 0.010872912406921387 - -key: test_mcc -value: [0.5 0.48038446 0.66143783 0.73214286 0.19642857 0.64465837 - 0.87287156 0.46770717 0.87287156 0.64465837] - -mean value: 0.6073160755428127 - -key: train_mcc -value: [0.84942274 0.54335816 0.94199209 0.88320546 0.94201665 0.94199209 - 0.91240409 0.57874991 0.90259957 0.70581053] - -mean value: 0.8201551284685795 - -key: test_fscore -value: [0.75 0.54545455 0.82352941 0.85714286 0.57142857 0.72727273 - 0.94117647 0.54545455 0.94117647 0.84210526] - -mean value: 0.7544740862852317 - -key: train_fscore -value: [0.92517007 0.62626263 0.97142857 0.94202899 0.97058824 0.97142857 - 0.95588235 0.66666667 0.95104895 0.85534591] - -mean value: 0.8835850940554824 - -key: test_precision -value: [0.75 1. 0.7 0.85714286 0.57142857 1. - 0.88888889 1. 0.88888889 0.72727273] - -mean value: 0.8383621933621933 - -key: train_precision -value: [0.86075949 1. 0.95774648 0.94202899 0.98507463 0.95774648 - 0.95588235 1. 0.90666667 0.74725275] - -mean value: 0.9313157830650873 - -key: test_recall -value: [0.75 0.375 1. 0.85714286 0.57142857 0.57142857 - 1. 0.375 1. 1. ] - -mean value: 0.75 - -key: train_recall -value: [1. 0.45588235 0.98550725 0.94202899 0.95652174 0.98550725 - 0.95588235 0.5 1. 1. ] - -mean value: 0.8781329923273657 - -key: test_accuracy -value: [0.75 0.6875 0.8 0.86666667 0.6 0.8 - 0.93333333 0.66666667 0.93333333 0.8 ] - -mean value: 0.7837500000000001 - -key: train_accuracy -value: [0.91911765 0.72794118 0.97080292 0.94160584 0.97080292 0.97080292 - 0.95620438 0.75182482 0.94890511 0.83211679] - -mean value: 0.8990124516960067 - -key: test_roc_auc -value: [0.75 0.6875 0.8125 0.86607143 0.59821429 0.78571429 - 0.92857143 0.6875 0.92857143 0.78571429] - -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)) -0.7830357142857143 - -key: train_roc_auc -value: [0.91911765 0.72794118 0.9706948 0.94160273 0.97090793 0.9706948 - 0.95620205 0.75 0.94927536 0.83333333] - -mean value: 0.8989769820971867 - -key: test_jcc -value: [0.6 0.375 0.7 0.75 0.4 0.57142857 - 0.88888889 0.375 0.88888889 0.72727273] - -mean value: 0.6276479076479077 - -key: train_jcc -value: [0.86075949 0.45588235 0.94444444 0.89041096 0.94285714 0.94444444 - 0.91549296 0.5 0.90666667 0.74725275] - -mean value: 0.8108211208928097 - -key: TN -value: 62 - -mean value: 62.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 57 - -mean value: 57.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.7 - -Accuracy on Blind test: 0.85 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01295042 0.0128715 0.01299977 0.0135572 0.01265907 0.01324272 - 0.01365757 0.01310682 0.01281857 0.01315165] - -mean value: 0.013101530075073243 - -key: score_time -value: [0.01125312 0.01119184 0.01119018 0.01110387 0.01123881 0.011729 - 0.01153088 0.01119852 0.01162338 0.01146555] - -mean value: 0.01135251522064209 - -key: test_mcc -value: [0.37796447 0.40451992 0.25 0.875 0.25 0. - 0.87287156 0.46770717 0.25 0.6000992 ] - -mean value: 0.43481623229268634 - -key: train_mcc -value: [0.32510161 0.49770114 0.40420891 0.86939892 0.40420891 0.68283343 - 0.86339318 0.52198402 0.45246145 0.84688958] - -mean value: 0.5868181154630788 - -key: test_fscore -value: [0.72727273 0.73684211 0.66666667 0.93333333 0.66666667 0. - 0.94117647 0.54545455 0.22222222 0.82352941] - -mean value: 0.626316414923226 - -key: train_fscore -value: [0.71204188 0.76836158 0.73796791 0.93617021 0.73796791 0.77876106 - 0.93150685 0.59793814 0.50549451 0.92413793] - -mean value: 0.7630348000501478 - -key: test_precision -value: [0.57142857 0.63636364 0.5 0.875 0.5 0. - 0.88888889 1. 1. 0.77777778] - -mean value: 0.6749458874458875 - -key: train_precision -value: [0.55284553 0.62385321 0.58474576 0.91666667 0.58474576 1. - 0.87179487 1. 1. 0.87012987] - -mean value: 0.8004781673479597 - -key: test_recall -value: [1. 0.875 1. 1. 1. 0. 1. 0.375 0.125 0.875] - -mean value: 0.725 - -key: train_recall -value: [1. 1. 1. 0.95652174 1. 0.63768116 - 1. 0.42647059 0.33823529 0.98529412] - -mean value: 0.8344202898550723 - -key: test_accuracy -value: [0.625 0.6875 0.53333333 0.93333333 0.53333333 0.53333333 - 0.93333333 0.66666667 0.53333333 0.8 ] - -mean value: 0.6779166666666667 - -key: train_accuracy -value: [0.59558824 0.69852941 0.64233577 0.93430657 0.64233577 0.81751825 - 0.9270073 0.71532847 0.67153285 0.91970803] - -mean value: 0.7564190639759553 - -key: test_roc_auc -value: [0.625 0.6875 0.5625 0.9375 0.5625 0.5 - 0.92857143 0.6875 0.5625 0.79464286] - -mean value: 0.6848214285714287 - -key: train_roc_auc -value: [0.59558824 0.69852941 0.63970588 0.93414322 0.63970588 0.81884058 - 0.92753623 0.71323529 0.66911765 0.92018329] - -mean value: 0.755658567774936 - -key: test_jcc -value: [0.57142857 0.58333333 0.5 0.875 0.5 0. - 0.88888889 0.375 0.125 0.7 ] - -mean value: 0.5118650793650794 - -key: train_jcc -value: [0.55284553 0.62385321 0.58474576 0.88 0.58474576 0.63768116 - 0.87179487 0.42647059 0.33823529 0.85897436] - -mean value: 0.6359346537430649 - -key: TN -value: 48 - -mean value: 48.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 28 - -mean value: 28.0 - -key: TP -value: 55 - -mean value: 55.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.11194253 0.09431767 0.09566665 0.09451294 0.09525967 0.0946734 - 0.09667301 0.0985446 0.09776855 0.09758306] - -mean value: 0.09769420623779297 - -key: score_time -value: [0.0144887 0.01460576 0.01471305 0.01483321 0.01478577 0.01461482 - 0.01462436 0.01519608 0.01608753 0.01459646] - -mean value: 0.01485457420349121 - -key: test_mcc -value: [0.62994079 0.8819171 0.73214286 1. 0.73214286 1. - 0.87287156 0.875 1. 0.87287156] - -mean value: 0.8596886728210563 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.94117647 0.85714286 1. 0.85714286 1. - 0.94117647 0.93333333 1. 0.94117647] - -mean value: 0.9271148459383755 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.88888889 0.85714286 1. 0.85714286 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.9238095238095239 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 0.85714286 1. 0.85714286 1. - 1. 0.875 1. 1. ] - -mean value: 0.9339285714285716 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.9375 0.86666667 1. 0.86666667 1. - 0.93333333 0.93333333 1. 0.93333333] - -mean value: 0.9283333333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.9375 0.86607143 1. 0.86607143 1. - 0.92857143 0.9375 1. 0.92857143] - -mean value: 0.9276785714285716 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.88888889 0.75 1. 0.75 1. - 0.88888889 0.875 1. 0.88888889] - -mean value: 0.8708333333333333 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 70 - -mean value: 70.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 71 - -mean value: 71.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.84 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03070569 0.03946638 0.03145647 0.03729653 0.0305562 0.03059769 - 0.02873802 0.04426146 0.03538823 0.02899194] - -mean value: 0.033745861053466795 - -key: score_time -value: [0.02230477 0.01676178 0.0195477 0.02490187 0.01605606 0.01720977 - 0.0185101 0.01946902 0.02183986 0.0229888 ] - -mean value: 0.019958972930908203 - -key: test_mcc -value: [0.62994079 0.77459667 0.76376262 0.87287156 0.87287156 0.75592895 - 0.87287156 1. 0.875 0.87287156] - -mean value: 0.8290715263210501 - -key: train_mcc -value: [0.97100831 1. 0.98550725 1. 1. 1. - 1. 1. 0.98550418 1. ] - -mean value: 0.9942019738120464 - -key: test_fscore -value: [0.8 0.88888889 0.875 0.92307692 0.92307692 0.83333333 - 0.94117647 1. 0.93333333 0.94117647] - -mean value: 0.9059062342885873 - -key: train_fscore -value: [0.98507463 1. 0.99270073 1. 1. 1. - 1. 1. 0.99259259 1. ] - -mean value: 0.9970367949385273 - -key: test_precision -value: [0.85714286 0.8 0.77777778 1. 1. 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.9212698412698412 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 1. 0.85714286 0.85714286 0.71428571 - 1. 1. 0.875 1. ] - -mean value: 0.9053571428571429 - -key: train_recall -value: [0.97058824 1. 0.98550725 1. 1. 1. - 1. 1. 0.98529412 1. ] - -mean value: 0.9941389599317988 - -key: test_accuracy -value: [0.8125 0.875 0.86666667 0.93333333 0.93333333 0.86666667 - 0.93333333 1. 0.93333333 0.93333333] - -mean value: 0.9087500000000001 - -key: train_accuracy -value: [0.98529412 1. 0.99270073 1. 1. 1. - 1. 1. 0.99270073 1. ] - -mean value: 0.9970695577501074 - -key: test_roc_auc -value: [0.8125 0.875 0.875 0.92857143 0.92857143 0.85714286 - 0.92857143 1. 0.9375 0.92857143] - -mean value: 0.9071428571428571 - -key: train_roc_auc -value: [0.98529412 1. 0.99275362 1. 1. 1. - 1. 1. 0.99264706 1. ] - -mean value: 0.9970694799658993 - -key: test_jcc -value: [0.66666667 0.8 0.77777778 0.85714286 0.85714286 0.71428571 - 0.88888889 1. 0.875 0.88888889] - -mean value: 0.8325793650793651 - -key: train_jcc -value: [0.97058824 1. 0.98550725 1. 1. 1. - 1. 1. 0.98529412 1. ] - -mean value: 0.9941389599317988 - -key: TN -value: 69 - -mean value: 69.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.9 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.03822041 0.04516506 0.04575491 0.04620242 0.04413915 0.04986811 - 0.06049204 0.0440228 0.04267597 0.04133439] - -mean value: 0.04578752517700195 - -key: score_time -value: [0.01998234 0.02082443 0.02234125 0.02065015 0.02187395 0.02372122 - 0.02151942 0.02155709 0.0215466 0.02156043] - -mean value: 0.02155768871307373 - -key: test_mcc -value: [ 0.51639778 0.12598816 0.46428571 0.47245559 -0.21821789 0.64465837 - 0.49099025 0.49099025 0.66143783 0.19642857] - -mean value: 0.384541462895231 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.71428571 0.58823529 0.71428571 0.66666667 0.30769231 0.72727273 - 0.71428571 0.71428571 0.76923077 0.625 ] - -mean value: 0.6541240622122976 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.55555556 0.71428571 0.8 0.33333333 1. - 0.83333333 0.83333333 1. 0.625 ] - -mean value: 0.7528174603174603 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.625 0.71428571 0.57142857 0.28571429 0.57142857 - 0.625 0.625 0.625 0.625 ] - -mean value: 0.5892857142857142 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.5625 0.73333333 0.73333333 0.4 0.8 - 0.73333333 0.73333333 0.8 0.6 ] - -mean value: 0.6845833333333333 - -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 0.5625 0.73214286 0.72321429 0.39285714 0.78571429 - 0.74107143 0.74107143 0.8125 0.59821429] - -mean value: 0.6839285714285714 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.55555556 0.41666667 0.55555556 0.5 0.18181818 0.57142857 - 0.55555556 0.55555556 0.625 0.45454545] - -mean value: 0.49716810966810965 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 59 - -mean value: 59.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.09 - -Accuracy on Blind test: 0.56 - -Running classifier: 21 -Model_name: Gradient Boosting -Model func: GradientBoostingClassifier(random_state=42) - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.28157949 0.25451016 0.25529194 0.25148749 0.25579357 0.25624275 - 0.25024486 0.27509332 0.26206994 0.25641561] - -mean value: 0.2598729133605957 - -key: score_time -value: [0.0089767 0.00908971 0.00900173 0.00886941 0.00891423 0.00886917 - 0.0089736 0.00901198 0.00910449 0.00886345] - -mean value: 0.008967447280883788 - -key: test_mcc -value: [0.75 0.77459667 0.76376262 0.87287156 0.6000992 0.87287156 - 0.87287156 0.76376262 1. 0.87287156] - -mean value: 0.8143707342818287 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.875 0.88888889 0.875 0.92307692 0.76923077 0.92307692 - 0.94117647 0.85714286 1. 0.94117647] - -mean value: 0.8993769302592831 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.875 0.8 0.77777778 1. 0.83333333 1. - 0.88888889 1. 1. 0.88888889] - -mean value: 0.906388888888889 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 1. 1. 0.85714286 0.71428571 0.85714286 - 1. 0.75 1. 1. ] - -mean value: 0.9053571428571429 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.875 0.86666667 0.93333333 0.8 0.93333333 - 0.93333333 0.86666667 1. 0.93333333] - -mean value: 0.9016666666666666 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.875 0.875 0.92857143 0.79464286 0.92857143 - 0.92857143 0.875 1. 0.92857143] - -mean value: 0.9008928571428572 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.77777778 0.8 0.77777778 0.85714286 0.625 0.85714286 - 0.88888889 0.75 1. 0.88888889] - -mean value: 0.8222619047619049 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 68 - -mean value: 68.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 69 - -mean value: 69.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.9 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.0164001 0.01641011 0.01619625 0.016886 0.01677108 0.01692677 - 0.01703286 0.01680374 0.01673031 0.01696873] - -mean value: 0.016712594032287597 - -key: score_time -value: [0.01169324 0.01153493 0.01181769 0.01264262 0.01444697 0.01417255 - 0.01461315 0.01426792 0.01169634 0.01290679] - -mean value: 0.012979221343994141 - -key: test_mcc -value: [0.57735027 0.57735027 0.46770717 0.875 0.56407607 1. - 0.64465837 0.87287156 0.75592895 0.64465837] - -mean value: 0.6979601035946793 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.73684211 0.93333333 0.77777778 1. - 0.84210526 0.94117647 0.88888889 0.84210526] - -mean value: 0.8562229102167184 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.66666667 0.66666667 0.58333333 0.875 0.63636364 1. - 0.72727273 0.88888889 0.8 0.72727273] - -mean value: 0.7571464646464647 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.75 0.66666667 0.93333333 0.73333333 1. - 0.8 0.93333333 0.86666667 0.8 ] - -mean value: 0.8233333333333335 - -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 0.6875 0.9375 0.75 1. - 0.78571429 0.92857143 0.85714286 0.78571429] - -mean value: 0.8232142857142858 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.66666667 0.58333333 0.875 0.63636364 1. - 0.72727273 0.88888889 0.8 0.72727273] - -mean value: 0.7571464646464647 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 49 - -mean value: 49.0 - -key: FP -value: 0 - -mean value: 0.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 76 - -mean value: 76.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.61 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.0328958 0.03519583 0.06664515 0.04126024 0.04941845 0.05472493 - 0.04746461 0.03544927 0.03318977 0.06595039] - -mean value: 0.04621944427490234 - -key: score_time -value: [0.01581264 0.02167654 0.01969385 0.03978634 0.02318883 0.02819037 - 0.02565694 0.02331448 0.02085543 0.01781631] - -mean value: 0.02359917163848877 -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: 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 - ros_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: 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 - ros_CV['Resampling'] = rs_ros -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: 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 - ros_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: 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 - ros_BT['Resampling'] = rs_ros - -key: test_mcc -value: [0.51639778 0.25819889 0.60714286 0.73214286 0.6000992 0.64465837 - 1. 0.875 0.875 0.73214286] - -mean value: 0.6840782810039336 - -key: train_mcc -value: [0.94158382 0.94117647 0.94160273 0.92709446 0.97080136 0.95629932 - 0.94160273 0.95629932 0.94160273 0.95630861] - -mean value: 0.9474371541021677 - -key: test_fscore -value: [0.71428571 0.57142857 0.8 0.85714286 0.76923077 0.72727273 - 1. 0.93333333 0.93333333 0.875 ] - -mean value: 0.8181027306027306 - -key: train_fscore -value: [0.97101449 0.97058824 0.97101449 0.96402878 0.98550725 0.97841727 - 0.97058824 0.97777778 0.97058824 0.97810219] - -mean value: 0.9737626948490679 - -key: test_precision -value: [0.83333333 0.66666667 0.75 0.85714286 0.83333333 1. - 1. 1. 1. 0.875 ] - -mean value: 0.881547619047619 - -key: train_precision -value: [0.95714286 0.97058824 0.97101449 0.95714286 0.98550725 0.97142857 - 0.97058824 0.98507463 0.97058824 0.97101449] - -mean value: 0.9710089850346367 - -key: test_recall -value: [0.625 0.5 0.85714286 0.85714286 0.71428571 0.57142857 - 1. 0.875 0.875 0.875 ] - -mean value: 0.775 - -key: train_recall -value: [0.98529412 0.97058824 0.97101449 0.97101449 0.98550725 0.98550725 - 0.97058824 0.97058824 0.97058824 0.98529412] - -mean value: 0.9765984654731458 - -key: test_accuracy -value: [0.75 0.625 0.8 0.86666667 0.8 0.8 - 1. 0.93333333 0.93333333 0.86666667] - -mean value: 0.8375 - -key: train_accuracy -value: [0.97058824 0.97058824 0.97080292 0.96350365 0.98540146 0.97810219 - 0.97080292 0.97810219 0.97080292 0.97810219] - -mean value: 0.9736796908544442 - -key: test_roc_auc -value: [0.75 0.625 0.80357143 0.86607143 0.79464286 0.78571429 - 1. 0.9375 0.9375 0.86607143] - -mean value: 0.8366071428571429 - -key: train_roc_auc -value: [0.97058824 0.97058824 0.97080136 0.96344842 0.98540068 0.97804774 - 0.97080136 0.97804774 0.97080136 0.97815431] - -mean value: 0.9736679454390453 - -key: test_jcc -value: [0.55555556 0.4 0.66666667 0.75 0.625 0.57142857 - 1. 0.875 0.875 0.77777778] - -mean value: 0.709642857142857 - -key: train_jcc -value: [0.94366197 0.94285714 0.94366197 0.93055556 0.97142857 0.95774648 - 0.94285714 0.95652174 0.94285714 0.95714286] - -mean value: 0.9489290574364059 - -key: TN -value: 68 - -mean value: 68.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 59 - -mean value: 59.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.84 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.19917345 0.19675255 0.19733953 0.19775033 0.19791675 0.19742084 - 0.19664717 0.21271873 0.26473117 0.19704652] - -mean value: 0.2057497024536133 - -key: score_time -value: [0.02243996 0.02309203 0.0232439 0.02306867 0.02281857 0.023067 - 0.01989818 0.02087092 0.02154446 0.02196884] - -mean value: 0.02220125198364258 - -key: test_mcc -value: [0.77459667 0.25819889 0.6000992 0.87287156 0.6000992 0.75592895 - 1. 0.875 0.875 0.73214286] - -mean value: 0.7343937319391883 - -key: train_mcc -value: [1. 0.98540068 0.98550725 0.98550725 0.98550725 0.98550725 - 0.98550418 0.95629932 0.98550418 0.95630861] - -mean value: 0.9811045948988539 - -key: test_fscore -value: [0.85714286 0.57142857 0.76923077 0.92307692 0.76923077 0.83333333 - 1. 0.93333333 0.93333333 0.875 ] - -mean value: 0.846510989010989 - -key: train_fscore -value: [1. 0.99259259 0.99270073 0.99270073 0.99270073 0.99270073 - 0.99259259 0.97777778 0.99259259 0.97810219] - -mean value: 0.9904460665044608 - -key: test_precision -value: [1. 0.66666667 0.83333333 1. 0.83333333 1. - 1. 1. 1. 0.875 ] - -mean value: 0.9208333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.98507463 1. 0.97101449] - -mean value: 0.9956089119619295 - -key: test_recall -value: [0.75 0.5 0.71428571 0.85714286 0.71428571 0.71428571 - 1. 0.875 0.875 0.875 ] - -mean value: 0.7875 - -key: train_recall -value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 0.98550725 - 0.98529412 0.97058824 0.98529412 0.98529412] - -mean value: 0.9853793691389597 - -key: test_accuracy -value: [0.875 0.625 0.8 0.93333333 0.8 0.86666667 - 1. 0.93333333 0.93333333 0.86666667] - -mean value: 0.8633333333333335 - -key: train_accuracy -value: [1. 0.99264706 0.99270073 0.99270073 0.99270073 0.99270073 - 0.99270073 0.97810219 0.99270073 0.97810219] - -mean value: 0.9905055817947618 - -key: test_roc_auc -value: [0.875 0.625 0.79464286 0.92857143 0.79464286 0.85714286 - 1. 0.9375 0.9375 0.86607143] - -mean value: 0.8616071428571429 - -key: train_roc_auc -value: [1. 0.99264706 0.99275362 0.99275362 0.99275362 0.99275362 - 0.99264706 0.97804774 0.99264706 0.97815431] - -mean value: 0.9905157715260018 - -key: test_jcc -value: [0.75 0.4 0.625 0.85714286 0.625 0.71428571 - 1. 0.875 0.875 0.77777778] - -mean value: 0.7499206349206349 - -key: train_jcc -value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 0.98550725 - 0.98529412 0.95652174 0.98529412 0.95714286] - -mean value: 0.9811575934721715 - -key: TN -value: 71 - -mean value: 71.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 60 - -mean value: 60.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.82 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - random_state=42)) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.02283621 0.02274132 0.0230844 0.02192616 0.02395177 0.02656484 - 0.02280498 0.02494693 0.02449894 0.02404141] - -mean value: 0.02373969554901123 - -key: score_time -value: [0.01147294 0.0114243 0.01145148 0.01142192 0.01138258 0.01138496 - 0.0114224 0.01137829 0.01136613 0.01135755] - -mean value: 0.011406254768371583 - -key: test_mcc -value: [ 0.65465367 0. 0.40824829 0.40824829 0.1 -0.05976143 - 0.1 0.31622777 0.55 0.55 ] - -mean value: 0.30276165871858207 - -key: train_mcc -value: [0.79134188 0.81322028 0.85811633 0.81322028 0.90586932 0.88254861 - 0.81556009 0.79343933 0.8151574 0.83722687] - -mean value: 0.8325700404796367 - -key: test_fscore -value: [0.83333333 0.44444444 0.66666667 0.72727273 0.5 0.54545455 - 0.5 0.72727273 0.8 0.8 ] - -mean value: 0.6544444444444444 - -key: train_fscore -value: [0.88607595 0.9 0.92682927 0.9 0.95348837 0.94252874 - 0.90243902 0.88607595 0.9 0.91358025] - -mean value: 0.9111017546055893 - -key: test_precision -value: [0.71428571 0.5 0.75 0.66666667 0.5 0.42857143 - 0.5 0.66666667 0.8 0.8 ] - -mean value: 0.6326190476190476 - -key: train_precision -value: [0.94594595 0.94736842 0.95 0.94736842 0.95348837 0.93181818 - 0.94871795 0.94594595 0.94736842 0.94871795] - -mean value: 0.9466739606396889 - -key: test_recall -value: [1. 0.4 0.6 0.8 0.5 0.75 0.5 0.8 0.8 0.8 ] - -mean value: 0.695 - -key: train_recall -value: [0.83333333 0.85714286 0.9047619 0.85714286 0.95348837 0.95348837 - 0.86046512 0.83333333 0.85714286 0.88095238] - -mean value: 0.879125138427464 - -key: test_accuracy -value: [0.8 0.5 0.7 0.7 0.55555556 0.44444444 - 0.55555556 0.66666667 0.77777778 0.77777778] - -mean value: 0.6477777777777778 - -key: train_accuracy -value: [0.89285714 0.9047619 0.92857143 0.9047619 0.95294118 0.94117647 - 0.90588235 0.89411765 0.90588235 0.91764706] - -mean value: 0.9148599439775911 - -key: test_roc_auc -value: [0.8 0.5 0.7 0.7 0.55 0.475 0.55 0.65 0.775 0.775] - -mean value: 0.6475000000000001 - -key: train_roc_auc -value: [0.89285714 0.9047619 0.92857143 0.9047619 0.95293466 0.9410299 - 0.90642303 0.89341085 0.90531561 0.91722038] - -mean value: 0.9147286821705425 - -key: test_jcc -value: [0.71428571 0.28571429 0.5 0.57142857 0.33333333 0.375 - 0.33333333 0.57142857 0.66666667 0.66666667] - -mean value: 0.5017857142857143 - -key: train_jcc -value: [0.79545455 0.81818182 0.86363636 0.81818182 0.91111111 0.89130435 - 0.82222222 0.79545455 0.81818182 0.84090909] - -mean value: 0.837463768115942 - -key: TN -value: 28 - -mean value: 28.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 33 - -mean value: 33.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.76 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.53838086 0.66656113 0.56348276 0.54039288 0.59699011 0.64962125 - 0.55005503 0.54683185 0.61618948 0.53688884] - -mean value: 0.5805394172668457 - -key: score_time -value: [0.01186943 0.01767612 0.01306176 0.01297593 0.01463532 0.0142684 - 0.01302242 0.01305795 0.01307464 0.0128808 ] - -mean value: 0.013652276992797852 - -key: test_mcc -value: [ 0.65465367 0.21821789 0.40824829 0.81649658 -0.1 0.55 - 0.31622777 0.79056942 0.35 0.35 ] - -mean value: 0.4354413613394491 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.83333333 0.5 0.66666667 0.90909091 0.44444444 0.75 - 0.57142857 0.90909091 0.66666667 0.66666667] - -mean value: 0.6917388167388167 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.66666667 0.75 0.83333333 0.4 0.75 - 0.66666667 0.83333333 0.75 0.75 ] - -mean value: 0.7114285714285714 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.4 0.6 1. 0.5 0.75 0.5 1. 0.6 0.6 ] - -mean value: 0.695 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.6 0.7 0.9 0.44444444 0.77777778 - 0.66666667 0.88888889 0.66666667 0.66666667] - -mean value: 0.7111111111111111 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.6 0.7 0.9 0.45 0.775 0.65 0.875 0.675 0.675] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/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)) - -mean value: 0.71 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.71428571 0.33333333 0.5 0.83333333 0.28571429 0.6 - 0.4 0.83333333 0.5 0.5 ] - -mean value: 0.55 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 34 - -mean value: 34.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 33 - -mean value: 33.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.57 - -Accuracy on Blind test: 0.79 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01164794 0.01129961 0.009058 0.00929093 0.00825739 0.00817442 - 0.00800943 0.00845218 0.00890684 0.00797081] - -mean value: 0.009106755256652832 - -key: score_time -value: [0.01142001 0.01111174 0.00943184 0.008775 0.00926685 0.00886226 - 0.00826621 0.00894976 0.00857258 0.00840521] - -mean value: 0.00930614471435547 - -key: test_mcc -value: [ 0.21821789 0.65465367 0.2 0.21821789 -0.1 -0.05976143 - 0.31622777 -0.1 0.05976143 0. ] - -mean value: 0.14073172171968001 - -key: train_mcc -value: [0.47574393 0.49471786 0.69542165 0.52620136 0.53873283 0.6199864 - 0.52960905 0.48301723 0.51376105 0.50286789] - -mean value: 0.5380059259650506 - -key: test_fscore -value: [0.66666667 0.83333333 0.6 0.66666667 0.44444444 0.54545455 - 0.66666667 0.44444444 0.66666667 0.71428571] - -mean value: 0.6248629148629149 - -key: train_fscore -value: [0.7628866 0.77227723 0.83544304 0.75 0.79207921 0.82474227 - 0.78787879 0.76470588 0.76923077 0.77227723] - -mean value: 0.7831521006782899 - -key: test_precision -value: [0.57142857 0.71428571 0.6 0.57142857 0.4 0.42857143 - 0.5 0.5 0.57142857 0.55555556] - -mean value: 0.5412698412698412 - -key: train_precision -value: [0.67272727 0.66101695 0.89189189 0.78947368 0.68965517 0.74074074 - 0.69642857 0.65 0.71428571 0.66101695] - -mean value: 0.7167236946003597 - -key: test_recall -value: [0.8 1. 0.6 0.8 0.5 0.75 1. 0.4 0.8 1. ] - -mean value: 0.7649999999999999 - -key: train_recall -value: [0.88095238 0.92857143 0.78571429 0.71428571 0.93023256 0.93023256 - 0.90697674 0.92857143 0.83333333 0.92857143] - -mean value: 0.8767441860465116 - -key: test_accuracy -value: [0.6 0.8 0.6 0.6 0.44444444 0.44444444 - 0.55555556 0.44444444 0.55555556 0.55555556] - -mean value: 0.5599999999999999 - -key: train_accuracy -value: [0.72619048 0.72619048 0.8452381 0.76190476 0.75294118 0.8 - 0.75294118 0.71764706 0.75294118 0.72941176] - -mean value: 0.7565406162464986 - -key: test_roc_auc -value: [0.6 0.8 0.6 0.6 0.45 0.475 0.6 0.45 0.525 0.5 ] - -mean value: 0.56 - -key: train_roc_auc -value: [0.72619048 0.72619048 0.8452381 0.76190476 0.75083056 0.79844961 - 0.75110742 0.72009967 0.75387597 0.73172757] - -mean value: 0.7565614617940198 - -key: test_jcc -value: [0.5 0.71428571 0.42857143 0.5 0.28571429 0.375 - 0.5 0.28571429 0.5 0.55555556] - -mean value: 0.464484126984127 - -key: train_jcc -value: [0.61666667 0.62903226 0.7173913 0.6 0.6557377 0.70175439 - 0.65 0.61904762 0.625 0.62903226] - -mean value: 0.6443662197074088 - -key: TN -value: 17 - -mean value: 17.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 30 - -mean value: 30.0 - -key: TP -value: 36 - -mean value: 36.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.32 - -Accuracy on Blind test: 0.69 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00824237 0.00883603 0.00824642 0.00817323 0.00879383 0.00821328 - 0.00828743 0.00824833 0.00819349 0.00816178] - -mean value: 0.008339619636535645 - -key: score_time -value: [0.00865197 0.00939441 0.00830483 0.00840235 0.00835133 0.00827479 - 0.00831819 0.00830507 0.00837803 0.00836182] - -mean value: 0.008474278450012206 - -key: test_mcc -value: [ 0. -0.21821789 0.21821789 0. -0.47809144 -0.55 - -0.35 -0.47809144 -0.5976143 -0.31622777] - -mean value: -0.277002495815155 - -key: train_mcc -value: [0.48507125 0.31391444 0.36220897 0.4843221 0.56230818 0.46270205 - 0.52510205 0.49481869 0.4465631 0.52960905] - -mean value: 0.46666198870701453 - -key: test_fscore -value: [0. 0.25 0.66666667 0.44444444 0. 0.22222222 - 0.25 0.5 0. 0.25 ] - -mean value: 0.25833333333333336 - -key: train_fscore -value: [0.71052632 0.62337662 0.64935065 0.66666667 0.72222222 0.71604938 - 0.72 0.7027027 0.62686567 0.70422535] - -mean value: 0.6841985586578854 - -key: test_precision -value: [0. 0.33333333 0.57142857 0.5 0. 0.2 - 0.25 0.42857143 0. 0.33333333] - -mean value: 0.26166666666666666 - -key: train_precision -value: [0.79411765 0.68571429 0.71428571 0.85185185 0.89655172 0.76315789 - 0.84375 0.8125 0.84 0.86206897] - -mean value: 0.806399808330269 - -key: test_recall -value: [0. 0.2 0.8 0.4 0. 0.25 0.25 0.6 0. 0.2 ] - -mean value: 0.27 - -key: train_recall -value: [0.64285714 0.57142857 0.5952381 0.54761905 0.60465116 0.6744186 - 0.62790698 0.61904762 0.5 0.5952381 ] - -mean value: 0.5978405315614618 - -key: test_accuracy -value: [0.5 0.4 0.6 0.5 0.33333333 0.22222222 - 0.33333333 0.33333333 0.22222222 0.33333333] - -mean value: 0.3777777777777778 - -key: train_accuracy -value: [0.73809524 0.6547619 0.67857143 0.72619048 0.76470588 0.72941176 - 0.75294118 0.74117647 0.70588235 0.75294118] - -mean value: 0.7244677871148459 - -key: test_roc_auc -value: [0.5 0.4 0.6 0.5 0.3 0.225 0.325 0.3 0.25 0.35 ] - -mean value: 0.375 - -key: train_roc_auc -value: [0.73809524 0.6547619 0.67857143 0.72619048 0.7666113 0.73006645 - 0.75442968 0.73975637 0.70348837 0.75110742] - -mean value: 0.7243078626799556 - -key: test_jcc -value: [0. 0.14285714 0.5 0.28571429 0. 0.125 - 0.14285714 0.33333333 0. 0.14285714] - -mean value: 0.16726190476190475 - -key: train_jcc -value: [0.55102041 0.45283019 0.48076923 0.5 0.56521739 0.55769231 - 0.5625 0.54166667 0.45652174 0.54347826] - -mean value: 0.5211696193275064 - -key: TN -value: 23 - -mean value: 23.0 - -key: FP -value: 34 - -mean value: 34.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 13 - -mean value: 13.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.68 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00804973 0.00821543 0.00890303 0.00834084 0.00842309 0.00840545 - 0.00832963 0.00788832 0.00874853 0.0088377 ] - -mean value: 0.008414173126220703 - -key: score_time -value: [0.01010227 0.00930381 0.00980425 0.00989151 0.01001334 0.00915027 - 0.00909543 0.00902271 0.00962782 0.00973582] - -mean value: 0.00957472324371338 - -key: test_mcc -value: [ 0. 0. -0.21821789 0. -0.55 -0.1 - -0.31622777 0.15811388 0.35 0.47809144] - -mean value: -0.019824032951065405 - -key: train_mcc -value: [0.38138504 0.35724413 0.48112522 0.33333333 0.53019399 0.38920425 - 0.41163441 0.46034451 0.36500203 0.34182831] - -mean value: 0.4051295218916732 - -key: test_fscore -value: [0.44444444 0.28571429 0.5 0.28571429 0.22222222 0.44444444 - 0.4 0.5 0.66666667 0.57142857] - -mean value: 0.43206349206349215 - -key: train_fscore -value: [0.69767442 0.6746988 0.71794872 0.66666667 0.76190476 0.71111111 - 0.71264368 0.73563218 0.66666667 0.6744186 ] - -mean value: 0.7019365604803427 - -key: test_precision -value: [0.5 0.5 0.42857143 0.5 0.2 0.4 - 0.33333333 0.66666667 0.75 1. ] - -mean value: 0.5278571428571428 - -key: train_precision -value: [0.68181818 0.68292683 0.77777778 0.66666667 0.7804878 0.68085106 - 0.70454545 0.71111111 0.69230769 0.65909091] - -mean value: 0.7037583491293923 - -key: test_recall -value: [0.4 0.2 0.6 0.2 0.25 0.5 0.5 0.4 0.6 0.4 ] - -mean value: 0.40499999999999997 - -key: train_recall -value: [0.71428571 0.66666667 0.66666667 0.66666667 0.74418605 0.74418605 - 0.72093023 0.76190476 0.64285714 0.69047619] - -mean value: 0.7018826135105206 - -key: test_accuracy -value: [0.5 0.5 0.4 0.5 0.22222222 0.44444444 - 0.33333333 0.55555556 0.66666667 0.66666667] - -mean value: 0.47888888888888886 - -key: train_accuracy -value: [0.69047619 0.67857143 0.73809524 0.66666667 0.76470588 0.69411765 - 0.70588235 0.72941176 0.68235294 0.67058824] - -mean value: 0.7020868347338937 - -key: test_roc_auc -value: [0.5 0.5 0.4 0.5 0.225 0.45 0.35 0.575 0.675 0.7 ] - -mean value: 0.4875 - -key: train_roc_auc -value: [0.69047619 0.67857143 0.73809524 0.66666667 0.76495017 0.69352159 - 0.70570321 0.72978959 0.68189369 0.67081949] - -mean value: 0.7020487264673312 - -key: test_jcc -value: [0.28571429 0.16666667 0.33333333 0.16666667 0.125 0.28571429 - 0.25 0.33333333 0.5 0.4 ] - -mean value: 0.2846428571428571 - -key: train_jcc -value: [0.53571429 0.50909091 0.56 0.5 0.61538462 0.55172414 - 0.55357143 0.58181818 0.5 0.50877193] - -mean value: 0.5416075488335017 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 19 - -mean value: 19.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.17 - -Accuracy on Blind test: 0.58 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00887966 0.00883842 0.00862217 0.00868559 0.00890541 0.00862718 - 0.00858402 0.00897694 0.00865579 0.00867534] - -mean value: 0.008745050430297852 - -key: score_time -value: [0.00847769 0.0083859 0.008461 0.00841331 0.00859261 0.00839782 - 0.0084157 0.00859141 0.00845885 0.00843167] - -mean value: 0.00846259593963623 - -key: test_mcc -value: [ 0. 0.40824829 0.40824829 0. 0.1 0.15811388 - -0.1 0.1 0.31622777 0.63245553] - -mean value: 0.2023293761986659 - -key: train_mcc -value: [0.61904762 0.69224438 0.66742381 0.59540689 0.76782927 0.71824836 - 0.55290867 0.62437683 0.64833694 0.64709151] - -mean value: 0.6532914275382986 - -key: test_fscore -value: [0.44444444 0.72727273 0.66666667 0.61538462 0.5 0.6 - 0.44444444 0.6 0.72727273 0.75 ] - -mean value: 0.6075485625485626 - -key: train_fscore -value: [0.80952381 0.83950617 0.8372093 0.8 0.88888889 0.86363636 - 0.7816092 0.81395349 0.81481481 0.81927711] - -mean value: 0.8268419144237091 - -key: test_precision -value: [0.5 0.66666667 0.75 0.5 0.5 0.5 - 0.4 0.6 0.66666667 1. ] - -mean value: 0.6083333333333333 - -key: train_precision -value: [0.80952381 0.87179487 0.81818182 0.79069767 0.85106383 0.84444444 - 0.77272727 0.79545455 0.84615385 0.82926829] - -mean value: 0.8229310405169373 - -key: test_recall -value: [0.4 0.8 0.6 0.8 0.5 0.75 0.5 0.6 0.8 0.6 ] - -mean value: 0.635 - -key: train_recall -value: [0.80952381 0.80952381 0.85714286 0.80952381 0.93023256 0.88372093 - 0.79069767 0.83333333 0.78571429 0.80952381] - -mean value: 0.8318936877076413 - -key: test_accuracy -value: [0.5 0.7 0.7 0.5 0.55555556 0.55555556 - 0.44444444 0.55555556 0.66666667 0.77777778] - -mean value: 0.5955555555555556 - -key: train_accuracy -value: [0.80952381 0.8452381 0.83333333 0.79761905 0.88235294 0.85882353 - 0.77647059 0.81176471 0.82352941 0.82352941] - -mean value: 0.8262184873949578 - -key: test_roc_auc -value: [0.5 0.7 0.7 0.5 0.55 0.575 0.45 0.55 0.65 0.8 ] - -mean value: 0.5975 - -key: train_roc_auc -value: [0.80952381 0.8452381 0.83333333 0.79761905 0.88178295 0.85852713 - 0.77630122 0.8120155 0.8230897 0.82336656] - -mean value: 0.8260797342192691 - -key: test_jcc -value: [0.28571429 0.57142857 0.5 0.44444444 0.33333333 0.42857143 - 0.28571429 0.42857143 0.57142857 0.6 ] - -mean value: 0.4449206349206348 - -key: train_jcc -value: [0.68 0.72340426 0.72 0.66666667 0.8 0.76 - 0.64150943 0.68627451 0.6875 0.69387755] - -mean value: 0.705923241677241 - -key: TN -value: 26 - -mean value: 26.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 30 - -mean value: 30.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.35 - -Accuracy on Blind test: 0.66 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.43665123 0.46531892 0.39793062 0.38484907 0.37270451 0.38749909 - 0.40523052 0.50272679 0.37766552 0.39441299] - -mean value: 0.412498927116394 - -key: score_time -value: [0.01195264 0.01200628 0.01183558 0.01191711 0.01194119 0.01192427 - 0.01196218 0.01190591 0.01190948 0.01190758] - -mean value: 0.011926221847534179 - -key: test_mcc -value: [ 0.5 0. 0.21821789 0.40824829 -0.31622777 -0.1 - -0.63245553 0.5976143 -0.31622777 0.31622777] - -mean value: 0.06753971873165382 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.76923077 0.28571429 0.5 0.72727273 0.4 0.44444444 - 0. 0.83333333 0.25 0.72727273] - -mean value: 0.49372682872682877 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.625 0.5 0.66666667 0.66666667 0.33333333 0.4 - 0. 0.71428571 0.33333333 0.66666667] - -mean value: 0.4905952380952381 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.2 0.4 0.8 0.5 0.5 0. 1. 0.2 0.8] - -mean value: 0.54 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.7 0.5 0.6 0.7 0.33333333 0.44444444 - 0.22222222 0.77777778 0.33333333 0.66666667] - -mean value: 0.5277777777777778 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.7 0.5 0.6 0.7 0.35 0.45 0.2 0.75 0.35 0.65] - -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: [0.625 0.16666667 0.33333333 0.57142857 0.25 0.28571429 - 0. 0.71428571 0.14285714 0.57142857] - -mean value: 0.36607142857142855 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 26 - -mean value: 26.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.35 - -Accuracy on Blind test: 0.69 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01370859 0.0131712 0.01013899 0.01002789 0.00981402 0.00985694 - 0.00986934 0.01002073 0.0107832 0.01089931] - -mean value: 0.010829019546508788 - -key: score_time -value: [0.01132679 0.01054096 0.00878572 0.00834441 0.00832295 0.00824118 - 0.00826597 0.00855398 0.00896335 0.00902319] - -mean value: 0.009036850929260255 - -key: test_mcc -value: [0.81649658 0.81649658 0.5 1. 0.5976143 0.79056942 - 0.55 1. 0.8 0.55 ] - -mean value: 0.7421176881564744 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.90909091 0.57142857 1. 0.66666667 0.85714286 - 0.75 1. 0.88888889 0.8 ] - -mean value: 0.8332106782106783 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 1. 1. 1. 1. - 0.75 1. 1. 0.8 ] - -mean value: 0.9383333333333335 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 1. 0.4 1. 0.5 0.75 0.75 1. 0.8 0.8 ] - -mean value: 0.78 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 0.7 1. 0.77777778 0.88888889 - 0.77777778 1. 0.88888889 0.77777778] - -mean value: 0.8611111111111113 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 0.7 1. 0.75 0.875 0.775 1. 0.9 0.775] - -mean value: 0.8575000000000002 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.83333333 0.4 1. 0.5 0.75 - 0.6 1. 0.8 0.66666667] - -mean value: 0.735 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 44 - -mean value: 44.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 3 - -mean value: 3.0 - -key: TP -value: 37 - -mean value: 37.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.84 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.08355451 0.08239985 0.08116961 0.0823493 0.08216691 0.0857296 - 0.08329606 0.08283973 0.08216929 0.08263755] - -mean value: 0.08283123970031739 - -key: score_time -value: [0.01656103 0.01668882 0.01673579 0.01669979 0.01810622 0.01679373 - 0.01664114 0.01679635 0.01673222 0.01815653] - -mean value: 0.016991162300109865 - -key: test_mcc -value: [ 0.21821789 0.65465367 0. 0.65465367 -0.1 -0.1 - -0.35 0.55 -0.05976143 0.1 ] - -mean value: 0.1567763801185227 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.83333333 0.44444444 0.83333333 0.44444444 0.44444444 - 0.25 0.8 0.28571429 0.6 ] - -mean value: 0.5602380952380952 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.57142857 0.71428571 0.5 0.71428571 0.4 0.4 - 0.25 0.8 0.5 0.6 ] - -mean value: 0.5449999999999999 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 1. 0.4 1. 0.5 0.5 0.25 0.8 0.2 0.6 ] - -mean value: 0.605 - -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 0.5 0.8 0.44444444 0.44444444 - 0.33333333 0.77777778 0.44444444 0.55555556] - -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: [0.6 0.8 0.5 0.8 0.45 0.45 0.325 0.775 0.475 0.55 ] - -mean value: 0.5725 - -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 0.71428571 0.28571429 0.71428571 0.28571429 0.28571429 - 0.14285714 0.66666667 0.16666667 0.42857143] - -mean value: 0.41904761904761906 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 25 - -mean value: 25.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 29 - -mean value: 29.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.29 - -Accuracy on Blind test: 0.65 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.0083909 0.0083034 0.00807333 0.00824189 0.00825071 0.00829339 - 0.00813794 0.0088129 0.00833988 0.00830221] - -mean value: 0.00831465721130371 - -key: score_time -value: [0.00840092 0.00839686 0.0083375 0.00847769 0.00835156 0.00837564 - 0.0086894 0.00898671 0.00903249 0.00888491] - -mean value: 0.008593368530273437 - -key: test_mcc -value: [ 0.21821789 -0.2 0. -0.40824829 -0.15811388 0.15811388 - -0.35 0.15811388 0.15811388 -0.35 ] - -mean value: -0.07738026342110325 - -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 0.54545455 0.36363636 0.28571429 0.6 - 0.25 0.5 0.5 0.4 ] - -mean value: 0.45114718614718613 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.57142857 0.4 0.5 0.33333333 0.33333333 0.5 - 0.25 0.66666667 0.66666667 0.4 ] - -mean value: 0.4621428571428572 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 0.4 0.6 0.4 0.25 0.75 0.25 0.4 0.4 0.4 ] - -mean value: 0.465 - -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 0.5 0.3 0.44444444 0.55555556 - 0.33333333 0.55555556 0.55555556 0.33333333] - -mean value: 0.45777777777777773 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6 0.4 0.5 0.3 0.425 0.575 0.325 0.575 0.575 0.325] - -mean value: 0.4600000000000001 - -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 0.375 0.22222222 0.16666667 0.42857143 - 0.14285714 0.33333333 0.33333333 0.25 ] - -mean value: 0.3001984126984127 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 21 - -mean value: 21.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 22 - -mean value: 22.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.65 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.06693983 1.04320121 1.05023813 1.05610228 1.05119729 1.10538578 - 1.0328548 1.04281378 1.02934623 1.02944255] - -mean value: 1.0507521867752074 - -key: score_time -value: [0.10711551 0.09000683 0.09520698 0.09461856 0.09211016 0.09304357 - 0.08741426 0.09318328 0.08647776 0.08651567] - -mean value: 0.09256925582885742 - -key: test_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/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -[ 0.81649658 0.65465367 0.21821789 0.81649658 0.31622777 -0.1 - 0.05976143 0.79056942 0.35 0.31622777] - -mean value: 0.42386511003419114 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.83333333 0.5 0.90909091 0.57142857 0.44444444 - 0.33333333 0.90909091 0.66666667 0.72727273] - -mean value: 0.6803751803751804 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.71428571 0.66666667 0.83333333 0.66666667 0.4 - 0.5 0.83333333 0.75 0.66666667] - -mean value: 0.6864285714285715 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.4 1. 0.5 0.5 0.25 1. 0.6 0.8 ] - -mean value: 0.705 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.8 0.6 0.9 0.66666667 0.44444444 - 0.55555556 0.88888889 0.66666667 0.66666667] - -mean value: 0.7088888888888889 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.8 0.6 0.9 0.65 0.45 0.525 0.875 0.675 0.65 ] - -mean value: 0.7025 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.71428571 0.33333333 0.83333333 0.4 0.28571429 - 0.2 0.83333333 0.5 0.57142857] - -mean value: 0.5504761904761905 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 34 - -mean value: 34.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.53 - -Accuracy on Blind test: 0.77 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.8055284 0.83201861 0.82570887 0.8074472 0.82467222 0.85785246 - 0.80040121 0.82698226 0.83134413 0.83202553] - -mean value: 0.8243980884552002 - -key: score_time -value: [0.18204045 0.19004273 0.14946604 0.1972661 0.15155721 0.19060373 - 0.16753435 0.19431329 0.16234851 0.18571186] - -mean value: 0.17708842754364013 - -key: test_mcc -value: [ 0.6 0.65465367 0.65465367 0.65465367 0.31622777 0.15811388 - -0.15811388 0.79056942 0.35 0.79056942] - -mean value: 0.4811327608224959 - -key: train_mcc -value: [0.85811633 0.90578946 0.9047619 0.85811633 0.88444936 0.88444936 - 0.90586932 0.90586932 0.83545719 0.88261351] - -mean value: 0.8825492092714203 - -key: test_fscore -value: [0.8 0.83333333 0.75 0.83333333 0.57142857 0.6 - 0.28571429 0.90909091 0.66666667 0.90909091] - -mean value: 0.7158658008658008 - -key: train_fscore -value: [0.93023256 0.95348837 0.95238095 0.93023256 0.94382022 0.94382022 - 0.95348837 0.95238095 0.91566265 0.94117647] - -mean value: 0.9416683335855869 - -key: test_precision -value: [0.8 0.71428571 1. 0.71428571 0.66666667 0.5 - 0.33333333 0.83333333 0.75 0.83333333] - -mean value: 0.7145238095238095 - -key: train_precision -value: [0.90909091 0.93181818 0.95238095 0.90909091 0.91304348 0.91304348 - 0.95348837 0.95238095 0.92682927 0.93023256] - -mean value: 0.9291399059808885 - -key: test_recall -value: [0.8 1. 0.6 1. 0.5 0.75 0.25 1. 0.6 1. ] - -mean value: 0.75 - -key: train_recall -value: [0.95238095 0.97619048 0.95238095 0.95238095 0.97674419 0.97674419 - 0.95348837 0.95238095 0.9047619 0.95238095] - -mean value: 0.9549833887043189 - -key: test_accuracy -value: [0.8 0.8 0.8 0.8 0.66666667 0.55555556 - 0.44444444 0.88888889 0.66666667 0.88888889] - -mean value: 0.7311111111111112 - -key: train_accuracy -value: [0.92857143 0.95238095 0.95238095 0.92857143 0.94117647 0.94117647 - 0.95294118 0.95294118 0.91764706 0.94117647] - -mean value: 0.9408963585434174 - -key: test_roc_auc -value: [0.8 0.8 0.8 0.8 0.65 0.575 0.425 0.875 0.675 0.875] - -mean value: 0.7275 - -key: train_roc_auc -value: [0.92857143 0.95238095 0.95238095 0.92857143 0.94075305 0.94075305 - 0.95293466 0.95293466 0.91749723 0.94130676] - -mean value: 0.9408084163898115 - -key: test_jcc -value: [0.66666667 0.71428571 0.6 0.71428571 0.4 0.42857143 - 0.16666667 0.83333333 0.5 0.83333333] - -mean value: 0.5857142857142856 - -key: train_jcc -value: [0.86956522 0.91111111 0.90909091 0.86956522 0.89361702 0.89361702 - 0.91111111 0.90909091 0.84444444 0.88888889] - -mean value: 0.8900101851073174 - -key: TN -value: 33 - -mean value: 33.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 36 - -mean value: 36.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.53 - -Accuracy on Blind test: 0.77 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.04922676 0.03869319 0.03898191 0.0404942 0.03656006 0.03749299 - 0.03978753 0.0382123 0.04020357 0.03854203] - -mean value: 0.03981945514678955 - -key: score_time -value: [0.01003122 0.01038003 0.01095271 0.01000929 0.01008701 0.01012039 - 0.01010728 0.01007485 0.01013327 0.01011372] - -mean value: 0.010200977325439453 - -key: test_mcc -value: [1. 0.65465367 0.40824829 1. 0.55 0.55 - 0.31622777 0.79056942 0.79056942 0.79056942] - -mean value: 0.6850837972314963 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.83333333 0.66666667 1. 0.75 0.75 - 0.57142857 0.90909091 0.90909091 0.90909091] - -mean value: 0.8298701298701298 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.71428571 0.75 1. 0.75 0.75 - 0.66666667 0.83333333 0.83333333 0.83333333] - -mean value: 0.8130952380952381 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 0.6 1. 0.75 0.75 0.5 1. 1. 1. ] - -mean value: 0.86 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.8 0.7 1. 0.77777778 0.77777778 - 0.66666667 0.88888889 0.88888889 0.88888889] - -mean value: 0.8388888888888889 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.8 0.7 1. 0.775 0.775 0.65 0.875 0.875 0.875] - -mean value: 0.8324999999999999 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.71428571 0.5 1. 0.6 0.6 - 0.4 0.83333333 0.83333333 0.83333333] - -mean value: 0.7314285714285714 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.01391625 0.01757169 0.03148174 0.04039502 0.04079723 0.04090428 - 0.0404675 0.04072762 0.04066205 0.04058099] - -mean value: 0.03475043773651123 - -key: score_time -value: [0.01157188 0.01159167 0.02229023 0.02026033 0.02213502 0.02002287 - 0.02123189 0.02278447 0.01990438 0.02181673] - -mean value: 0.019360947608947753 - -key: test_mcc -value: [ 0.81649658 0.81649658 0.5 1. -0.1 -0.15811388 - 0.1 -0.1 0.79056942 0.55 ] - -mean value: 0.4215448693889128 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.90909091 0.88888889 0.57142857 1. 0.44444444 0.28571429 - 0.5 0.44444444 0.90909091 0.8 ] - -mean value: 0.6753102453102453 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 1. 1. 1. 0.4 0.33333333 - 0.5 0.5 0.83333333 0.8 ] - -mean value: 0.72 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.8 0.4 1. 0.5 0.25 0.5 0.4 1. 0.8 ] - -mean value: 0.6649999999999999 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 0.7 1. 0.44444444 0.44444444 - 0.55555556 0.44444444 0.88888889 0.77777778] - -mean value: 0.7055555555555556 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 0.7 1. 0.45 0.425 0.55 0.45 0.875 0.775] - -mean value: 0.7025 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.83333333 0.8 0.4 1. 0.28571429 0.16666667 - 0.33333333 0.28571429 0.83333333 0.66666667] - -mean value: 0.5604761904761905 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 35 - -mean value: 35.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 32 - -mean value: 32.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.6 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02287936 0.00857711 0.00837636 0.00851226 0.00820565 0.00880599 - 0.0081892 0.00892711 0.00876164 0.00872445] - -mean value: 0.0099959135055542 - -key: score_time -value: [0.01275063 0.00863481 0.00858188 0.00834084 0.0082655 0.00896335 - 0.00858784 0.00890231 0.00822568 0.00817227] - -mean value: 0.008942508697509765 - -key: test_mcc -value: [ 0.2 -0.40824829 0.21821789 -0.21821789 0.05976143 -0.1 - 0.35 0.31622777 0.31622777 0.31622777] - -mean value: 0.10501964380533704 - -key: train_mcc -value: [0.40766097 0.54777433 0.42857143 0.45250923 0.50609081 0.55315615 - 0.43521595 0.41222451 0.43521595 0.41374985] - -mean value: 0.45921691782565255 - -key: test_fscore -value: [0.6 0.22222222 0.5 0.5 0.33333333 0.44444444 - 0.66666667 0.72727273 0.72727273 0.72727273] - -mean value: 0.5448484848484848 - -key: train_fscore -value: [0.71910112 0.77108434 0.71428571 0.72941176 0.75294118 0.77647059 - 0.72093023 0.69135802 0.71428571 0.6835443 ] - -mean value: 0.7273412979975062 - -key: test_precision -value: [0.6 0.25 0.66666667 0.42857143 0.5 0.4 - 0.6 0.66666667 0.66666667 0.66666667] - -mean value: 0.5445238095238095 - -key: train_precision -value: [0.68085106 0.7804878 0.71428571 0.72093023 0.76190476 0.78571429 - 0.72093023 0.71794872 0.71428571 0.72972973] - -mean value: 0.7327068257693039 - -key: test_recall -value: [0.6 0.2 0.4 0.6 0.25 0.5 0.75 0.8 0.8 0.8 ] - -mean value: 0.57 - -key: train_recall -value: [0.76190476 0.76190476 0.71428571 0.73809524 0.74418605 0.76744186 - 0.72093023 0.66666667 0.71428571 0.64285714] - -mean value: 0.7232558139534884 - -key: test_accuracy -value: [0.6 0.3 0.6 0.4 0.55555556 0.44444444 - 0.66666667 0.66666667 0.66666667 0.66666667] - -mean value: 0.5566666666666666 - -key: train_accuracy -value: [0.70238095 0.77380952 0.71428571 0.72619048 0.75294118 0.77647059 - 0.71764706 0.70588235 0.71764706 0.70588235] - -mean value: 0.7293137254901961 - -key: test_roc_auc -value: [0.6 0.3 0.6 0.4 0.525 0.45 0.675 0.65 0.65 0.65 ] - -mean value: 0.55 - -key: train_roc_auc -value: [0.70238095 0.77380952 0.71428571 0.72619048 0.7530454 0.77657807 - 0.71760797 0.70542636 0.71760797 0.7051495 ] - -mean value: 0.7292081949058694 - -key: test_jcc -value: [0.42857143 0.125 0.33333333 0.33333333 0.2 0.28571429 - 0.5 0.57142857 0.57142857 0.57142857] - -mean value: 0.3920238095238095 - -key: train_jcc -value: [0.56140351 0.62745098 0.55555556 0.57407407 0.60377358 0.63461538 - 0.56363636 0.52830189 0.55555556 0.51923077] - -mean value: 0.5723597663529902 - -key: TN -value: 25 - -mean value: 25.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 27 - -mean value: 27.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.52 - -Accuracy on Blind test: 0.76 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00962329 0.01323986 0.01237082 0.01236582 0.01327753 0.01318359 - 0.01319122 0.01325846 0.01270938 0.01257205] - -mean value: 0.012579202651977539 - -key: score_time -value: [0.008636 0.01129222 0.01114297 0.01113963 0.01109719 0.01116228 - 0.01118302 0.01116157 0.01114726 0.01120234] - -mean value: 0.010916447639465332 - -key: test_mcc -value: [ 1. 0.6 0.5 0.6 -0.15811388 -0.05976143 - -0.05976143 0.39528471 0.39528471 0.1 ] - -mean value: 0.33129326711002366 - -key: train_mcc -value: [0.74535599 0.65094455 0.66953406 0.63245553 0.82687419 0.48724235 - 0.69036294 0.92967885 0.72991065 0.95396693] - -mean value: 0.7316326053036888 - -key: test_fscore -value: [1. 0.8 0.57142857 0.8 0.28571429 0.54545455 - 0.54545455 0.76923077 0.76923077 0.6 ] - -mean value: 0.6686513486513486 - -key: train_fscore -value: [0.83333333 0.83168317 0.76470588 0.72727273 0.8974359 0.76785714 - 0.85148515 0.96470588 0.86597938 0.97560976] - -mean value: 0.8480068319977526 - -key: test_precision -value: [1. 0.8 1. 0.8 0.33333333 0.42857143 - 0.42857143 0.625 0.625 0.6 ] - -mean value: 0.664047619047619 - -key: train_precision -value: [1. 0.71186441 1. 1. 1. 0.62318841 - 0.74137931 0.95348837 0.76363636 1. ] - -mean value: 0.8793556858650977 - -key: test_recall -value: [1. 0.8 0.4 0.8 0.25 0.75 0.75 1. 1. 0.6 ] - -mean value: 0.735 - -key: train_recall -value: [0.71428571 1. 0.61904762 0.57142857 0.81395349 1. - 1. 0.97619048 1. 0.95238095] - -mean value: 0.8647286821705427 - -key: test_accuracy -value: [1. 0.8 0.7 0.8 0.44444444 0.44444444 - 0.44444444 0.66666667 0.66666667 0.55555556] - -mean value: 0.6522222222222223 - -key: train_accuracy -value: [0.85714286 0.79761905 0.80952381 0.78571429 0.90588235 0.69411765 - 0.82352941 0.96470588 0.84705882 0.97647059] - -mean value: 0.8461764705882352 - -key: test_roc_auc -value: [1. 0.8 0.7 0.8 0.425 0.475 0.475 0.625 0.625 0.55 ] - -mean value: 0.6475 - -key: train_roc_auc -value: [0.85714286 0.79761905 0.80952381 0.78571429 0.90697674 0.69047619 - 0.82142857 0.96483942 0.84883721 0.97619048] - -mean value: 0.8458748615725359 - -key: test_jcc -value: [1. 0.66666667 0.4 0.66666667 0.16666667 0.375 - 0.375 0.625 0.625 0.42857143] - -mean value: 0.5328571428571429 - -key: train_jcc -value: [0.71428571 0.71186441 0.61904762 0.57142857 0.81395349 0.62318841 - 0.74137931 0.93181818 0.76363636 0.95238095] - -mean value: 0.7442983013891087 - -key: TN -value: 27 - -mean value: 27.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.61 - -Accuracy on Blind test: 0.81 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01054049 0.01207495 0.01262665 0.01228166 0.01229572 0.01198316 - 0.0122354 0.012609 0.012393 0.01217747] - -mean value: 0.012121748924255372 - -key: score_time -value: [0.01128149 0.01144648 0.01137066 0.01132822 0.01126194 0.01129317 - 0.01131725 0.01129222 0.01129723 0.01152325] - -mean value: 0.011341190338134766 - -key: test_mcc -value: [ 0.33333333 0.65465367 0.33333333 0.6 0.31622777 -0.05976143 - -0.31622777 0.79056942 0.79056942 0.63245553] - -mean value: 0.40751532690257897 - -key: train_mcc -value: [0.34641016 0.6882472 0.57735027 0.82462113 0.92967885 0.52445525 - 0.74667082 0.92964003 0.85983393 0.80605538] - -mean value: 0.7232963006785581 - -key: test_fscore -value: [0.71428571 0.83333333 0.33333333 0.8 0.57142857 0.54545455 - 0. 0.90909091 0.90909091 0.75 ] - -mean value: 0.6366017316017316 - -key: train_fscore -value: [0.71794872 0.84848485 0.66666667 0.89473684 0.96470588 0.78181818 - 0.86419753 0.96385542 0.93023256 0.88 ] - -mean value: 0.8512646650067099 - -key: test_precision -value: [0.55555556 0.71428571 1. 0.8 0.66666667 0.42857143 - 0. 0.83333333 0.83333333 1. ] - -mean value: 0.6831746031746031 - -key: train_precision -value: [0.56 0.73684211 1. 1. 0.97619048 0.64179104 - 0.92105263 0.97560976 0.90909091 1. ] - -mean value: 0.8720576922997172 - -key: test_recall -value: [1. 1. 0.2 0.8 0.5 0.75 0. 1. 1. 0.6 ] - -mean value: 0.6849999999999999 - -key: train_recall -value: [1. 1. 0.5 0.80952381 0.95348837 1. - 0.81395349 0.95238095 0.95238095 0.78571429] - -mean value: 0.8767441860465116 - -key: test_accuracy -value: [0.6 0.8 0.6 0.8 0.66666667 0.44444444 - 0.44444444 0.88888889 0.88888889 0.77777778] - -mean value: 0.691111111111111 - -key: train_accuracy -value: [0.60714286 0.82142857 0.75 0.9047619 0.96470588 0.71764706 - 0.87058824 0.96470588 0.92941176 0.89411765] - -mean value: 0.8424509803921568 - -key: test_roc_auc -value: [0.6 0.8 0.6 0.8 0.65 0.475 0.4 0.875 0.875 0.8 ] - -mean value: 0.6874999999999999 - -key: train_roc_auc -value: [0.60714286 0.82142857 0.75 0.9047619 0.96483942 0.71428571 - 0.87126246 0.96456257 0.92967885 0.89285714] - -mean value: 0.8420819490586933 - -key: test_jcc -value: [0.55555556 0.71428571 0.2 0.66666667 0.4 0.375 - 0. 0.83333333 0.83333333 0.6 ] - -mean value: 0.5178174603174603 - -key: train_jcc -value: [0.56 0.73684211 0.5 0.80952381 0.93181818 0.64179104 - 0.76086957 0.93023256 0.86956522 0.78571429] - -mean value: 0.7526356767843785 - -key: TN -value: 32 - -mean value: 32.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 33 - -mean value: 33.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.51 - -Accuracy on Blind test: 0.77 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.09869885 0.08734775 0.08220458 0.08168125 0.08259201 0.08675361 - 0.08353996 0.08585095 0.08518791 0.08337188] - -mean value: 0.08572287559509277 - -key: score_time -value: [0.01459026 0.01583123 0.01436472 0.01501179 0.01493001 0.01488781 - 0.01446915 0.01454186 0.01576042 0.0150547 ] - -mean value: 0.014944195747375488 - -key: test_mcc -value: [0.6 0.81649658 0. 1. 0.55 0.55 - 0.55 0.79056942 0.5976143 0.55 ] - -mean value: 0.6004680300637018 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.90909091 0.28571429 1. 0.75 0.75 - 0.75 0.90909091 0.83333333 0.8 ] - -mean value: 0.7787229437229437 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.83333333 0.5 1. 0.75 0.75 - 0.75 0.83333333 0.71428571 0.8 ] - -mean value: 0.7730952380952381 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 1. 0.2 1. 0.75 0.75 0.75 1. 1. 0.8 ] - -mean value: 0.805 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8 0.9 0.5 1. 0.77777778 0.77777778 - 0.77777778 0.88888889 0.77777778 0.77777778] - -mean value: 0.7977777777777778 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8 0.9 0.5 1. 0.775 0.775 0.775 0.875 0.75 0.775] - -mean value: 0.7925000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.83333333 0.16666667 1. 0.6 0.6 - 0.6 0.83333333 0.71428571 0.66666667] - -mean value: 0.6680952380952382 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 38 - -mean value: 38.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02725291 0.02678299 0.02645731 0.03228021 0.03082395 0.03413343 - 0.03731322 0.02821302 0.03662419 0.03972507] - -mean value: 0.03196063041687012 - -key: score_time -value: [0.02253675 0.0191834 0.01804137 0.01951694 0.02123547 0.01963592 - 0.01912975 0.01852632 0.02202606 0.0218308 ] - -mean value: 0.02016627788543701 - -key: test_mcc -value: [0.81649658 0.65465367 0.21821789 1. 0.55 0.55 - 0.31622777 0.79056942 0.79056942 0.55 ] - -mean value: 0.6236734737972723 - -key: train_mcc -value: [0.97646729 1. 0.95238095 1. 0.97673145 0.97673145 - 0.95396693 0.97674419 0.97674419 1. ] - -mean value: 0.9789766453432106 - -key: test_fscore -value: [0.88888889 0.83333333 0.5 1. 0.75 0.75 - 0.57142857 0.90909091 0.90909091 0.8 ] - -mean value: 0.7911832611832612 - -key: train_fscore -value: [0.98823529 1. 0.97619048 1. 0.98850575 0.98850575 - 0.97727273 0.98823529 0.98823529 1. ] - -mean value: 0.9895180580069018 - -key: test_precision -value: [1. 0.71428571 0.66666667 1. 0.75 0.75 - 0.66666667 0.83333333 0.83333333 0.8 ] - -mean value: 0.8014285714285714 - -key: train_precision -value: [0.97674419 1. 0.97619048 1. 0.97727273 0.97727273 - 0.95555556 0.97674419 0.97674419 1. ] - -mean value: 0.9816524044431022 - -key: test_recall -value: [0.8 1. 0.4 1. 0.75 0.75 0.5 1. 1. 0.8 ] - -mean value: 0.8 - -key: train_recall -value: [1. 1. 0.97619048 1. 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9976190476190476 - -key: test_accuracy -value: [0.9 0.8 0.6 1. 0.77777778 0.77777778 - 0.66666667 0.88888889 0.88888889 0.77777778] - -mean value: 0.8077777777777779 - -key: train_accuracy -value: [0.98809524 1. 0.97619048 1. 0.98823529 0.98823529 - 0.97647059 0.98823529 0.98823529 1. ] - -mean value: 0.9893697478991598 - -key: test_roc_auc -value: [0.9 0.8 0.6 1. 0.775 0.775 0.65 0.875 0.875 0.775] - -mean value: 0.8025 - -key: train_roc_auc -value: [0.98809524 1. 0.97619048 1. 0.98809524 0.98809524 - 0.97619048 0.98837209 0.98837209 1. ] - -mean value: 0.9893410852713178 - -key: test_jcc -value: [0.8 0.71428571 0.33333333 1. 0.6 0.6 - 0.4 0.83333333 0.83333333 0.66666667] - -mean value: 0.6780952380952381 - -key: train_jcc -value: [0.97674419 1. 0.95348837 1. 0.97727273 0.97727273 - 0.95555556 0.97674419 0.97674419 1. ] - -mean value: 0.9793821940333569 - -key: TN -value: 38 - -mean value: 38.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 38 - -mean value: 38.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01234031 0.01516294 0.01516199 0.0153873 0.01572132 0.01583409 - 0.01557136 0.01581097 0.0155983 0.0156157 ] - -mean value: 0.015220427513122558 - -key: score_time -value: [0.01128292 0.01133847 0.01157451 0.01171637 0.01160073 0.01173162 - 0.01159739 0.01156569 0.01162362 0.01267815] - -mean value: 0.011670947074890137 - -key: test_mcc -value: [ 0. 0.40824829 0. 0.40824829 -0.31622777 -0.31622777 - -0.55 -0.05976143 -0.31622777 0.31622777] - -mean value: -0.04257203815726696 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.54545455 0.66666667 0.44444444 0.72727273 0.4 0.4 - 0.22222222 0.28571429 0.25 0.72727273] - -mean value: 0.46690476190476193 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.5 0.75 0.5 0.66666667 0.33333333 0.33333333 - 0.2 0.5 0.33333333 0.66666667] - -mean value: 0.47833333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.6 0.6 0.4 0.8 0.5 0.5 0.25 0.2 0.2 0.8 ] - -mean value: 0.48500000000000004 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.5 0.7 0.5 0.7 0.33333333 0.33333333 - 0.22222222 0.44444444 0.33333333 0.66666667] - -mean value: 0.47333333333333333 - -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 0.7 0.5 0.7 0.35 0.35 0.225 0.475 0.35 0.65 ] - -mean value: 0.4800000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.375 0.5 0.28571429 0.57142857 0.25 0.25 - 0.125 0.16666667 0.14285714 0.57142857] - -mean value: 0.3238095238095238 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 22 - -mean value: 22.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 23 - -mean value: 23.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.12 - -Accuracy on Blind test: 0.56 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.21245885 0.189152 0.19599938 0.19062042 0.19430876 0.19379067 - 0.19145727 0.19425774 0.19209957 0.18954039] - -mean value: 0.19436850547790527 - -key: score_time -value: [0.00882983 0.00903535 0.00904727 0.00899792 0.00883126 0.00867009 - 0.01124382 0.00880098 0.00910807 0.00881577] - -mean value: 0.009138035774230956 - -key: test_mcc -value: [0.81649658 0.81649658 0.40824829 1. 0.5976143 0.8 - 0.55 0.79056942 0.79056942 0.79056942] - -mean value: 0.7360564002112795 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.90909091 0.66666667 1. 0.66666667 0.88888889 - 0.75 0.90909091 0.90909091 0.90909091] - -mean value: 0.8497474747474747 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.83333333 0.75 1. 1. 0.8 - 0.75 0.83333333 0.83333333 0.83333333] - -mean value: 0.8633333333333333 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.8 1. 0.6 1. 0.5 1. 0.75 1. 1. 1. ] - -mean value: 0.865 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9 0.9 0.7 1. 0.77777778 0.88888889 - 0.77777778 0.88888889 0.88888889 0.88888889] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -mean value: 0.861111111111111 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9 0.9 0.7 1. 0.75 0.9 0.775 0.875 0.875 0.875] - -mean value: 0.8550000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.83333333 0.5 1. 0.5 0.8 - 0.6 0.83333333 0.83333333 0.83333333] - -mean value: 0.7533333333333333 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 40 - -mean value: 40.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 41 - -mean value: 41.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01017809 0.01389313 0.01364994 0.01352382 0.01384807 0.0141468 - 0.01385188 0.01381516 0.01406789 0.01416254] - -mean value: 0.013513731956481933 - -key: score_time -value: [0.01132488 0.01158571 0.01188087 0.01190567 0.01249957 0.01161242 - 0.01325011 0.01272106 0.01160336 0.01318431] - -mean value: 0.012156796455383301 - -key: test_mcc -value: [-0.2 -0.2 0. -0.21821789 -0.47809144 0.31622777 - 0.05976143 -0.31622777 0.15811388 -0.5976143 ] - -mean value: -0.1476048325161808 - -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 0.61538462 0.5 0. 0.57142857 - 0.33333333 0.25 0.5 0. ] - -mean value: 0.35701465201465205 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.4 0.4 0.5 0.42857143 0. 0.66666667 - 0.5 0.33333333 0.66666667 0. ] - -mean value: 0.3895238095238095 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.4 0.4 0.8 0.6 0. 0.5 0.25 0.2 0.4 0. ] - -mean value: 0.35500000000000004 - -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 0.5 0.4 0.33333333 0.66666667 - 0.55555556 0.33333333 0.55555556 0.22222222] - -mean value: 0.4366666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.4 0.4 0.5 0.4 0.3 0.65 0.525 0.35 0.575 0.25 ] - -mean value: 0.43499999999999994 - -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 0.44444444 0.33333333 0. 0.4 - 0.2 0.14285714 0.33333333 0. ] - -mean value: 0.2353968253968254 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 24 - -mean value: 24.0 - -key: FP -value: 30 - -mean value: 30.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 17 - -mean value: 17.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: -0.01 - -Accuracy on Blind test: 0.48 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.03160405 0.02819967 0.02554464 0.03197432 0.030545 0.03205037 - 0.02785897 0.03191566 0.02753639 0.03187776] - -mean value: 0.029910683631896973 - -key: score_time -value: [0.01829004 0.02292609 0.02184868 0.01490974 0.02200842 0.01734495 - 0.02255273 0.02025127 0.02185893 0.02053523] - -mean value: 0.02025260925292969 - -key: test_mcc -value: [ 0.5 0.40824829 0.40824829 0.81649658 -0.1 0.35 - 0.31622777 0.79056942 0.55 0.35 ] - -mean value: 0.4389790342914385 - -key: train_mcc -value: [1. 1. 1. 1. 0.97674419 0.97673145 - 0.97673145 0.95293466 1. 1. ] - -mean value: 0.9883141751954136 - -key: test_fscore -value: [0.76923077 0.66666667 0.66666667 0.90909091 0.44444444 0.66666667 - 0.57142857 0.90909091 0.8 0.66666667] - -mean value: 0.7069952269952269 - -key: train_fscore -value: [1. 1. 1. 1. 0.98823529 0.98850575 - 0.98850575 0.97619048 1. 1. ] - -mean value: 0.9941437264560996 - -key: test_precision -value: [0.625 0.75 0.75 0.83333333 0.4 0.6 - 0.66666667 0.83333333 0.8 0.75 ] - -mean value: 0.7008333333333334 - -key: train_precision -value: [1. 1. 1. 1. 1. 0.97727273 - 0.97727273 0.97619048 1. 1. ] - -mean value: 0.9930735930735931 - -key: test_recall -value: [1. 0.6 0.6 1. 0.5 0.75 0.5 1. 0.8 0.6 ] - -mean value: 0.735 - -key: train_recall -value: [1. 1. 1. 1. 0.97674419 1. - 1. 0.97619048 1. 1. ] - -mean value: 0.9952934662236987 - -key: test_accuracy -value: [0.7 0.7 0.7 0.9 0.44444444 0.66666667 - 0.66666667 0.88888889 0.77777778 0.66666667] - -mean value: 0.711111111111111 - -key: train_accuracy -value: [1. 1. 1. 1. 0.98823529 0.98823529 - 0.98823529 0.97647059 1. 1. ] - -mean value: 0.9941176470588236 - -key: test_roc_auc -value: [0.7 0.7 0.7 0.9 0.45 0.675 0.65 0.875 0.775 0.675] - -mean value: 0.7100000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 0.98837209 0.98809524 - 0.98809524 0.97646733 1. 1. ] - -mean value: 0.9941029900332226 - -key: test_jcc -value: [0.625 0.5 0.5 0.83333333 0.28571429 0.5 - 0.4 0.83333333 0.66666667 0.5 ] - -mean value: 0.564404761904762 - -key: train_jcc -value: [1. 1. 1. 1. 0.97674419 0.97727273 - 0.97727273 0.95348837 1. 1. ] - -mean value: 0.9884778012684989 - -key: TN -value: 32 - -mean value: 32.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: 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 - rus_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: 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 - rus_CV['Resampling'] = rs_rus -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: 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 - rus_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: 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 - rus_BT['Resampling'] = rs_rus - -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.56 - -Accuracy on Blind test: 0.79 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.20420551 0.23215771 0.18351507 0.18484735 0.18196034 0.18221092 - 0.18410993 0.17798209 0.18241525 0.17692542] - -mean value: 0.1890329599380493 - -key: score_time -value: [0.02086949 0.01631784 0.02118921 0.02124715 0.02169418 0.02193141 - 0.02344728 0.02114987 0.02232218 0.0211997 ] - -mean value: 0.021136832237243653 - -key: test_mcc -value: [ 0.65465367 0.40824829 0.65465367 0.81649658 -0.1 0.55 - 0.5976143 0.79056942 0.8 0.35 ] - -mean value: 0.5522235932516835 - -key: train_mcc -value: [1. 1. 1. 1. 0.97674419 1. - 1. 0.95293466 1. 1. ] - -mean value: 0.99296788482835 - -key: test_fscore -value: [0.83333333 0.66666667 0.75 0.90909091 0.44444444 0.75 - 0.66666667 0.90909091 0.88888889 0.66666667] - -mean value: 0.7484848484848484 - -key: train_fscore -value: [1. 1. 1. 1. 0.98823529 1. - 1. 0.97619048 1. 1. ] - -mean value: 0.9964425770308123 - -key: test_precision -value: [0.71428571 0.75 1. 0.83333333 0.4 0.75 - 1. 0.83333333 1. 0.75 ] - -mean value: 0.8030952380952382 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 1. 0.97619048 1. 1. ] - -mean value: 0.9976190476190476 - -key: test_recall -value: [1. 0.6 0.6 1. 0.5 0.75 0.5 1. 0.8 0.6 ] - -mean value: 0.735 - -key: train_recall -value: [1. 1. 1. 1. 0.97674419 1. - 1. 0.97619048 1. 1. ] - -mean value: 0.9952934662236987 - -key: test_accuracy -value: [0.8 0.7 0.8 0.9 0.44444444 0.77777778 - 0.77777778 0.88888889 0.88888889 0.66666667] - -mean value: 0.7644444444444445 - -key: train_accuracy -value: [1. 1. 1. 1. 0.98823529 1. - 1. 0.97647059 1. 1. ] - -mean value: 0.9964705882352941 - -key: test_roc_auc -value: [0.8 0.7 0.8 0.9 0.45 0.775 0.75 0.875 0.9 0.675] - -mean value: 0.7625000000000001 - -key: train_roc_auc -value: [1. 1. 1. 1. 0.98837209 1. - 1. 0.97646733 1. 1. ] - -mean value: 0.996483942414175 - -key: test_jcc -value: [0.71428571 0.5 0.6 0.83333333 0.28571429 0.6 - 0.5 0.83333333 0.8 0.5 ] - -mean value: 0.6166666666666667 - -key: train_jcc -value: [1. 1. 1. 1. 0.97674419 1. - 1. 0.95348837 1. 1. ] - -mean value: 0.9930232558139535 - -key: TN -value: 37 - -mean value: 37.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg -value: 47 - -mean value: 47.0 - -key: trainingY_pos -value: 47 - -mean value: 47.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.71 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.03890443 0.04712152 0.04645896 0.0403986 0.02747059 0.02553892 - 0.0305891 0.02482867 0.02699924 0.02433038] - -mean value: 0.03326404094696045 - -key: score_time -value: [0.0116756 0.0124619 0.01195359 0.01166916 0.01168728 0.01173449 - 0.01199651 0.01173067 0.01165748 0.01163507] - -mean value: 0.011820173263549805 - -key: test_mcc -value: [0.51639778 0.51639778 0.60714286 0.73214286 0.32732684 0.64465837 - 0.64465837 0.60714286 0.73214286 0.875 ] - -mean value: 0.620301056535467 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer 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_mcc -value: [0.808911 0.808911 0.85400682 0.8251972 0.8251972 0.81031543 - 0.79688349 0.85400682 0.82480818 0.79560955] - -mean value: 0.8203846710669712 - -key: test_fscore -value: [0.71428571 0.71428571 0.8 0.85714286 0.61538462 0.72727273 - 0.84210526 0.8 0.875 0.93333333] - -mean value: 0.7878810224862856 - -key: train_fscore -value: [0.90510949 0.9037037 0.92753623 0.91176471 0.91176471 0.90510949 - 0.89393939 0.92647059 0.91176471 0.89705882] - -mean value: 0.909422183704111 - -key: test_precision -value: [0.83333333 0.83333333 0.75 0.85714286 0.66666667 1. - 0.72727273 0.85714286 0.875 1. ] - -mean value: 0.8399891774891775 - -key: train_precision -value: [0.89855072 0.91044776 0.92753623 0.92537313 0.92537313 0.91176471 - 0.921875 0.92647059 0.91176471 0.89705882] - -mean value: 0.9156214809901897 - -key: test_recall -value: [0.625 0.625 0.85714286 0.85714286 0.57142857 0.57142857 - 1. 0.75 0.875 0.875 ] - -mean value: 0.7607142857142858 - -key: train_recall -value: [0.91176471 0.89705882 0.92753623 0.89855072 0.89855072 0.89855072 - 0.86764706 0.92647059 0.91176471 0.89705882] - -mean value: 0.9034953111679453 - -key: test_accuracy -value: [0.75 0.75 0.8 0.86666667 0.66666667 0.8 - 0.8 0.8 0.86666667 0.93333333] - -mean value: 0.8033333333333335 - -key: train_accuracy -value: [0.90441176 0.90441176 0.9270073 0.91240876 0.91240876 0.90510949 - 0.89781022 0.9270073 0.91240876 0.89781022] - -mean value: 0.9100794332331473 - -key: test_roc_auc -value: [0.75 0.75 0.80357143 0.86607143 0.66071429 0.78571429 - 0.78571429 0.80357143 0.86607143 0.9375 ] - -mean value: 0.8008928571428571 - -key: train_roc_auc -value: [0.90441176 0.90441176 0.92700341 0.91251066 0.91251066 0.90515772 - 0.89759165 0.92700341 0.91240409 0.89780477] - -mean value: 0.910080988917306 - -key: test_jcc -value: [0.55555556 0.55555556 0.66666667 0.75 0.44444444 0.57142857 - 0.72727273 0.66666667 0.77777778 0.875 ] - -mean value: 0.6590367965367965 - -key: train_jcc -value: [0.82666667 0.82432432 0.86486486 0.83783784 0.83783784 0.82666667 - 0.80821918 0.8630137 0.83783784 0.81333333] - -mean value: 0.8340602246081698 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 58 - -mean value: 58.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.60730147 0.62539339 0.75115871 0.61732793 0.60234904 0.76394868 - 0.6198101 0.61897731 0.96049881 0.89969039] - -mean value: 0.7066455841064453 - -key: score_time -value: [0.01314354 0.01313806 0.01318407 0.01316643 0.01313019 0.01303983 - 0.01355648 0.01404309 0.01357317 0.01316524] - -mean value: 0.013314008712768555 - -key: test_mcc -value: [0.75 0.67419986 0.46428571 0.73214286 0.46428571 0.64465837 - 0.75592895 0.87287156 0.73214286 0.60714286] - -mean value: 0.6697658740645969 - -key: train_mcc -value: [1. 0.98540068 1. 1. 0.94160273 1. - 1. 1. 1. 1. ] - -mean value: 0.9927003404297572 - -key: test_fscore -value: [0.875 0.76923077 0.71428571 0.85714286 0.71428571 0.72727273 - 0.88888889 0.94117647 0.875 0.8 ] - -mean value: 0.8162283141694907 - -key: train_fscore -value: [1. 0.99259259 1. 1. 0.97101449 1. - 1. 1. 1. 1. ] - -mean value: 0.9963607085346216 - -key: test_precision -value: [0.875 1. 0.71428571 0.85714286 0.71428571 1. - 0.8 0.88888889 0.875 0.85714286] - -mean value: 0.8581746031746033 - -key: train_precision -value: [1. 1. 1. 1. 0.97101449 1. - 1. 1. 1. 1. ] - -mean value: 0.9971014492753623 - -key: test_recall -value: [0.875 0.625 0.71428571 0.85714286 0.71428571 0.57142857 - 1. 1. 0.875 0.75 ] - -mean value: 0.7982142857142857 - -key: train_recall -value: [1. 0.98529412 1. 1. 0.97101449 1. - 1. 1. 1. 1. ] - -mean value: 0.9956308610400683 - -key: test_accuracy -value: [0.875 0.8125 0.73333333 0.86666667 0.73333333 0.8 - 0.86666667 0.93333333 0.86666667 0.8 ] - -mean value: 0.8287500000000001 - -key: train_accuracy -value: [1. 0.99264706 1. 1. 0.97080292 1. - 1. 1. 1. 1. ] - -mean value: 0.9963449978531559 - -key: test_roc_auc -value: [0.875 0.8125 0.73214286 0.86607143 0.73214286 0.78571429 - 0.85714286 0.92857143 0.86607143 0.80357143] - -mean value: 0.8258928571428571 - -key: train_roc_auc -value: [1. 0.99264706 1. 1. 0.97080136 1. - 1. 1. 1. 1. ] - -mean value: 0.99634484228474 - -key: test_jcc -value: [0.77777778 0.625 0.55555556 0.75 0.55555556 0.57142857 - 0.8 0.88888889 0.77777778 0.66666667] - -mean value: 0.6968650793650794 - -key: train_jcc -value: [1. 0.98529412 1. 1. 0.94366197 1. - 1. 1. 1. 1. ] - -mean value: 0.9928956089478046 - -key: TN -value: 67 - -mean value: 67.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 60 - -mean value: 60.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01197147 0.01149893 0.00909448 0.00837827 0.00916386 0.00894952 - 0.00821567 0.00840712 0.00828362 0.00828409] - -mean value: 0.009224700927734374 - -key: score_time -value: [0.01136208 0.01117897 0.00898957 0.00902128 0.0088501 0.00829625 - 0.00840592 0.00831342 0.00911379 0.00844216] - -mean value: 0.009197354316711426 - -key: test_mcc -value: [ 0.13483997 0.62994079 -0.18898224 0.87287156 0.05455447 0.05455447 - 0.33928571 0.33928571 0.21821789 0.46428571] - -mean value: 0.2918854063491848 - -key: train_mcc -value: [0.45593166 0.51520752 0.50394373 0.51832907 0.56316734 0.47754676 - 0.49979869 0.50427283 0.46184366 0.43147539] - -mean value: 0.493151664179406 - -key: test_fscore -value: [0.46153846 0.8 0.47058824 0.92307692 0.46153846 0.46153846 - 0.66666667 0.66666667 0.57142857 0.75 ] - -mean value: 0.623304244774833 - -key: train_fscore -value: [0.72592593 0.76258993 0.75 0.75912409 0.7761194 0.76433121 - 0.68421053 0.74242424 0.71317829 0.70229008] - -mean value: 0.7380193694400431 - -key: test_precision -value: [0.6 0.85714286 0.4 1. 0.5 0.5 - 0.71428571 0.71428571 0.66666667 0.75 ] - -mean value: 0.6702380952380953 - -key: train_precision -value: [0.73134328 0.74647887 0.76119403 0.76470588 0.8 0.68181818 - 0.84782609 0.765625 0.75409836 0.73015873] - -mean value: 0.7583248428614385 - -key: test_recall -value: [0.375 0.75 0.57142857 0.85714286 0.42857143 0.42857143 - 0.625 0.625 0.5 0.75 ] - -mean value: 0.5910714285714286 - -key: train_recall -value: [0.72058824 0.77941176 0.73913043 0.75362319 0.75362319 0.86956522 - 0.57352941 0.72058824 0.67647059 0.67647059] - -mean value: 0.7263000852514919 - -key: test_accuracy -value: [0.5625 0.8125 0.4 0.93333333 0.53333333 0.53333333 - 0.66666667 0.66666667 0.6 0.73333333] - -mean value: 0.6441666666666667 - -key: train_accuracy -value: [0.72794118 0.75735294 0.75182482 0.75912409 0.7810219 0.72992701 - 0.73722628 0.75182482 0.72992701 0.71532847] - -mean value: 0.7441498497209104 - -key: test_roc_auc -value: [0.5625 0.8125 0.41071429 0.92857143 0.52678571 0.52678571 - 0.66964286 0.66964286 0.60714286 0.73214286] - -mean value: 0.6446428571428571 - -key: train_roc_auc -value: [0.72794118 0.75735294 0.75191816 0.75916454 0.78122336 0.72890026 - 0.73604007 0.75159847 0.72953964 0.71504689] - -mean value: 0.7438725490196079 - -key: test_jcc -value: [0.3 0.66666667 0.30769231 0.85714286 0.3 0.3 - 0.5 0.5 0.4 0.6 ] - -mean value: 0.4731501831501831 - -key: train_jcc -value: [0.56976744 0.61627907 0.6 0.61176471 0.63414634 0.6185567 - 0.52 0.59036145 0.55421687 0.54117647] - -mean value: 0.5856269043845849 - -key: TN -value: 53 - -mean value: 53.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.41 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00908732 0.00908971 0.00919962 0.00935507 0.00946116 0.00924921 - 0.00891304 0.00912547 0.00865936 0.00867224] - -mean value: 0.009081220626831055 - -key: score_time -value: [0.00918055 0.00966501 0.00865173 0.00877142 0.00867128 0.00875568 - 0.00846004 0.00884628 0.0087254 0.00924444] - -mean value: 0.008897185325622559 - -key: test_mcc -value: [ 0.48038446 0.51639778 -0.46428571 0.64465837 -0.33928571 0.05455447 - -0.19642857 0.49099025 0.09449112 -0.20044593] - -mean value: 0.10810305245378571 - -key: train_mcc -value: [0.34459104 0.46444879 0.40740412 0.43493568 0.49877088 0.4754592 - 0.45696395 0.42182014 0.40803461 0.4060076 ] - -mean value: 0.43184360253867615 - -key: test_fscore -value: [0.54545455 0.71428571 0.26666667 0.72727273 0.28571429 0.46153846 - 0.4 0.71428571 0.46153846 0.18181818] - -mean value: 0.4758574758574758 - -key: train_fscore -value: [0.63414634 0.69918699 0.67716535 0.69767442 0.72 0.73134328 - 0.68333333 0.67741935 0.66666667 0.672 ] - -mean value: 0.6858935744689493 - -key: test_precision -value: [1. 0.83333333 0.25 1. 0.28571429 0.5 - 0.42857143 0.83333333 0.6 0.33333333] - -mean value: 0.6064285714285714 - -key: train_precision -value: [0.70909091 0.78181818 0.74137931 0.75 0.80357143 0.75384615 - 0.78846154 0.75 0.74545455 0.73684211] - -mean value: 0.7560464172850743 - -key: test_recall -value: [0.375 0.625 0.28571429 0.57142857 0.28571429 0.42857143 - 0.375 0.625 0.375 0.125 ] - -mean value: 0.40714285714285714 - -key: train_recall -value: [0.57352941 0.63235294 0.62318841 0.65217391 0.65217391 0.71014493 - 0.60294118 0.61764706 0.60294118 0.61764706] - -mean value: 0.6284739982949701 - -key: test_accuracy -value: [0.6875 0.75 0.26666667 0.8 0.33333333 0.53333333 - 0.4 0.73333333 0.53333333 0.4 ] - -mean value: 0.54375 - -key: train_accuracy -value: [0.66911765 0.72794118 0.70072993 0.71532847 0.74452555 0.73722628 - 0.72262774 0.7080292 0.70072993 0.70072993] - -mean value: 0.7126985830828682 - -key: test_roc_auc -value: [0.6875 0.75 0.26785714 0.78571429 0.33035714 0.52678571 - 0.40178571 0.74107143 0.54464286 0.41964286] - -mean value: 0.5455357142857143 - -key: train_roc_auc -value: [0.66911765 0.72794118 0.70130009 0.71579284 0.7452046 0.7374254 - 0.72176044 0.70737425 0.70002131 0.70012788] - -mean value: 0.7126065643648765 - -key: test_jcc -value: [0.375 0.55555556 0.15384615 0.57142857 0.16666667 0.3 - 0.25 0.55555556 0.3 0.1 ] - -mean value: 0.33280525030525027 - -key: train_jcc -value: [0.46428571 0.5375 0.51190476 0.53571429 0.5625 0.57647059 - 0.51898734 0.51219512 0.5 0.5060241 ] - -mean value: 0.522558191024897 - -key: TN -value: 52 - -mean value: 52.0 - -key: FP -value: 45 - -mean value: 45.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 31 - -mean value: 31.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.66 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01157355 0.00804496 0.00798678 0.0088408 0.00798821 0.00800323 - 0.00893235 0.00840855 0.00808954 0.00812149] - -mean value: 0.008598947525024414 - -key: score_time -value: [0.01089644 0.00977564 0.00971937 0.00999427 0.01003385 0.0099225 - 0.00957441 0.01004291 0.00989676 0.01009011] - -mean value: 0.00999462604522705 - -key: test_mcc -value: [ 0.40451992 -0.13483997 -0.04029115 0.07142857 -0.32732684 0.04029115 - -0.07142857 0.09449112 0.37796447 0.37796447] - 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0.61764706 0.64705882 0.60294118 0.66176471] - -mean value: 0.6388320545609548 - -key: test_accuracy -value: [0.6875 0.4375 0.46666667 0.53333333 0.33333333 0.53333333 - 0.46666667 0.53333333 0.66666667 0.66666667] - -mean value: 0.5325 - -key: train_accuracy -value: [0.69117647 0.69852941 0.7080292 0.68613139 0.70072993 0.70072993 - 0.71532847 0.70072993 0.69343066 0.69343066] - -mean value: 0.6988246028338342 - -key: test_roc_auc -value: [0.6875 0.4375 0.48214286 0.53571429 0.33928571 0.51785714 - 0.46428571 0.54464286 0.67857143 0.67857143] - -mean value: 0.5366071428571428 - -key: train_roc_auc -value: [0.69117647 0.69852941 0.70822677 0.68638107 0.70151321 0.70098039 - 0.71462063 0.70034101 0.69277494 0.69320119] - -mean value: 0.6987745098039216 - -key: test_jcc -value: [0.44444444 0.18181818 0.38461538 0.36363636 0.23076923 0.22222222 - 0.33333333 0.3 0.44444444 0.44444444] - -mean value: 0.334972804972805 - -key: train_jcc -value: [0.5 0.51764706 0.54022989 0.51136364 0.5 0.52873563 - 0.51851852 0.51764706 0.4939759 0.51724138] - -mean value: 0.5145359072695397 - -key: TN -value: 46 - -mean value: 46.0 - -key: FP -value: 41 - -mean value: 41.0 - -key: FN -value: 30 - -mean value: 30.0 - -key: TP -value: 35 - -mean value: 35.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.02 - -Accuracy on Blind test: 0.53 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00973821 0.00956583 0.00944161 0.00950122 0.0096302 0.00952268 - 0.00966358 0.00958252 0.00967121 0.00958061] - -mean value: 0.009589767456054688 - -key: score_time -value: [0.00870752 0.0086112 0.00871968 0.00859761 0.00864124 0.00872111 - 0.00869703 0.00871563 0.00870109 0.00881076] - -mean value: 0.008692288398742675 - -key: test_mcc -value: [ 0.40451992 0.40451992 -0.04029115 0.87287156 0.07142857 0.18898224 - 0.32732684 0.49099025 0.60714286 0.60714286] - -mean value: 0.3934633858302462 - -key: train_mcc -value: [0.6918501 0.677644 0.70801364 0.66581484 0.73747083 0.7082535 - 0.73747083 0.75186529 0.64981886 0.69345557] - -mean value: 0.702165745179242 - -key: test_fscore -value: [0.61538462 0.61538462 0.55555556 0.92307692 0.53333333 0.5 - 0.70588235 0.71428571 0.8 0.8 ] - -mean value: 0.6762903109961933 - -key: train_fscore -value: [0.84892086 0.83333333 0.85507246 0.83916084 0.87142857 0.85714286 - 0.86567164 0.87407407 0.82089552 0.84444444] - -mean value: 0.8510144610840692 - -key: test_precision -value: [0.8 0.8 0.45454545 1. 0.5 0.6 - 0.66666667 0.83333333 0.85714286 0.85714286] - -mean value: 0.7368831168831168 - -key: train_precision -value: [0.83098592 0.859375 0.85507246 0.81081081 0.85915493 0.84507042 - 0.87878788 0.88059701 0.83333333 0.85074627] - -mean value: 0.8503934037887863 - -key: test_recall -value: [0.5 0.5 0.71428571 0.85714286 0.57142857 0.42857143 - 0.75 0.625 0.75 0.75 ] - -mean value: 0.6446428571428571 - -key: train_recall -value: [0.86764706 0.80882353 0.85507246 0.86956522 0.88405797 0.86956522 - 0.85294118 0.86764706 0.80882353 0.83823529] - -mean value: 0.8522378516624041 - -key: test_accuracy -value: [0.6875 0.6875 0.46666667 0.93333333 0.53333333 0.6 - 0.66666667 0.73333333 0.8 0.8 ] - -mean value: 0.6908333333333333 - -key: train_accuracy -value: [0.84558824 0.83823529 0.8540146 0.83211679 0.86861314 0.8540146 - 0.86861314 0.87591241 0.82481752 0.84671533] - -mean value: 0.8508641047659941 - -key: test_roc_auc -value: [0.6875 0.6875 0.48214286 0.92857143 0.53571429 0.58928571 - 0.66071429 0.74107143 0.80357143 0.80357143] - -mean value: 0.6919642857142858 - -key: train_roc_auc -value: [0.84558824 0.83823529 0.85400682 0.83184143 0.86849957 0.85390026 - 0.86849957 0.87585251 0.82470162 0.84665388] - -mean value: 0.8507779198635976 - -key: test_jcc -value: [0.44444444 0.44444444 0.38461538 0.85714286 0.36363636 0.33333333 - 0.54545455 0.55555556 0.66666667 0.66666667] - -mean value: 0.5261960261960262 - -key: train_jcc -value: [0.7375 0.71428571 0.74683544 0.72289157 0.7721519 0.75 - 0.76315789 0.77631579 0.69620253 0.73076923] - -mean value: 0.7410110068948254 - -key: TN -value: 56 - -mean value: 56.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 20 - -mean value: 20.0 - -key: TP -value: 49 - -mean value: 49.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.68 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.64121795 0.56911659 0.5362215 0.69446111 0.57372665 0.55694437 - 0.56460571 0.69939494 0.5615809 0.53818989] - -mean value: 0.5935459613800049 - -key: score_time -value: [0.01190567 0.01195049 0.01202846 0.01563144 0.01198316 0.01194787 - 0.01196098 0.01196241 0.01197839 0.0123086 ] - -mean value: 0.01236574649810791 - -key: test_mcc -value: [0.51639778 0.40451992 0.46428571 0.60714286 0.6000992 0.34247476 - 0.47245559 0.60714286 0.73214286 0.37796447] - -mean value: 0.5124626004817079 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.71428571 0.61538462 0.71428571 0.8 0.76923077 0.54545455 - 0.77777778 0.8 0.875 0.61538462] - -mean value: 0.7226803751803752 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.83333333 0.8 0.71428571 0.75 0.83333333 0.75 - 0.7 0.85714286 0.875 0.8 ] - -mean value: 0.7913095238095238 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.5 0.71428571 0.85714286 0.71428571 0.42857143 - 0.875 0.75 0.875 0.5 ] - -mean value: 0.6839285714285713 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.6875 0.73333333 0.8 0.8 0.66666667 - 0.73333333 0.8 0.86666667 0.66666667] - -mean value: 0.7504166666666667 - -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 0.6875 0.73214286 0.80357143 0.79464286 0.65178571 - 0.72321429 0.80357143 0.86607143 0.67857143] - -mean value: 0.7491071428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.55555556 0.44444444 0.55555556 0.66666667 0.625 0.375 - 0.63636364 0.66666667 0.77777778 0.44444444] - -mean value: 0.5747474747474748 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 62 - -mean value: 62.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 52 - -mean value: 52.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.79 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.01560283 0.01606917 0.01327395 0.01165366 0.01096725 0.0107851 - 0.01119351 0.0121634 0.01090837 0.01211286] - -mean value: 0.012473011016845703 - -key: score_time -value: [0.0116744 0.0098846 0.00842261 0.00833201 0.0082736 0.00857115 - 0.00834417 0.00823522 0.00829744 0.00839138] - -mean value: 0.008842658996582032 - -key: test_mcc -value: [0.8819171 0.8819171 0.87287156 0.6000992 0.75592895 0.87287156 - 0.87287156 0.60714286 0.875 0.60714286] - -mean value: 0.782776274866145 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93333333 0.94117647 0.92307692 0.76923077 0.83333333 0.92307692 - 0.94117647 0.8 0.93333333 0.8 ] - -mean value: 0.8797737556561087 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88888889 1. 0.83333333 1. 1. - 0.88888889 0.85714286 1. 0.85714286] - -mean value: 0.9325396825396826 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 1. 0.85714286 0.71428571 0.71428571 0.85714286 - 1. 0.75 0.875 0.75 ] - -mean value: 0.8392857142857142 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.9375 0.93333333 0.8 0.86666667 0.93333333 - 0.93333333 0.8 0.93333333 0.8 ] - -mean value: 0.8875 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.9375 0.92857143 0.79464286 0.85714286 0.92857143 - 0.92857143 0.80357143 0.9375 0.80357143] - -mean value: 0.8857142857142858 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.875 0.88888889 0.85714286 0.625 0.71428571 0.85714286 - 0.88888889 0.66666667 0.875 0.66666667] - -mean value: 0.7914682539682539 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 71 - -mean value: 71.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 5 - -mean value: 5.0 - -key: TP -value: 64 - -mean value: 64.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.89 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.09069848 0.08667779 0.08607292 0.08668923 0.08903408 0.08655572 - 0.08875108 0.09235215 0.08659458 0.0865767 ] - -mean value: 0.08800027370452881 - -key: score_time -value: [0.01674008 0.01685047 0.01681376 0.01759171 0.01680708 0.01680541 - 0.01830816 0.01680827 0.01712513 0.01683068] - -mean value: 0.017068076133728027 - -key: test_mcc -value: [0.5 0.62994079 0.37796447 0.6000992 0.33928571 0.6000992 - 0.64465837 0.60714286 1. 0.6000992 ] - -mean value: 0.5899289798453752 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.75 0.8 0.70588235 0.76923077 0.66666667 0.76923077 - 0.84210526 0.8 1. 0.82352941] - -mean value: 0.7926645232991982 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.75 0.85714286 0.6 0.83333333 0.625 0.83333333 - 0.72727273 0.85714286 1. 0.77777778] - -mean value: 0.7861002886002886 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 - 1. 0.75 1. 0.875 ] - -mean value: 0.8125 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.75 0.8125 0.66666667 0.8 0.66666667 0.8 - 0.8 0.8 1. 0.8 ] - -mean value: 0.7895833333333333 - -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 0.8125 0.67857143 0.79464286 0.66964286 0.79464286 - 0.78571429 0.80357143 1. 0.79464286] - -mean value: 0.7883928571428571 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.6 0.66666667 0.54545455 0.625 0.5 0.625 - 0.72727273 0.66666667 1. 0.7 ] - -mean value: 0.6656060606060606 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 58 - -mean value: 58.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 18 - -mean value: 18.0 - -key: TP -value: 62 - -mean value: 62.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.71 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.00859261 0.00851965 0.00834799 0.00839686 0.00845599 0.00841427 - 0.00834894 0.00838566 0.00838327 0.00876045] - -mean value: 0.008460569381713866 - -key: score_time -value: [0.00829458 0.00828695 0.00835848 0.00816917 0.00823879 0.00826216 - 0.00824952 0.00828099 0.0082531 0.00842953] - -mean value: 0.008282327651977539 - -key: test_mcc -value: [0.62994079 0.37796447 0.49099025 0.33928571 0.20044593 0.75592895 - 0.09449112 0.21821789 0.56407607 0.46428571] - -mean value: 0.41356269037191895 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.82352941 0.66666667 0.75 0.66666667 0.4 0.83333333 - 0.46153846 0.57142857 0.66666667 0.75 ] - -mean value: 0.6589829778065073 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.77777778 0.71428571 0.66666667 0.625 0.66666667 1. - 0.6 0.66666667 1. 0.75 ] - -mean value: 0.7467063492063492 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 0.625 0.85714286 0.71428571 0.28571429 0.71428571 - 0.375 0.5 0.5 0.75 ] - -mean value: 0.6196428571428572 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.6875 0.73333333 0.66666667 0.6 0.86666667 - 0.53333333 0.6 0.73333333 0.73333333] - -mean value: 0.6966666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.6875 0.74107143 0.66964286 0.58035714 0.85714286 - 0.54464286 0.60714286 0.75 0.73214286] - -mean value: 0.6982142857142857 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.7 0.5 0.6 0.5 0.25 0.71428571 - 0.3 0.4 0.5 0.6 ] - -mean value: 0.5064285714285713 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 59 - -mean value: 59.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 47 - -mean value: 47.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: -0.17 - -Accuracy on Blind test: 0.45 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.12350488 1.12175465 1.12073541 1.12479877 1.1150465 1.11790395 - 1.12138486 1.13313246 1.12897301 1.12279344] - -mean value: 1.123002791404724 - -key: score_time -value: [0.08789635 0.08756709 0.08721948 0.08704138 0.08737206 0.0873611 - 0.08692431 0.09309626 0.08746028 0.0869782 ] - -mean value: 0.08789165019989013 - -key: test_mcc -value: [0.62994079 0.62994079 0.73214286 0.6000992 0.6000992 0.75592895 - 0.64465837 0.73214286 1. 0.87287156] - -mean value: 0.7197824565463824 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.8 0.8 0.85714286 0.76923077 0.76923077 0.83333333 - 0.84210526 0.875 1. 0.94117647] - -mean value: 0.8487219462683859 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.85714286 0.85714286 0.83333333 0.83333333 1. - 0.72727273 0.875 1. 0.88888889] - -mean value: 0.8729256854256855 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 - 1. 0.875 1. 1. ] - -mean value: 0.8375 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.8125 0.8125 0.86666667 0.8 0.8 0.86666667 - 0.8 0.86666667 1. 0.93333333] - -mean value: 0.8558333333333333 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.8125 0.8125 0.86607143 0.79464286 0.79464286 0.85714286 - 0.78571429 0.86607143 1. 0.92857143] - -mean value: 0.8517857142857143 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.66666667 0.66666667 0.75 0.625 0.625 0.71428571 - 0.72727273 0.77777778 1. 0.88888889] - -mean value: 0.7441558441558442 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 66 - -mean value: 66.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 64 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -mean value: 64.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.84 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.79375076 0.85313535 0.81692481 0.92896914 0.86560106 0.92556882 - 0.86164188 0.86464143 0.81101966 0.85759234] - -mean value: 0.8578845262527466 - -key: score_time -value: [0.19001794 0.19530773 0.17721558 0.17968392 0.17640471 0.17840695 - 0.15903306 0.17607164 0.18895531 0.16712546] - -mean value: 0.17882223129272462 - -key: test_mcc -value: [0.77459667 0.62994079 0.49099025 0.73214286 0.60714286 0.53452248 - 0.64465837 0.73214286 0.73214286 0.87287156] - -mean value: 0.6751151555181728 - -key: train_mcc -value: [0.92737353 0.92737353 0.91392776 0.92787101 0.9158731 0.92787101 - 0.9139999 0.91281179 0.92791659 0.92791659] - -mean value: 0.9222934807228448 - -key: test_fscore -value: [0.85714286 0.8 0.75 0.85714286 0.8 0.6 - 0.84210526 0.875 0.875 0.94117647] - -mean value: 0.8197567448031844 - -key: train_fscore -value: [0.96402878 0.96402878 0.95774648 0.96453901 0.95833333 0.96453901 - 0.95714286 0.95652174 0.96402878 0.96402878] - -mean value: 0.9614937530577929 - -key: test_precision -value: [1. 0.85714286 0.66666667 0.85714286 0.75 1. - 0.72727273 0.875 0.875 0.88888889] - -mean value: 0.8497113997113998 - -key: train_precision -value: [0.94366197 0.94366197 0.93150685 0.94444444 0.92 0.94444444 - 0.93055556 0.94285714 0.94366197 0.94366197] - -mean value: 0.9388456323940598 - -key: test_recall -value: [0.75 0.75 0.85714286 0.85714286 0.85714286 0.42857143 - 1. 0.875 0.875 1. ] - -mean value: 0.825 - -key: train_recall -value: [0.98529412 0.98529412 0.98550725 0.98550725 1. 0.98550725 - 0.98529412 0.97058824 0.98529412 0.98529412] - -mean value: 0.9853580562659847 - -key: test_accuracy -value: [0.875 0.8125 0.73333333 0.86666667 0.8 0.73333333 - 0.8 0.86666667 0.86666667 0.93333333] - -mean value: 0.8287500000000001 - -key: train_accuracy -value: [0.96323529 0.96323529 0.95620438 0.96350365 0.95620438 0.96350365 - 0.95620438 0.95620438 0.96350365 0.96350365] - -mean value: 0.9605302705023616 - -key: test_roc_auc -value: [0.875 0.8125 0.74107143 0.86607143 0.80357143 0.71428571 - 0.78571429 0.86607143 0.86607143 0.92857143] - -mean value: 0.8258928571428571 - -key: train_roc_auc -value: [0.96323529 0.96323529 0.95598892 0.96334186 0.95588235 0.96334186 - 0.95641517 0.95630861 0.96366155 0.96366155] - -mean value: 0.9605072463768115 - -key: test_jcc -value: [0.75 0.66666667 0.6 0.75 0.66666667 0.42857143 - 0.72727273 0.77777778 0.77777778 0.88888889] - -mean value: 0.7033621933621934 - -key: train_jcc -value: [0.93055556 0.93055556 0.91891892 0.93150685 0.92 0.93150685 - 0.91780822 0.91666667 0.93055556 0.93055556] - -mean value: 0.9258629725616027 - -key: TN -value: 63 - -mean value: 63.0 - -key: FP -value: 13 - -mean value: 13.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 63 - -mean value: 63.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.06579757 0.04870868 0.04732394 0.0479579 0.04998922 0.04546762 - 0.04415822 0.04513502 0.04604268 0.04867816] - -mean value: 0.04892590045928955 - -key: score_time -value: [0.01023173 0.01019073 0.01020455 0.01103163 0.01026368 0.01042342 - 0.01025009 0.01019382 0.01028919 0.01038814] - -mean value: 0.010346698760986327 - -key: test_mcc -value: [0.77459667 0.8819171 0.73214286 0.875 0.75592895 1. - 0.64465837 0.73214286 1. 0.87287156] - -mean value: 0.8269258365398123 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.85714286 0.94117647 0.85714286 0.93333333 0.83333333 1. - 0.84210526 0.875 1. 0.94117647] - -mean value: 0.9080410585286746 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88888889 0.85714286 0.875 1. 1. - 0.72727273 0.875 1. 0.88888889] - -mean value: 0.9112193362193363 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.75 1. 0.85714286 1. 0.71428571 1. - 1. 0.875 1. 1. ] - -mean value: 0.9196428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.9375 0.86666667 0.93333333 0.86666667 1. - 0.8 0.86666667 1. 0.93333333] - -mean value: 0.9079166666666666 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.9375 0.86607143 0.9375 0.85714286 1. - 0.78571429 0.86607143 1. 0.92857143] - -mean value: 0.9053571428571429 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.75 0.88888889 0.75 0.875 0.71428571 1. - 0.72727273 0.77777778 1. 0.88888889] - -mean value: 0.8372113997113997 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 68 - -mean value: 68.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 70 - -mean value: 70.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.83 - -Accuracy on Blind test: 0.92 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.02466106 0.06205916 0.04544544 0.05353069 0.06253004 0.04796767 - 0.02154779 0.02102947 0.02075434 0.04984069] - -mean value: 0.040936636924743655 - -key: score_time -value: [0.01995134 0.02262306 0.02079415 0.0347898 0.02177382 0.01164317 - 0.01162004 0.01168466 0.01153636 0.02154279] - -mean value: 0.01879591941833496 - -key: test_mcc -value: [0.51639778 0.40451992 0.21821789 0.75592895 0.32732684 0.18898224 - 0.21821789 0.33928571 0.60714286 0.37796447] - -mean value: 0.3953984539759108 - -key: train_mcc -value: [1. 1. 1. 0.98550725 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_fscore -value: [0.71428571 0.61538462 0.625 0.83333333 0.61538462 0.5 - 0.57142857 0.66666667 0.8 0.61538462] - -mean value: 0.6556868131868132 - -key: train_fscore -value: [1. 1. 1. 0.99270073 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_precision -value: [0.83333333 0.8 0.55555556 1. 0.66666667 0.6 - 0.66666667 0.71428571 0.85714286 0.8 ] - -mean value: 0.7493650793650793 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.5 0.71428571 0.71428571 0.57142857 0.42857143 - 0.5 0.625 0.75 0.5 ] - -mean value: 0.5928571428571429 - -key: train_recall -value: [1. 1. 1. 0.98550725 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: test_accuracy -value: [0.75 0.6875 0.6 0.86666667 0.66666667 0.6 - 0.6 0.66666667 0.8 0.66666667] - -mean value: 0.6904166666666667 - -key: train_accuracy -value: [1. 1. 1. 0.99270073 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992700729927007 - -key: test_roc_auc -value: [0.75 0.6875 0.60714286 0.85714286 0.66071429 0.58928571 - 0.60714286 0.66964286 0.80357143 0.67857143] - -mean value: 0.6910714285714287 - -key: train_roc_auc -value: [1. 1. 1. 0.99275362 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9992753623188406 - -key: test_jcc -value: [0.55555556 0.44444444 0.45454545 0.71428571 0.44444444 0.33333333 - 0.4 0.5 0.66666667 0.44444444] - -mean value: 0.49577200577200575 - -key: train_jcc -value: [1. 1. 1. 0.98550725 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9985507246376812 - -key: TN -value: 60 - -mean value: 60.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.71 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02083278 0.00883913 0.0087893 0.00854588 0.00853181 0.00851607 - 0.00848556 0.00949192 0.00852609 0.00841713] - -mean value: 0.009897565841674805 - -key: score_time -value: [0.01730442 0.00882506 0.00852299 0.00838923 0.00841427 0.00838327 - 0.00844216 0.00907063 0.00833464 0.00883269] - -mean value: 0.009451937675476075 - -key: test_mcc -value: [ 0.13483997 0.62994079 -0.18898224 0.87287156 0.07142857 0.07142857 - 0.34247476 0.19642857 0.46428571 0.60714286] - -mean value: 0.32018591307057886 - -key: train_mcc -value: [0.44136741 0.41337702 0.4457507 0.46009427 0.50373224 0.47592003 - 0.53458044 0.4457507 0.50427283 0.5182264 ] - -mean value: 0.47430720433398815 - -key: test_fscore -value: [0.46153846 0.8 0.47058824 0.92307692 0.53333333 0.53333333 - 0.73684211 0.625 0.75 0.8 ] - -mean value: 0.6633712391839327 - -key: train_fscore -value: [0.72463768 0.71830986 0.73239437 0.73758865 0.75714286 0.75 - 0.75384615 0.71212121 0.74242424 0.75555556] - -mean value: 0.7384020580083823 - -key: test_precision -value: [0.6 0.85714286 0.4 1. 0.5 0.5 - 0.63636364 0.625 0.75 0.85714286] - -mean value: 0.672564935064935 - -key: train_precision -value: [0.71428571 0.68918919 0.71232877 0.72222222 0.74647887 0.72 - 0.79032258 0.734375 0.765625 0.76119403] - -mean value: 0.7356021376555757 - -key: test_recall -value: [0.375 0.75 0.57142857 0.85714286 0.57142857 0.57142857 - 0.875 0.625 0.75 0.75 ] - -mean value: 0.6696428571428571 - -key: train_recall -value: [0.73529412 0.75 0.75362319 0.75362319 0.76811594 0.7826087 - 0.72058824 0.69117647 0.72058824 0.75 ] - -mean value: 0.7425618073316282 - -key: test_accuracy -value: [0.5625 0.8125 0.4 0.93333333 0.53333333 0.53333333 - 0.66666667 0.6 0.73333333 0.8 ] - -mean value: 0.6575 - -key: train_accuracy -value: [0.72058824 0.70588235 0.72262774 0.72992701 0.75182482 0.73722628 - 0.76642336 0.72262774 0.75182482 0.75912409] - -mean value: 0.7368076427651352 - -key: test_roc_auc -value: [0.5625 0.8125 0.41071429 0.92857143 0.53571429 0.53571429 - 0.65178571 0.59821429 0.73214286 0.80357143] - -mean value: 0.6571428571428571 - -key: train_roc_auc -value: [0.72058824 0.70588235 0.72239983 0.72975277 0.75170503 0.73689258 - 0.76609122 0.72239983 0.75159847 0.75905797] - -mean value: 0.7366368286445013 - -key: test_jcc -value: [0.3 0.66666667 0.30769231 0.85714286 0.36363636 0.36363636 - 0.58333333 0.45454545 0.6 0.66666667] - -mean value: 0.5163320013320013 - -key: train_jcc -value: [0.56818182 0.56043956 0.57777778 0.58426966 0.6091954 0.6 - 0.60493827 0.55294118 0.59036145 0.60714286] - -mean value: 0.5855247972620871 - -key: TN -value: 49 - -mean value: 49.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 51 - -mean value: 51.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.68 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.00937438 0.01451468 0.01359081 0.01436234 0.01521063 0.01441789 - 0.01421762 0.01421499 0.01419473 0.01438093] - -mean value: 0.013847899436950684 - -key: score_time -value: [0.00882316 0.01088619 0.01098871 0.01142168 0.01146245 0.01141715 - 0.0114491 0.01136684 0.01177049 0.01148844] - -mean value: 0.011107420921325684 - -key: test_mcc -value: [0.28867513 0.57735027 0.47245559 0.66143783 0.47245559 0.28571429 - 0.26189246 0.73214286 0.56407607 0.36689969] - -mean value: 0.4683099787909484 - -key: train_mcc -value: [0.73854895 0.92657079 0.63749097 0.80014442 0.89869927 0.58151892 - 0.41647809 0.91281179 0.56742751 0.65830386] - -mean value: 0.7137994554372971 - -key: test_fscore -value: [0.7 0.66666667 0.66666667 0.82352941 0.66666667 0.25 - 0.5 0.875 0.66666667 0.4 ] - -mean value: 0.6215196078431373 - -key: train_fscore -value: [0.87179487 0.96296296 0.73394495 0.90196078 0.94814815 0.67307692 - 0.45454545 0.95652174 0.65346535 0.75229358] - -mean value: 0.7908714762617265 - -key: test_precision -value: [0.58333333 1. 0.8 0.7 0.8 1. - 0.75 0.875 1. 1. ] - -mean value: 0.8508333333333333 - -key: train_precision -value: [0.77272727 0.97014925 1. 0.82142857 0.96969697 1. - 1. 0.94285714 1. 1. ] - -mean value: 0.9476859210441301 - -key: test_recall -value: [0.875 0.5 0.57142857 1. 0.57142857 0.14285714 - 0.375 0.875 0.5 0.25 ] - -mean value: 0.5660714285714286 - -key: train_recall -value: [1. 0.95588235 0.57971014 1. 0.92753623 0.50724638 - 0.29411765 0.97058824 0.48529412 0.60294118] - -mean value: 0.7323316283034953 - -key: test_accuracy -value: [0.625 0.75 0.73333333 0.8 0.73333333 0.6 - 0.6 0.86666667 0.73333333 0.6 ] - -mean value: 0.7041666666666666 - -key: train_accuracy -value: [0.85294118 0.96323529 0.78832117 0.89051095 0.94890511 0.75182482 - 0.64963504 0.95620438 0.74452555 0.80291971] - -mean value: 0.8349023185916703 - -key: test_roc_auc -value: [0.625 0.75 0.72321429 0.8125 0.72321429 0.57142857 - 0.61607143 0.86607143 0.75 0.625 ] - -mean value: 0.70625 - -key: train_roc_auc -value: [0.85294118 0.96323529 0.78985507 0.88970588 0.94906223 0.75362319 - 0.64705882 0.95630861 0.74264706 0.80147059] - -mean value: 0.8345907928388746 - -key: test_jcc -value: [0.53846154 0.5 0.5 0.7 0.5 0.14285714 - 0.33333333 0.77777778 0.5 0.25 ] - -mean value: 0.47424297924297926 - -key: train_jcc -value: [0.77272727 0.92857143 0.57971014 0.82142857 0.90140845 0.50724638 - 0.29411765 0.91666667 0.48529412 0.60294118] - -mean value: 0.6810111853013765 - -key: TN -value: 64 - -mean value: 64.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 43 - -mean value: 43.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.87 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01372719 0.01259232 0.01290774 0.01390266 0.01349616 0.01290226 - 0.01301146 0.01315951 0.01388788 0.01293182] - -mean value: 0.013251900672912598 - -key: score_time -value: [0.01121998 0.01127958 0.01127815 0.01130819 0.01134586 0.01132011 - 0.01129007 0.01129627 0.01130867 0.01120424] - -mean value: 0.011285114288330077 - -key: test_mcc -value: [0.77459667 0.62994079 0.21821789 0.73214286 0.41931393 0.41931393 - 0.49099025 0.60714286 0.73214286 1. ] - -mean value: 0.6023802041663276 - -key: train_mcc -value: [0.94158382 0.78357455 0.82788248 0.91392776 0.75258453 0.78803902 - 0.70450233 0.8978896 0.88920184 0.79573573] - -mean value: 0.8294921649284455 - -key: test_fscore -value: [0.85714286 0.8 0.625 0.85714286 0.44444444 0.44444444 - 0.71428571 0.8 0.875 1. ] - -mean value: 0.7417460317460318 - -key: train_fscore -value: [0.97014925 0.8951049 0.91666667 0.95774648 0.84033613 0.88372093 - 0.79646018 0.94814815 0.9375 0.89932886] - -mean value: 0.9045161543262183 - -key: test_precision -value: [1. 0.85714286 0.55555556 0.85714286 1. 1. - 0.83333333 0.85714286 0.875 1. ] - -mean value: 0.883531746031746 - -key: train_precision -value: [0.98484848 0.85333333 0.88 0.93150685 1. 0.95 - 1. 0.95522388 1. 0.82716049] - -mean value: 0.9382073041921062 - -key: test_recall -value: [0.75 0.75 0.71428571 0.85714286 0.28571429 0.28571429 - 0.625 0.75 0.875 1. ] - -mean value: 0.6892857142857143 - -key: train_recall -value: [0.95588235 0.94117647 0.95652174 0.98550725 0.72463768 0.82608696 - 0.66176471 0.94117647 0.88235294 0.98529412] - -mean value: 0.8860400682011935 - -key: test_accuracy -value: [0.875 0.8125 0.6 0.86666667 0.66666667 0.66666667 - 0.73333333 0.8 0.86666667 1. ] - -mean value: 0.78875 - -key: train_accuracy -value: [0.97058824 0.88970588 0.91240876 0.95620438 0.86131387 0.89051095 - 0.83211679 0.94890511 0.94160584 0.89051095] - -mean value: 0.9093870759982826 - -key: test_roc_auc -value: [0.875 0.8125 0.60714286 0.86607143 0.64285714 0.64285714 - 0.74107143 0.80357143 0.86607143 1. ] - -mean value: 0.7857142857142858 - -key: train_roc_auc -value: [0.97058824 0.88970588 0.9120844 0.95598892 0.86231884 0.89098465 - 0.83088235 0.9488491 0.94117647 0.89119778] - -mean value: 0.9093776641091219 - -key: test_jcc -value: [0.75 0.66666667 0.45454545 0.75 0.28571429 0.28571429 - 0.55555556 0.66666667 0.77777778 1. ] - -mean value: 0.6192640692640692 - -key: train_jcc -value: [0.94202899 0.81012658 0.84615385 0.91891892 0.72463768 0.79166667 - 0.66176471 0.90140845 0.88235294 0.81707317] - -mean value: 0.8296131949179335 - -key: TN -value: 67 - -mean value: 67.0 - -key: FP -value: 23 - -mean value: 23.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 53 - -mean value: 53.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.89 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.11206031 0.09339643 0.092906 0.09413314 0.09428453 0.0976553 - 0.09314227 0.09387136 0.09383416 0.0931735 ] - -mean value: 0.09584569931030273 - -key: score_time -value: [0.01528907 0.0143764 0.01447558 0.01455736 0.02002788 0.01480746 - 0.01442051 0.014539 0.01443863 0.01441383] - -mean value: 0.015134572982788086 - -key: test_mcc -value: [0.8819171 0.8819171 0.87287156 0.875 0.87287156 0.87287156 - 0.64465837 0.73214286 0.875 0.87287156] - -mean value: 0.8382121679515432 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93333333 0.94117647 0.92307692 0.93333333 0.92307692 0.92307692 - 0.84210526 0.875 0.93333333 0.94117647] - -mean value: 0.9168688973565133 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88888889 1. 0.875 1. 1. - 0.72727273 0.875 1. 0.88888889] - -mean value: 0.9255050505050505 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 1. 0.85714286 1. 0.85714286 0.85714286 - 1. 0.875 0.875 1. ] - -mean value: 0.9196428571428571 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.9375 0.93333333 0.93333333 0.93333333 0.93333333 - 0.8 0.86666667 0.93333333 0.93333333] - -mean value: 0.9141666666666668 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.9375 0.92857143 0.9375 0.92857143 0.92857143 - 0.78571429 0.86607143 0.9375 0.92857143] - -mean value: 0.9116071428571428 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.875 0.88888889 0.85714286 0.875 0.85714286 0.85714286 - 0.72727273 0.77777778 0.875 0.88888889] - -mean value: 0.8479256854256855 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 69 - -mean value: 69.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 70 - -mean value: 70.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.84 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.02656746 0.02978373 0.03765631 0.03283286 0.04684496 0.04813409 - 0.03314996 0.04111743 0.0357182 0.03724337] - -mean value: 0.03690483570098877 - -key: score_time -value: [0.01678157 0.01610088 0.02883625 0.01758456 0.03211522 0.01859903 - 0.01993012 0.02483487 0.02393889 0.02293921] - -mean value: 0.022166061401367187 - -key: test_mcc -value: [0.77459667 0.8819171 0.875 0.47245559 0.64465837 0.87287156 - 0.75592895 0.60714286 1. 0.87287156] - -mean value: 0.7757442660460769 - -key: train_mcc -value: [0.98540068 0.98540068 1. 0.98550418 0.98550725 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.992732002454171 - -key: test_fscore -value: [0.85714286 0.94117647 0.93333333 0.66666667 0.72727273 0.92307692 - 0.88888889 0.8 1. 0.94117647] - -mean value: 0.8678734337557866 - -key: train_fscore -value: [0.99259259 0.99259259 1. 0.99280576 0.99270073 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9963392400434884 - -key: test_precision -value: [1. 0.88888889 0.875 0.8 1. 1. - 0.8 0.85714286 1. 0.88888889] - -mean value: 0.9109920634920636 - -key: train_precision -value: [1. 1. 1. 0.98571429 1. 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9971221532091097 - -key: test_recall -value: [0.75 1. 1. 0.57142857 0.57142857 0.85714286 - 1. 0.75 1. 1. ] - -mean value: 0.85 - -key: train_recall -value: [0.98529412 0.98529412 1. 1. 0.98550725 1. - 1. 1. 1. 1. ] - -mean value: 0.9956095481670928 - -key: test_accuracy -value: [0.875 0.9375 0.93333333 0.73333333 0.8 0.93333333 - 0.86666667 0.8 1. 0.93333333] - -mean value: 0.88125 - -key: train_accuracy -value: [0.99264706 0.99264706 1. 0.99270073 0.99270073 1. - 1. 0.99270073 1. 1. ] - -mean value: 0.9963396307428081 - -key: test_roc_auc -value: [0.875 0.9375 0.9375 0.72321429 0.78571429 0.92857143 - 0.85714286 0.80357143 1. 0.92857143] - -mean value: 0.8776785714285713 - -key: train_roc_auc -value: [0.99264706 0.99264706 1. 0.99264706 0.99275362 1. - 1. 0.99275362 1. 1. ] - -mean value: 0.99634484228474 - -key: test_jcc -value: [0.75 0.88888889 0.875 0.5 0.57142857 0.85714286 - 0.8 0.66666667 1. 0.88888889] - -mean value: 0.7798015873015872 - -key: train_jcc -value: [0.98529412 0.98529412 1. 0.98571429 0.98550725 1. - 1. 0.98550725 1. 1. ] - -mean value: 0.9927317013762027 - -key: TN -value: 69 - -mean value: 69.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 65 - -mean value: 65.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.9 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.03322458 0.04529357 0.0201118 0.01896811 0.01995182 0.03240466 - 0.04606581 0.04578567 0.04500175 0.04564452] - -mean value: 0.035245227813720706 - -key: score_time -value: [0.02058101 0.02035928 0.01196337 0.01195431 0.0119977 0.02226043 - 0.020432 0.02399397 0.02383852 0.02329302] - -mean value: 0.01906735897064209 - -key: test_mcc -value: [ 0.37796447 0.40451992 0.21821789 0.47245559 -0.09449112 0.47245559 - 0.19642857 0.21821789 0.49099025 0.73214286] - -mean value: 0.348890191683233 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.61538462 0.625 0.66666667 0.33333333 0.66666667 - 0.625 0.57142857 0.71428571 0.875 ] - -mean value: 0.6359432234432234 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.71428571 0.8 0.55555556 0.8 0.4 0.8 - 0.625 0.66666667 0.83333333 0.875 ] - -mean value: 0.7069841269841269 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.625 0.5 0.71428571 0.57142857 0.28571429 0.57142857 - 0.625 0.5 0.625 0.875 ] - -mean value: 0.5892857142857142 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.6875 0.6875 0.6 0.73333333 0.46666667 0.73333333 - 0.6 0.6 0.73333333 0.86666667] - -mean value: 0.6708333333333332 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.6875 0.6875 0.60714286 0.72321429 0.45535714 0.72321429 - 0.59821429 0.60714286 0.74107143 0.86607143] - -mean value: 0.6696428571428571 - -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 0.44444444 0.45454545 0.5 0.2 0.5 - 0.45454545 0.4 0.55555556 0.77777778] - -mean value: 0.47868686868686866 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 57 - -mean value: 57.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 45 - -mean value: 45.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.09 - -Accuracy on Blind test: 0.56 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.28226662 0.25447321 0.25262737 0.25650167 0.25346684 0.25236917 - 0.25692153 0.25983357 0.2588613 0.25281692] - -mean value: 0.25801382064819334 - -key: score_time -value: [0.00900578 0.0091064 0.00908732 0.00908399 0.00883031 0.00888848 - 0.00898743 0.00883675 0.00886703 0.00891113] - -mean value: 0.008960461616516114 - -key: test_mcc -value: [0.8819171 0.8819171 0.875 0.6000992 0.75592895 0.64465837 - 0.75592895 0.73214286 1. 0.87287156] - -mean value: 0.8000464086869412 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.93333333 0.94117647 0.93333333 0.76923077 0.83333333 0.72727273 - 0.88888889 0.875 1. 0.94117647] - -mean value: 0.8842745326568856 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88888889 0.875 0.83333333 1. 1. - 0.8 0.875 1. 0.88888889] - -mean value: 0.9161111111111111 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.875 1. 1. 0.71428571 0.71428571 0.57142857 - 1. 0.875 1. 1. ] - -mean value: 0.875 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.9375 0.9375 0.93333333 0.8 0.86666667 0.8 - 0.86666667 0.86666667 1. 0.93333333] - -mean value: 0.8941666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.9375 0.9375 0.9375 0.79464286 0.85714286 0.78571429 - 0.85714286 0.86607143 1. 0.92857143] - -mean value: 0.8901785714285715 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.875 0.88888889 0.875 0.625 0.71428571 0.57142857 - 0.8 0.77777778 1. 0.88888889] - -mean value: 0.8016269841269841 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 69 - -mean value: 69.0 - -key: FP -value: 9 - -mean value: 9.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 67 - -mean value: 67.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.79 - -Accuracy on Blind test: 0.9 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis()/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.01454568 0.02235794 0.01665139 0.0166266 0.01675177 0.01677036 - 0.01663136 0.01952362 0.01697612 0.01721311] - -mean value: 0.017404794692993164 - -key: score_time -value: [0.01141596 0.01199937 0.01192641 0.01170635 0.01309991 0.01283741 - 0.01319933 0.01196384 0.01391196 0.01398873] - -mean value: 0.012604928016662598 - -key: test_mcc -value: [0.77459667 0.77459667 0.56407607 0.66143783 0.66143783 0.76376262 - 0.41931393 0.75592895 0.87287156 0.64465837] - -mean value: 0.6892680497530497 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.88888889 0.88888889 0.77777778 0.82352941 0.82352941 0.875 - 0.76190476 0.88888889 0.94117647 0.84210526] - -mean value: 0.851168976362475 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.8 0.63636364 0.7 0.7 0.77777778 - 0.61538462 0.8 0.88888889 0.72727273] - -mean value: 0.7445687645687646 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.875 0.875 0.73333333 0.8 0.8 0.86666667 - 0.66666667 0.86666667 0.93333333 0.8 ] - -mean value: 0.8216666666666667 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.875 0.875 0.75 0.8125 0.8125 0.875 - 0.64285714 0.85714286 0.92857143 0.78571429] - -mean value: 0.8214285714285715 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.8 0.8 0.63636364 0.7 0.7 0.77777778 - 0.61538462 0.8 0.88888889 0.72727273] - -mean value: 0.7445687645687646 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 49 - -mean value: 49.0 - -key: FP -value: 0 - -mean value: 0.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 76 - -mean value: 76.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.0 - -Accuracy on Blind test: 0.61 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('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: [0.02903247 0.0127809 0.01289296 0.01284266 0.01288414 0.03248739 - 0.04138494 0.03767085 0.04280519 0.04593873] - -mean value: 0.028072023391723634 - -key: score_time -value: [0.02271175 0.01150751 0.01150703 0.01153731 0.01144195 0.01955628 - 0.02092075 0.02405858 0.02117944 0.0240984 ] - -mean value: 0.017851901054382325 - -key: test_mcc -value: [0.62994079 0.67419986 0.73214286 0.73214286 0.46428571 0.64465837 - 0.64465837 0.87287156 0.875 0.76376262] - -mean value: 0.7033662998593934 - -key: train_mcc -value: [0.95681396 0.94158382 0.95629932 0.97122151 0.95629932 0.95630861 - 0.92791659 0.94160273 0.94160273 0.95629932] - -mean value: 0.950594790135199 - -key: test_fscore -value: [0.8 0.76923077 0.85714286 0.85714286 0.71428571 0.72727273 - 0.84210526 0.94117647 0.93333333 0.85714286] - -mean value: 0.8298832849297246 - -key: train_fscore -value: [0.97841727 0.97014925 0.97841727 0.98529412 0.97841727 0.97810219 - 0.96402878 0.97058824 0.97058824 0.97777778] - -mean value: 0.9751780385065005 - -key: test_precision -value: [0.85714286 1. 0.85714286 0.85714286 0.71428571 1. - 0.72727273 0.88888889 1. 1. ] - -mean value: 0.8901875901875901 - -key: train_precision -value: [0.95774648 0.98484848 0.97142857 1. 0.97142857 0.98529412 - 0.94366197 0.97058824 0.97058824 0.98507463] - -mean value: 0.9740659293510818 - -key: test_recall -value: [0.75 0.625 0.85714286 0.85714286 0.71428571 0.57142857 - 1. 1. 0.875 0.75 ] - -mean value: 0.8 - -key: train_recall -value: [1. 0.95588235 0.98550725 0.97101449 0.98550725 0.97101449 - 0.98529412 0.97058824 0.97058824 0.97058824] - -mean value: 0.9765984654731458 - -key: test_accuracy -value: [0.8125 0.8125 0.86666667 0.86666667 0.73333333 0.8 - 0.8 0.93333333 0.93333333 0.86666667] - -mean value: 0.8425 - -key: train_accuracy -value: [0.97794118 0.97058824 0.97810219 0.98540146 0.97810219 0.97810219 - 0.96350365 0.97080292 0.97080292 0.97810219] - -mean value: 0.9751449119793903 - -key: test_roc_auc -value: [0.8125 0.8125 0.86607143 0.86607143 0.73214286 0.78571429 - 0.78571429 0.92857143 0.9375 0.875 ] - -mean value: 0.8401785714285716 - -key: train_roc_auc -value: [0.97794118 0.97058824 0.97804774 0.98550725 0.97804774 0.97815431 - 0.96366155 0.97080136 0.97080136 0.97804774] - -mean value: 0.9751598465473146 - -key: test_jcc -value: [0.66666667 0.625 0.75 0.75 0.55555556 0.57142857 - 0.72727273 0.88888889 0.875 0.75 ] - -mean value: 0.715981240981241 - -key: train_jcc -value: [0.95774648 0.94202899 0.95774648 0.97101449 0.95774648 0.95714286 - 0.93055556 0.94285714 0.94285714 0.95652174] - -mean value: 0.9516217352423721 - -key: TN -value: 67 - -mean value: 67.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.84 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: 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 - rouC_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: 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 - rouC_CV['Resampling'] = rs_rouC -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: 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 - rouC_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: 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 - rouC_BT['Resampling'] = rs_rouC -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=166)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.1613934 0.19614768 0.19616151 0.1958096 0.19677949 0.1965363 - 0.1959517 0.19425201 0.28738642 0.32958293] - -mean value: 0.21500010490417482 - -key: score_time -value: [0.02007866 0.02105141 0.02255297 0.023417 0.02007699 0.02118158 - 0.02282619 0.02111578 0.02351999 0.02216053] - -mean value: 0.021798110008239745 - -key: test_mcc -value: [0.62994079 0.67419986 0.73214286 0.73214286 0.6000992 0.75592895 - 0.64465837 0.87287156 0.875 0.49099025] - -mean value: 0.7007974694460358 - -key: train_mcc -value: [0.98540068 0.94158382 0.97080136 0.97122151 0.98550725 0.98550725 - 0.92791659 0.98550418 0.98550418 0.97120941] - -mean value: 0.971015622159533 - -key: test_fscore -value: [0.8 0.76923077 0.85714286 0.85714286 0.76923077 0.83333333 - 0.84210526 0.94117647 0.93333333 0.71428571] - -mean value: 0.8316981367445763 - -key: train_fscore -value: [0.99270073 0.97014925 0.98550725 0.98529412 0.99270073 0.99270073 - 0.96402878 0.99259259 0.99259259 0.98507463] - -mean value: 0.9853341396565509 - -key: test_precision -value: [0.85714286 1. 0.85714286 0.85714286 0.83333333 1. - 0.72727273 0.88888889 1. 0.83333333] - -mean value: 0.8854256854256854 - -key: train_precision -value: [0.98550725 0.98484848 0.98550725 1. 1. 1. - 0.94366197 1. 1. 1. ] - -mean value: 0.9899524949433094 - -key: test_recall -value: [0.75 0.625 0.85714286 0.85714286 0.71428571 0.71428571 - 1. 1. 0.875 0.625 ] - -mean value: 0.8017857142857142 - -key: train_recall -value: [1. 0.95588235 0.98550725 0.97101449 0.98550725 0.98550725 - 0.98529412 0.98529412 0.98529412 0.97058824] - -mean value: 0.9809889173060528 - -key: test_accuracy -value: [0.8125 0.8125 0.86666667 0.86666667 0.8 0.86666667 - 0.8 0.93333333 0.93333333 0.73333333] - -mean value: 0.8425 - -key: train_accuracy -value: [0.99264706 0.97058824 0.98540146 0.98540146 0.99270073 0.99270073 - 0.96350365 0.99270073 0.99270073 0.98540146] - -mean value: 0.9853746243022756 - -key: test_roc_auc -value: [0.8125 0.8125 0.86607143 0.86607143 0.79464286 0.85714286 - 0.78571429 0.92857143 0.9375 0.74107143] - -mean value: 0.8401785714285716 - -key: train_roc_auc -value: [0.99264706 0.97058824 0.98540068 0.98550725 0.99275362 0.99275362 - 0.96366155 0.99264706 0.99264706 0.98529412] - -mean value: 0.9853900255754475 - -key: test_jcc -value: [0.66666667 0.625 0.75 0.75 0.625 0.71428571 - 0.72727273 0.88888889 0.875 0.55555556] - -mean value: 0.7177669552669552 - -key: train_jcc -value: [0.98550725 0.94202899 0.97142857 0.97101449 0.98550725 0.98550725 - 0.93055556 0.98529412 0.98529412 0.97058824] - -mean value: 0.9712725814963667 - -key: TN -value: 67 - -mean value: 67.0 - -key: FP -value: 15 - -mean value: 15.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 61 - -mean value: 61.0 - -key: trainingY_neg -value: 76 - -mean value: 76.0 - -key: trainingY_pos -value: 76 - -mean value: 76.0 - -key: blindY_neg -value: 24 - -mean value: 24.0 - -key: blindY_pos -value: 38 - -mean value: 38.0 - -MCC on Blind test: 0.65 - -Accuracy on Blind test: 0.82 - -PASS: sorting df by score that is mapped onto the order I want - -Concatenating dfs with different resampling methods [WF]: 70/30 -No. of dfs combining: 10 -The sampling methods are: - Resampling -Logistic Regression none -Logistic Regression smnc -Logistic Regression ros -Logistic Regression rus -Logistic Regression rouC - -PASS: 10 dfs successfully combined -nrows in combined_df_wf: 240 -ncols in combined_df_wf: 9 - -Concatenating dfs with different resampling methods: 70/30 -No. of dfs combining: 5 -The sampling methods are: - Resampling training_size -0 none 123 -24 smnc 152 -48 ros 152 -72 rus 94 -96 rouC 152 - -PASS: 5 dfs successfully combined -nrows in combined_df: 120 -ncols in combined_df: 32 - -File successfully written: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/pnca_baselineC_7030.csv - -File successfully written: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/pnca_baselineC_ext_7030.csv diff --git a/scripts/ml/log_rpob_7030.txt b/scripts/ml/log_rpob_7030.txt deleted file mode 100644 index 615c963..0000000 --- a/scripts/ml/log_rpob_7030.txt +++ /dev/null @@ -1,26172 +0,0 @@ -/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: -A value is trying to be set on a copy of a slice from a DataFrame - -See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy - mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) -/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/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -1.22.4 -1.4.1 - -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: 1133 - -PASS: my_features_df and aa_df successfully combined -nrows: 1133 -ncols: 274 -count of NULL values before imputation - -or_mychisq 339 -log10_or_mychisq 339 -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 - -PASS: x_features has no target variable - -No. of columns for x_features: 175 - -PASS: ML data with input features, training and test generated... - -Total no. of input features: 175 ---------No. of numerical features: 169 ---------No. of categorical features: 6 - -Total no. of evolutionary features: 3 - -Total no. of stability features: 28 ---------Common stabilty cols: 5 ---------Foldx cols: 23 - -Total no. of affinity features: 6 ---------Common affinity cols: 3 ---------Gene specific affinity cols: 3 - -Total no. of residue level features: 132 ---------AA index cols: 123 ---------Residue Prop cols: 3 ---------AA change Prop cols: 6 - -Total no. of genomic features: 6 ---------MAF+OR cols: 2 ---------Lineage cols: 4 ---------Other cols: 0 - -------------------------------------------------------------- -Successfully split data: ALL features -actual values: training set -imputed values: blind test set - -Total data size: 557 - -Train data size: (373, 175) -y_train numbers: Counter({0: 189, 1: 184}) - -Test data size: (184, 175) -y_test_numbers: Counter({0: 93, 1: 91}) - -y_train ratio: 1.0271739130434783 -y_test ratio: 1.021978021978022 -------------------------------------------------------------- - -index: 0 -ind: 1 - -Mask count check: True - -index: 1 -ind: 2 - -Mask count check: True - -index: 2 -ind: 3 - -Mask count check: True -Original Data - Counter({0: 189, 1: 184}) Data dim: (373, 175) -Simple Random OverSampling - Counter({1: 189, 0: 189}) -(378, 175) -Simple Random UnderSampling - Counter({0: 184, 1: 184}) -(368, 175) -Simple Combined Over and UnderSampling - Counter({0: 189, 1: 189}) -(378, 175) - -SMOTE_NC OverSampling - Counter({1: 189, 0: 189}) -(378, 175) - -##################################################################### - -Running ML analysis: feature groups -Gene name: rpoB -Drug name: rifampicin - -Output directory: /home/tanu/git/Data/rifampicin/output/ml/tts_7030/ - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegression(random_state=42))]) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -key: fit_time -value: [0.0343864 0.03471208 0.03470755 0.03553247 0.04428053 0.05908394 - 0.05134583 0.05877709 0.05582857 0.03485203] - -mean value: 0.04435064792633057 - -key: score_time -value: [0.01296234 0.01200461 0.01311469 0.01207113 0.01461744 0.01454163 - 0.01601505 0.02854133 0.01352239 0.01343131] - -mean value: 0.015082192420959473 - -key: test_mcc -value: [0.73786479 0.63960215 0.76376262 0.69356297 0.63129316 0.63129316 - 0.6754386 0.78362573 0.68035483 0.94721815] - -mean value: 0.7184016144856723 - -key: train_mcc -value: [0.85874337 0.87470584 0.85136485 0.87528201 0.86375895 0.84661008 - 0.86555037 0.88389869 0.85230019 0.84608954] - -mean value: 0.8618303886205002 - -key: test_fscore -value: [0.87179487 0.8 0.84848485 0.82352941 0.82051282 0.82051282 - 0.83333333 0.88888889 0.84210526 0.97142857] - -mean value: 0.8520590829878755 - -key: train_fscore -value: [0.9245283 0.93577982 0.92260062 0.93538462 0.92923077 0.91925466 - 0.92789969 0.93710692 0.92260062 0.91975309] - -mean value: 0.9274139090970248 - -key: test_precision -value: [0.85 0.875 1. 0.93333333 0.76190476 0.76190476 - 0.83333333 0.88888889 0.8 1. ] - -mean value: 0.870436507936508 - -key: train_precision -value: [0.96078431 0.94444444 0.94303797 0.95 0.94968553 0.94871795 - 0.96732026 0.98026316 0.94904459 0.94303797] - -mean value: 0.9536336196166074 - -key: test_recall -value: [0.89473684 0.73684211 0.73684211 0.73684211 0.88888889 0.88888889 - 0.83333333 0.88888889 0.88888889 0.94444444] - -mean value: 0.843859649122807 - -key: train_recall -value: [0.89090909 0.92727273 0.9030303 0.92121212 0.90963855 0.89156627 - 0.89156627 0.89759036 0.89759036 0.89759036] - -mean value: 0.9027966411098941 - -key: test_accuracy -value: [0.86842105 0.81578947 0.86842105 0.83783784 0.81081081 0.81081081 - 0.83783784 0.89189189 0.83783784 0.97297297] - -mean value: 0.8552631578947368 - -key: train_accuracy -value: [0.92835821 0.93731343 0.92537313 0.9375 0.93154762 0.92261905 - 0.93154762 0.94047619 0.92559524 0.92261905] - -mean value: 0.9302949538024166 - -key: test_roc_auc -value: [0.86842105 0.81578947 0.86842105 0.84064327 0.8128655 0.8128655 - 0.8377193 0.89181287 0.83918129 0.97222222] - -mean value: 0.8559941520467836 - -key: train_roc_auc -value: [0.92780749 0.93716578 0.92504456 0.93721425 0.93128987 0.92225372 - 0.93107725 0.93997165 0.92526577 0.92232459] - -mean value: 0.9299414922095739 - -key: test_jcc -value: [0.77272727 0.66666667 0.73684211 0.7 0.69565217 0.69565217 - 0.71428571 0.8 0.72727273 0.94444444] - -mean value: 0.745354327848607 - -key: train_jcc -value: [0.85964912 0.87931034 0.85632184 0.87861272 0.86781609 0.85057471 - 0.86549708 0.8816568 0.85632184 0.85142857] - -mean value: 0.864718911934192 - -key: TN -value: 164 - -mean value: 164.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 25 - -mean value: 25.0 - -key: TP -value: 155 - -mean value: 155.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.83 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.91629219 0.76209617 0.74195194 1.36273265 0.81091499 0.86507416 - 0.96326089 0.77209091 0.94134378 0.84567738] - -mean value: 0.8981435060501098 - -key: score_time -value: [0.01345372 0.01356983 0.01466441 0.01465106 0.01469493 0.01335931 - 0.0135088 0.01351452 0.01366925 0.01371694] - -mean value: 0.013880276679992675 - -key: test_mcc -value: [0.84327404 0.57894737 0.74620251 0.68035483 0.74044197 0.78362573 - 0.83918129 0.83871328 0.83918129 0.78362573] - -mean value: 0.7673548042593707 - -key: train_mcc -value: [0.98210148 1. 0.98805704 0.98809144 0.99406481 0.98215647 - 0.96454419 0.98215647 0.98809355 0.97625295] - -mean value: 0.9845518392444313 - -key: test_fscore -value: [0.92307692 0.78947368 0.85714286 0.83333333 0.87179487 0.88888889 - 0.91891892 0.91428571 0.91891892 0.88888889] - -mean value: 0.8804722999459841 - -key: train_fscore -value: [0.99088146 1. 0.99393939 0.99393939 0.996997 0.99093656 - 0.98170732 0.99093656 0.9939759 0.98787879] - -mean value: 0.9921192364191246 - -key: test_precision -value: [0.9 0.78947368 0.9375 0.88235294 0.80952381 0.88888889 - 0.89473684 0.94117647 0.89473684 0.88888889] - -mean value: 0.8827278367487346 - -key: train_precision -value: [0.99390244 1. 0.99393939 0.99393939 0.99401198 0.99393939 - 0.99382716 0.99393939 0.9939759 0.99390244] - -mean value: 0.9945377493962546 - -key: test_recall -value: [0.94736842 0.78947368 0.78947368 0.78947368 0.94444444 0.88888889 - 0.94444444 0.88888889 0.94444444 0.88888889] - -mean value: 0.881578947368421 - -key: train_recall -value: [0.98787879 1. 0.99393939 0.99393939 1. 0.98795181 - 0.96987952 0.98795181 0.9939759 0.98192771] - -mean value: 0.9897444322745528 - -key: test_accuracy -value: [0.92105263 0.78947368 0.86842105 0.83783784 0.86486486 0.89189189 - 0.91891892 0.91891892 0.91891892 0.89189189] - -mean value: 0.8822190611664297 - -key: train_accuracy -value: [0.99104478 1. 0.99402985 0.99404762 0.99702381 0.99107143 - 0.98214286 0.99107143 0.99404762 0.98809524] - -mean value: 0.9922574626865671 - -key: test_roc_auc -value: [0.92105263 0.78947368 0.86842105 0.83918129 0.86695906 0.89181287 - 0.91959064 0.91812865 0.91959064 0.89181287] - -mean value: 0.8826023391812866 - -key: train_roc_auc -value: [0.99099822 1. 0.99402852 0.99404572 0.99705882 0.99103473 - 0.98199858 0.99103473 0.99404678 0.98802268] - -mean value: 0.9922268772999872 - -key: test_jcc -value: [0.85714286 0.65217391 0.75 0.71428571 0.77272727 0.8 - 0.85 0.84210526 0.85 0.8 ] - -mean value: 0.7888435020357216 - -key: train_jcc -value: [0.98192771 1. 0.98795181 0.98795181 0.99401198 0.98203593 - 0.96407186 0.98203593 0.98802395 0.9760479 ] - -mean value: 0.9844058870211384 - -key: TN -value: 165 - -mean value: 165.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 163 - -mean value: 163.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01403332 0.01323676 0.01034784 0.01055479 0.00971746 0.01200938 - 0.01085329 0.01131749 0.01292825 0.01131463] - -mean value: 0.011631321907043458 - -key: score_time -value: [0.01309228 0.0101006 0.00962377 0.00998664 0.01304913 0.01012397 - 0.0097754 0.01058364 0.01499438 0.01035762] - -mean value: 0.011168742179870605 - -key: test_mcc -value: [0.68803296 0.21081851 0.45291081 0.150005 0.1875299 0.42489158 - 0.62280702 0.45906433 0.29824561 0.35104619] - -mean value: 0.384535191836478 - -key: train_mcc -value: [0.43308991 0.46903172 0.45665575 0.4796308 0.45826772 0.43909081 - 0.45606524 0.42980029 0.4763602 0.46173516] - -mean value: 0.4559727602808903 - -key: test_fscore -value: [0.83333333 0.59459459 0.64516129 0.5 0.54545455 0.73170732 - 0.81081081 0.72222222 0.64864865 0.64705882] - -mean value: 0.6678991585989318 - -key: train_fscore -value: [0.70219436 0.72100313 0.71826625 0.7124183 0.72171254 0.68852459 - 0.7012987 0.69620253 0.72839506 0.70550162] - -mean value: 0.709551708787245 - -key: test_precision -value: [0.88235294 0.61111111 0.83333333 0.61538462 0.6 0.65217391 - 0.78947368 0.72222222 0.63157895 0.6875 ] - -mean value: 0.7025130767850178 - -key: train_precision -value: [0.72727273 0.74675325 0.73417722 0.77304965 0.73291925 0.75539568 - 0.76056338 0.73333333 0.74683544 0.76223776] - -mean value: 0.7472537691608301 - -key: test_recall -value: [0.78947368 0.57894737 0.52631579 0.42105263 0.5 0.83333333 - 0.83333333 0.72222222 0.66666667 0.61111111] - -mean value: 0.6482456140350878 - -key: train_recall -value: [0.67878788 0.6969697 0.7030303 0.66060606 0.71084337 0.63253012 - 0.65060241 0.6626506 0.71084337 0.65662651] - -mean value: 0.6763490324936108 - -key: test_accuracy -value: [0.84210526 0.60526316 0.71052632 0.56756757 0.59459459 0.7027027 - 0.81081081 0.72972973 0.64864865 0.67567568] - -mean value: 0.6887624466571834 - -key: train_accuracy -value: [0.71641791 0.73432836 0.72835821 0.73809524 0.72916667 0.7172619 - 0.72619048 0.71428571 0.73809524 0.72916667] - -mean value: 0.7271366382373845 - -key: test_roc_auc -value: [0.84210526 0.60526316 0.71052632 0.57163743 0.59210526 0.70614035 - 0.81140351 0.72953216 0.64912281 0.67397661] - -mean value: 0.6891812865497077 - -key: train_roc_auc -value: [0.71586453 0.73377897 0.72798574 0.73673578 0.7289511 0.71626506 - 0.7253012 0.71367824 0.73777463 0.72831325] - -mean value: 0.7264648499241361 - -key: test_jcc -value: [0.71428571 0.42307692 0.47619048 0.33333333 0.375 0.57692308 - 0.68181818 0.56521739 0.48 0.47826087] - -mean value: 0.5104105966497271 - -key: train_jcc -value: [0.5410628 0.56372549 0.56038647 0.55329949 0.5645933 0.525 - 0.54 0.53398058 0.57281553 0.545 ] - -mean value: 0.5499863675884444 - -key: TN -value: 138 - -mean value: 138.0 - -key: FP -value: 65 - -mean value: 65.0 - -key: FN -value: 51 - -mean value: 51.0 - -key: TP -value: 119 - -mean value: 119.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.39 - -Accuracy on Blind test: 0.7 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01336336 0.01327896 0.01127768 0.01055002 0.00948644 0.01518345 - 0.01483941 0.01207328 0.01498985 0.01172137] - -mean value: 0.012676382064819336 - -key: score_time -value: [0.00970793 0.01412702 0.01071215 0.00895739 0.00943995 0.01038647 - 0.01121449 0.01185536 0.0123713 0.0102222 ] - -mean value: 0.01089942455291748 - -key: test_mcc -value: [ 0. 0.05292561 0.31622777 0.03274332 -0.02631579 0.19469789 - 0.18768409 0.13450292 0.02631579 0.19005848] - -mean value: 0.11088400791104162 - -key: train_mcc -value: [0.30884068 0.3011984 0.26223693 0.32763695 0.31695823 0.27502525 - 0.34542877 0.26171405 0.25697733 0.28573627] - -mean value: 0.29417528655707104 - -key: test_fscore -value: [0.51282051 0.55 0.64864865 0.47058824 0.48648649 0.61538462 - 0.57142857 0.55555556 0.5 0.59459459] - -mean value: 0.5505507220213103 - -key: train_fscore -value: [0.66081871 0.64220183 0.64367816 0.63897764 0.66666667 0.64534884 - 0.67261905 0.62424242 0.63556851 0.625 ] - -mean value: 0.645512183387194 - -key: test_precision -value: [0.5 0.52380952 0.66666667 0.53333333 0.47368421 0.57142857 - 0.58823529 0.55555556 0.5 0.57894737] - -mean value: 0.5491660523858666 - -key: train_precision -value: [0.63841808 0.64814815 0.61202186 0.67567568 0.6424581 0.62359551 - 0.66470588 0.62804878 0.61581921 0.64935065] - -mean value: 0.6398241888250947 - -key: test_recall -value: [0.52631579 0.57894737 0.63157895 0.42105263 0.5 0.66666667 - 0.55555556 0.55555556 0.5 0.61111111] - -mean value: 0.5546783625730993 - -key: train_recall -value: [0.68484848 0.63636364 0.67878788 0.60606061 0.69277108 0.6686747 - 0.68072289 0.62048193 0.65662651 0.60240964] - -mean value: 0.6527747353048559 - -key: test_accuracy -value: [0.5 0.52631579 0.65789474 0.51351351 0.48648649 0.59459459 - 0.59459459 0.56756757 0.51351351 0.59459459] - -mean value: 0.5549075391180655 - -key: train_accuracy -value: [0.65373134 0.65074627 0.62985075 0.66369048 0.6577381 0.63690476 - 0.67261905 0.63095238 0.62797619 0.64285714] - -mean value: 0.646706645344705 - -key: test_roc_auc -value: [0.5 0.52631579 0.65789474 0.51608187 0.48684211 0.59649123 - 0.59356725 0.56725146 0.51315789 0.59502924] - -mean value: 0.5552631578947369 - -key: train_roc_auc -value: [0.65418895 0.65053476 0.63057041 0.66267943 0.65815025 0.63727853 - 0.67271439 0.6308292 0.62831325 0.64238129] - -mean value: 0.6467640446390119 - -key: test_jcc -value: [0.34482759 0.37931034 0.48 0.30769231 0.32142857 0.44444444 - 0.4 0.38461538 0.33333333 0.42307692] - -mean value: 0.3818728895625448 - -key: train_jcc -value: [0.49344978 0.47297297 0.47457627 0.46948357 0.5 0.47639485 - 0.50672646 0.45374449 0.46581197 0.45454545] - -mean value: 0.4767705814827921 - -key: TN -value: 105 - -mean value: 105.0 - -key: FP -value: 82 - -mean value: 82.0 - -key: FN -value: 84 - -mean value: 84.0 - -key: TP -value: 102 - -mean value: 102.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.15 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01180053 0.01188016 0.01144528 0.01039028 0.0091579 0.01060843 - 0.010885 0.01373529 0.01067901 0.01151967] - -mean value: 0.011210155487060548 - -key: score_time -value: [0.06475735 0.01963401 0.03865957 0.01739407 0.01714516 0.01823735 - 0.01858783 0.02996397 0.01877666 0.01837492] - -mean value: 0.026153087615966797 - -key: test_mcc -value: [ 0.26315789 0.15877684 0.42640143 0.19469789 0.40469382 -0.19469789 - 0.13450292 0.1875299 0.19469789 0.35558302] - -mean value: 0.2125343725839552 - -key: train_mcc -value: [0.48041947 0.48645276 0.44470983 0.50663502 0.42248182 0.52427874 - 0.46418955 0.49416225 0.47012534 0.47610276] - -mean value: 0.4769557541314259 - -key: test_fscore -value: [0.63157895 0.55555556 0.68571429 0.57142857 0.68571429 0.35294118 - 0.55555556 0.54545455 0.61538462 0.625 ] - -mean value: 0.5824327538646423 - -key: train_fscore -value: [0.73394495 0.73939394 0.71903323 0.73817035 0.70516717 0.76331361 - 0.72560976 0.74626866 0.72948328 0.73170732] - -mean value: 0.733209226843559 - -key: test_precision -value: [0.63157895 0.58823529 0.75 0.625 0.70588235 0.375 - 0.55555556 0.6 0.57142857 0.71428571] - -mean value: 0.6116966435697087 - -key: train_precision -value: [0.74074074 0.73939394 0.71686747 0.76973684 0.71165644 0.75 - 0.7345679 0.73964497 0.73619632 0.74074074] - -mean value: 0.7379545365245168 - -key: test_recall -value: [0.63157895 0.52631579 0.63157895 0.52631579 0.66666667 0.33333333 - 0.55555556 0.5 0.66666667 0.55555556] - -mean value: 0.5593567251461988 - -key: train_recall -value: [0.72727273 0.73939394 0.72121212 0.70909091 0.69879518 0.77710843 - 0.71686747 0.75301205 0.72289157 0.72289157] - -mean value: 0.7288535962029938 - -key: test_accuracy -value: [0.63157895 0.57894737 0.71052632 0.59459459 0.7027027 0.40540541 - 0.56756757 0.59459459 0.59459459 0.67567568] - -mean value: 0.6056187766714082 - -key: train_accuracy -value: [0.74029851 0.74328358 0.72238806 0.75297619 0.71130952 0.76190476 - 0.73214286 0.74702381 0.73511905 0.73809524] - -mean value: 0.738454157782516 - -key: test_roc_auc -value: [0.63157895 0.57894737 0.71052632 0.59649123 0.70175439 0.40350877 - 0.56725146 0.59210526 0.59649123 0.67251462] - -mean value: 0.6051169590643275 - -key: train_roc_auc -value: [0.74010695 0.74322638 0.72237077 0.75220627 0.7111623 0.76208363 - 0.73196315 0.74709426 0.73497519 0.73791637] - -mean value: 0.7383105270316654 - -key: test_jcc -value: [0.46153846 0.38461538 0.52173913 0.4 0.52173913 0.21428571 - 0.38461538 0.375 0.44444444 0.45454545] - -mean value: 0.4162523104914409 - -key: train_jcc -value: [0.57971014 0.58653846 0.56132075 0.585 0.54460094 0.61722488 - 0.56937799 0.5952381 0.57416268 0.57692308] - -mean value: 0.5790097022550522 - -key: TN -value: 123 - -mean value: 123.0 - -key: FP -value: 81 - -mean value: 81.0 - -key: FN -value: 66 - -mean value: 66.0 - -key: TP -value: 103 - -mean value: 103.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.63 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02089095 0.01982832 0.02817822 0.0224781 0.02628708 0.01691031 - 0.01959848 0.02675772 0.01683712 0.02304029] - -mean value: 0.022080659866333008 - -key: score_time -value: [0.01413178 0.01324248 0.01694727 0.01519847 0.01492977 0.0114255 - 0.01163435 0.01256704 0.01113868 0.0187223 ] - -mean value: 0.01399376392364502 - -key: test_mcc -value: [0.57894737 0.47633051 0.48454371 0.36315314 0.40643275 0.37654316 - 0.51319869 0.51319869 0.29618896 0.63129316] - -mean value: 0.4639830131522736 - -key: train_mcc -value: [0.71979515 0.70241539 0.7139808 0.72619277 0.679115 0.72682201 - 0.74453168 0.75067552 0.72628008 0.69043739] - -mean value: 0.7180245793621042 - -key: test_fscore -value: [0.78947368 0.72222222 0.70588235 0.64705882 0.7027027 0.71428571 - 0.74285714 0.74285714 0.62857143 0.82051282] - -mean value: 0.7216424034690289 - -key: train_fscore -value: [0.85448916 0.84375 0.85093168 0.85889571 0.83333333 0.85802469 - 0.86769231 0.87037037 0.8597561 0.84242424] - -mean value: 0.8539667589366047 - -key: test_precision -value: [0.78947368 0.76470588 0.8 0.73333333 0.68421053 0.625 - 0.76470588 0.76470588 0.64705882 0.76190476] - -mean value: 0.7335098776352647 - -key: train_precision -value: [0.87341772 0.87096774 0.87261146 0.86956522 0.85443038 0.87974684 - 0.88679245 0.89240506 0.87037037 0.84756098] - -mean value: 0.8717868223105256 - -key: test_recall -value: [0.78947368 0.68421053 0.63157895 0.57894737 0.72222222 0.83333333 - 0.72222222 0.72222222 0.61111111 0.88888889] - -mean value: 0.718421052631579 - -key: train_recall -value: [0.83636364 0.81818182 0.83030303 0.84848485 0.81325301 0.8373494 - 0.84939759 0.84939759 0.84939759 0.8373494 ] - -mean value: 0.8369477911646586 - -key: test_accuracy -value: [0.78947368 0.73684211 0.73684211 0.67567568 0.7027027 0.67567568 - 0.75675676 0.75675676 0.64864865 0.81081081] - -mean value: 0.7290184921763869 - -key: train_accuracy -value: [0.85970149 0.85074627 0.85671642 0.86309524 0.83928571 0.86309524 - 0.87202381 0.875 0.86309524 0.8452381 ] - -mean value: 0.858799751243781 - -key: test_roc_auc -value: [0.78947368 0.73684211 0.73684211 0.67836257 0.70321637 0.67982456 - 0.75584795 0.75584795 0.64766082 0.8128655 ] - -mean value: 0.7296783625730994 - -key: train_roc_auc -value: [0.85935829 0.85026738 0.85632799 0.86283892 0.83897945 0.86279235 - 0.87175762 0.8746988 0.86293409 0.84514529] - -mean value: 0.8585100152933716 - -key: test_jcc -value: [0.65217391 0.56521739 0.54545455 0.47826087 0.54166667 0.55555556 - 0.59090909 0.59090909 0.45833333 0.69565217] - -mean value: 0.5674132630654369 - -key: train_jcc -value: [0.74594595 0.72972973 0.74054054 0.75268817 0.71428571 0.75135135 - 0.76630435 0.7704918 0.7540107 0.72774869] - -mean value: 0.745309699128771 - -key: TN -value: 140 - -mean value: 140.0 - -key: FP -value: 52 - -mean value: 52.0 - -key: FN -value: 49 - -mean value: 49.0 - -key: TP -value: 132 - -mean value: 132.0 - -key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.49 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.65317106 1.96399856 2.3143084 1.97237158 2.46304488 2.20589232 - 1.71940422 1.78963614 1.40883422 1.66870093] - -mean value: 1.9159362316131592 - -key: score_time -value: [0.01287794 0.01687479 0.01413894 0.01370263 0.01253629 0.01357865 - 0.02309346 0.01287794 0.01305556 0.01330233] - -mean value: 0.014603853225708008 - -key: test_mcc -value: [0.80757285 0.58218174 0.69989647 0.64788432 0.68035483 0.62280702 - 0.63129316 0.83918129 0.73821295 0.89181287] - -mean value: 0.7141197490022652 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.9047619 0.8 0.82352941 0.78787879 0.84210526 0.81081081 - 0.82051282 0.91891892 0.84848485 0.94444444] - -mean value: 0.8501447210735137 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.82608696 0.76190476 0.93333333 0.92857143 0.8 0.78947368 - 0.76190476 0.89473684 0.93333333 0.94444444] - -mean value: 0.8573789546329593 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.84210526 0.73684211 0.68421053 0.88888889 0.83333333 - 0.88888889 0.94444444 0.77777778 0.94444444] - -mean value: 0.8540935672514619 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.89473684 0.78947368 0.84210526 0.81081081 0.83783784 0.81081081 - 0.81081081 0.91891892 0.86486486 0.94594595] - -mean value: 0.8526315789473685 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.89473684 0.78947368 0.84210526 0.81432749 0.83918129 0.81140351 - 0.8128655 0.91959064 0.8625731 0.94590643] - -mean value: 0.8532163742690058 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.82608696 0.66666667 0.7 0.65 0.72727273 0.68181818 - 0.69565217 0.85 0.73684211 0.89473684] - -mean value: 0.7429075653560779 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 161 - -mean value: 161.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 28 - -mean value: 28.0 - -key: TP -value: 157 - -mean value: 157.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.63 - -Accuracy on Blind test: 0.82 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02132082 0.01724267 0.01608562 0.0149529 0.01561928 0.01630712 - 0.01538372 0.0156467 0.01646924 0.0157156 ] - -mean value: 0.016474366188049316 - -key: score_time -value: [0.0120573 0.00919485 0.00906229 0.00878835 0.00877357 0.00880909 - 0.00861526 0.00866675 0.00872469 0.00891066] - -mean value: 0.009160280227661133 - -key: test_mcc -value: [0.9486833 0.74620251 0.89973541 0.83918129 0.89736456 0.89181287 - 0.94736842 0.89181287 1. 0.73099415] - -mean value: 0.879315536920504 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97435897 0.85714286 0.94444444 0.91891892 0.94736842 0.94444444 - 0.97297297 0.94444444 1. 0.86486486] - -mean value: 0.9368960342644554 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95 0.9375 1. 0.94444444 0.9 0.94444444 - 0.94736842 0.94444444 1. 0.84210526] - -mean value: 0.9410307017543861 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.78947368 0.89473684 0.89473684 1. 0.94444444 - 1. 0.94444444 1. 0.88888889] - -mean value: 0.935672514619883 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97368421 0.86842105 0.94736842 0.91891892 0.94594595 0.94594595 - 0.97297297 0.94594595 1. 0.86486486] - -mean value: 0.938406827880512 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.86842105 0.94736842 0.91959064 0.94736842 0.94590643 - 0.97368421 0.94590643 1. 0.86549708] - -mean value: 0.9387426900584795 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95 0.75 0.89473684 0.85 0.9 0.89473684 - 0.94736842 0.89473684 1. 0.76190476] - -mean value: 0.8843483709273183 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 178 - -mean value: 178.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 172 - -mean value: 172.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.88 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.12278724 0.12156177 0.12039208 0.11667752 0.12120867 0.11756587 - 0.11896324 0.11722589 0.11644959 0.11813235] - -mean value: 0.11909642219543456 - -key: score_time -value: [0.01819134 0.01828933 0.01932883 0.01834726 0.01923108 0.01931071 - 0.02008462 0.01789856 0.01847148 0.01877713] - -mean value: 0.01879303455352783 - -key: test_mcc -value: [0.79388419 0.47368421 0.58218174 0.41299552 0.51461988 0.24408665 - 0.57184997 0.78362573 0.56725146 0.83871328] - -mean value: 0.578289263399727 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.9 0.73684211 0.77777778 0.68571429 0.75675676 0.5625 - 0.78947368 0.88888889 0.77777778 0.91428571] - -mean value: 0.7790016990674886 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85714286 0.73684211 0.82352941 0.75 0.73684211 0.64285714 - 0.75 0.88888889 0.77777778 0.94117647] - -mean value: 0.7905056759545924 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94736842 0.73684211 0.73684211 0.63157895 0.77777778 0.5 - 0.83333333 0.88888889 0.77777778 0.88888889] - -mean value: 0.7719298245614035 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.89473684 0.73684211 0.78947368 0.7027027 0.75675676 0.62162162 - 0.78378378 0.89189189 0.78378378 0.91891892] - -mean value: 0.7880512091038409 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.89473684 0.73684211 0.78947368 0.70467836 0.75730994 0.61842105 - 0.78508772 0.89181287 0.78362573 0.91812865] - -mean value: 0.7880116959064327 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.81818182 0.58333333 0.63636364 0.52173913 0.60869565 0.39130435 - 0.65217391 0.8 0.63636364 0.84210526] - -mean value: 0.649026073087858 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 152 - -mean value: 152.0 - -key: FP -value: 42 - -mean value: 42.0 - -key: FN -value: 37 - -mean value: 37.0 - -key: TP -value: 142 - -mean value: 142.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.55 - -Accuracy on Blind test: 0.78 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.00938344 0.00939775 0.00944567 0.00944495 0.00960183 0.00928593 - 0.00938463 0.0093329 0.00932264 0.00943232] - -mean value: 0.009403204917907715 - -key: score_time -value: [0.00865936 0.0086596 0.00866866 0.00867343 0.0086894 0.00845981 - 0.00859141 0.00854635 0.00868869 0.00865436] - -mean value: 0.008629107475280761 - -key: test_mcc -value: [0.15877684 0.26315789 0.26919095 0.30384671 0.24633537 0.24189738 - 0.41299552 0.18768409 0.46019501 0.02631579] - -mean value: 0.25703955528438305 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.6 0.63157895 0.58823529 0.62857143 0.63157895 0.58823529 - 0.71794872 0.57142857 0.70588235 0.5 ] - -mean value: 0.6163459553862031 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.57142857 0.63157895 0.66666667 0.6875 0.6 0.625 - 0.66666667 0.58823529 0.75 0.5 ] - -mean value: 0.6287076146247973 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.63157895 0.63157895 0.52631579 0.57894737 0.66666667 0.55555556 - 0.77777778 0.55555556 0.66666667 0.5 ] - -mean value: 0.6090643274853801 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.57894737 0.63157895 0.63157895 0.64864865 0.62162162 0.62162162 - 0.7027027 0.59459459 0.72972973 0.51351351] - -mean value: 0.6274537695590326 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.57894737 0.63157895 0.63157895 0.6505848 0.62280702 0.61988304 - 0.70467836 0.59356725 0.72807018 0.51315789] - -mean value: 0.6274853801169591 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.42857143 0.46153846 0.41666667 0.45833333 0.46153846 0.41666667 - 0.56 0.4 0.54545455 0.33333333] - -mean value: 0.4482102897102897 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 122 - -mean value: 122.0 - -key: FP -value: 72 - -mean value: 72.0 - -key: FN -value: 67 - -mean value: 67.0 - -key: TP -value: 112 - -mean value: 112.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.6 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -[1.55127692 1.51234651 1.5269444 1.53226781 1.5758934 1.54383922 - 1.56436038 1.54673672 1.53705311 1.55287671] - -mean value: 1.544359517097473 - -key: score_time -value: [0.09099317 0.09104776 0.09433341 0.10096717 0.09178352 0.09661984 - 0.09914851 0.09179831 0.1010499 0.09051657] - -mean value: 0.09482581615447998 - -key: test_mcc -value: [1. 0.73786479 0.76376262 0.83918129 0.7888597 0.94736842 - 0.83918129 0.83918129 0.94721815 0.89679028] - -mean value: 0.8599407809004956 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.86486486 0.84848485 0.91891892 0.89473684 0.97297297 - 0.91891892 0.91891892 0.97142857 0.94117647] - -mean value: 0.9250421327201513 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88888889 1. 0.94444444 0.85 0.94736842 - 0.89473684 0.89473684 1. 1. ] - -mean value: 0.9420175438596491 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.84210526 0.73684211 0.89473684 0.94444444 1. - 0.94444444 0.94444444 0.94444444 0.88888889] - -mean value: 0.9140350877192983 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.86842105 0.86842105 0.91891892 0.89189189 0.97297297 - 0.91891892 0.91891892 0.97297297 0.94594595] - -mean value: 0.9277382645803698 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.86842105 0.86842105 0.91959064 0.89327485 0.97368421 - 0.91959064 0.91959064 0.97222222 0.94444444] - -mean value: 0.927923976608187 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.76190476 0.73684211 0.85 0.80952381 0.94736842 - 0.85 0.85 0.94444444 0.88888889] - -mean value: 0.8638972431077695 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 178 - -mean value: 178.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 168 - -mean value: 168.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.93 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.91105819 0.89222145 0.88838792 0.92443156 0.926579 0.90934181 - 0.92040396 0.90189528 0.91631579 0.94257879] - -mean value: 0.9133213758468628 - -key: score_time -value: [0.19877291 0.22406077 0.27467585 0.20796537 0.20172167 0.20823526 - 0.20790195 0.208673 0.21042919 0.23190784] - -mean value: 0.21743438243865967 - -key: test_mcc -value: [1. 0.68803296 0.76376262 0.7888597 0.7888597 0.7888597 - 0.83918129 0.78362573 0.94721815 0.94721815] - -mean value: 0.8335617984152386 - -key: train_mcc -value: [0.97015724 0.97016256 0.96423353 0.97024264 0.97024896 0.96434396 - 0.96434396 0.97625295 0.9584715 0.95834146] - -mean value: 0.9666798766808119 - -key: test_fscore -value: [1. 0.83333333 0.84848485 0.88888889 0.89473684 0.89473684 - 0.91891892 0.88888889 0.97142857 0.97142857] - -mean value: 0.9110845705582549 - -key: train_fscore -value: [0.98480243 0.98489426 0.98170732 0.98480243 0.98489426 0.98181818 - 0.98181818 0.98787879 0.9787234 0.97885196] - -mean value: 0.9830191219449211 - -key: test_precision -value: [1. 0.88235294 1. 0.94117647 0.85 0.85 - 0.89473684 0.88888889 1. 1. ] - -mean value: 0.9307155142758858 - -key: train_precision -value: [0.98780488 0.98192771 0.98773006 0.98780488 0.98787879 0.98780488 - 0.98780488 0.99390244 0.98773006 0.98181818] - -mean value: 0.9872206754459242 - -key: test_recall -value: [1. 0.78947368 0.73684211 0.84210526 0.94444444 0.94444444 - 0.94444444 0.88888889 0.94444444 0.94444444] - -mean value: 0.897953216374269 - -key: train_recall -value: [0.98181818 0.98787879 0.97575758 0.98181818 0.98192771 0.97590361 - 0.97590361 0.98192771 0.96987952 0.97590361] - -mean value: 0.9788718510405257 - -key: test_accuracy -value: [1. 0.84210526 0.86842105 0.89189189 0.89189189 0.89189189 - 0.91891892 0.89189189 0.97297297 0.97297297] - -mean value: 0.9142958748221908 - -key: train_accuracy -value: [0.98507463 0.98507463 0.98208955 0.98511905 0.98511905 0.98214286 - 0.98214286 0.98809524 0.97916667 0.97916667] - -mean value: 0.983319118692253 - -key: test_roc_auc -value: [1. 0.84210526 0.86842105 0.89327485 0.89327485 0.89327485 - 0.91959064 0.89181287 0.97222222 0.97222222] - -mean value: 0.9146198830409356 - -key: train_roc_auc -value: [0.98502674 0.98511586 0.98199643 0.98506114 0.9850815 0.98206945 - 0.98206945 0.98802268 0.97905741 0.97912828] - -mean value: 0.9832628949045079 - -key: test_jcc -value: [1. 0.71428571 0.73684211 0.8 0.80952381 0.80952381 - 0.85 0.8 0.94444444 0.94444444] - -mean value: 0.840906432748538 - -key: train_jcc -value: [0.97005988 0.9702381 0.96407186 0.97005988 0.9702381 0.96428571 - 0.96428571 0.9760479 0.95833333 0.95857988] - -mean value: 0.9666200354995841 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 165 - -mean value: 165.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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/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 -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.11715555 0.22330427 0.05919528 0.06030846 0.06126213 0.06207466 - 0.06642628 0.06068993 0.06308246 0.06095958] - -mean value: 0.0834458589553833 - -key: score_time -value: [0.01131964 0.01097918 0.01060987 0.01057243 0.01069355 0.01060462 - 0.01061273 0.01083136 0.01056814 0.01056671] - -mean value: 0.010735821723937989 - -key: test_mcc -value: [0.9486833 0.89473684 0.89973541 0.83918129 0.94736842 0.94736842 - 0.94736842 0.89181287 1. 0.94736842] - -mean value: 0.9263623387255524 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97435897 0.94736842 0.94444444 0.91891892 0.97297297 0.97297297 - 0.97297297 0.94444444 1. 0.97297297] - -mean value: 0.9621427095111306 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95 0.94736842 1. 0.94444444 0.94736842 0.94736842 - 0.94736842 0.94444444 1. 0.94736842] - -mean value: 0.9575730994152047 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94736842 0.89473684 0.89473684 1. 1. - 1. 0.94444444 1. 1. ] - -mean value: 0.9681286549707602 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97368421 0.94736842 0.94736842 0.91891892 0.97297297 0.97297297 - 0.97297297 0.94594595 1. 0.97297297] - -mean value: 0.9625177809388337 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.94736842 0.94736842 0.91959064 0.97368421 0.97368421 - 0.97368421 0.94590643 1. 0.97368421] - -mean value: 0.9628654970760234 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95 0.9 0.89473684 0.85 0.94736842 0.94736842 - 0.94736842 0.89473684 1. 0.94736842] - -mean value: 0.9278947368421052 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 178 - -mean value: 178.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03129601 0.03530335 0.03923559 0.04932284 0.05312657 0.07098794 - 0.05933022 0.03388429 0.03405523 0.04206657] - -mean value: 0.04486086368560791 - -key: score_time -value: [0.01221609 0.01213336 0.01223111 0.02007055 0.01227474 0.02222705 - 0.01222205 0.01215982 0.01218152 0.0318377 ] - -mean value: 0.015955400466918946 - -key: test_mcc -value: [0.84327404 0.52704628 0.65465367 0.68035483 0.73099415 0.73099415 - 0.84834956 0.84834956 0.84834956 0.7888597 ] - -mean value: 0.7501225491822617 - -key: train_mcc -value: [0.93470153 0.94626877 0.9644337 0.95261595 0.95296258 0.93464974 - 0.94071778 0.97625295 0.95243498 0.94071778] - -mean value: 0.9495755766835041 - -key: test_fscore -value: [0.92307692 0.76923077 0.78787879 0.83333333 0.86486486 0.86486486 - 0.90909091 0.90909091 0.90909091 0.89473684] - -mean value: 0.8665259112627535 - -key: train_fscore -value: [0.96615385 0.97264438 0.98159509 0.97546012 0.97546012 0.96656535 - 0.9695122 0.98787879 0.97575758 0.9695122 ] - -mean value: 0.9740539663901193 - -key: test_precision -value: [0.9 0.75 0.92857143 0.88235294 0.84210526 0.84210526 - 1. 1. 1. 0.85 ] - -mean value: 0.8995134896063688 - -key: train_precision -value: [0.98125 0.97560976 0.99378882 0.98757764 0.99375 0.97546012 - 0.98148148 0.99390244 0.98170732 0.98148148] - -mean value: 0.9846009057484799 - -key: test_recall -value: [0.94736842 0.78947368 0.68421053 0.78947368 0.88888889 0.88888889 - 0.83333333 0.83333333 0.83333333 0.94444444] - -mean value: 0.8432748538011696 - -key: train_recall -value: [0.95151515 0.96969697 0.96969697 0.96363636 0.95783133 0.95783133 - 0.95783133 0.98192771 0.96987952 0.95783133] - -mean value: 0.9637677984665937 - -key: test_accuracy -value: [0.92105263 0.76315789 0.81578947 0.83783784 0.86486486 0.86486486 - 0.91891892 0.91891892 0.91891892 0.89189189] - -mean value: 0.8716216216216216 - -key: train_accuracy -value: [0.96716418 0.97313433 0.98208955 0.97619048 0.97619048 0.9672619 - 0.9702381 0.98809524 0.97619048 0.9702381 ] - -mean value: 0.9746792821606254 - -key: test_roc_auc -value: [0.92105263 0.76315789 0.81578947 0.83918129 0.86549708 0.86549708 - 0.91666667 0.91666667 0.91666667 0.89327485] - -mean value: 0.8713450292397662 - -key: train_roc_auc -value: [0.96693405 0.97308378 0.98190731 0.97597023 0.97597449 0.96715096 - 0.97009213 0.98802268 0.97611623 0.97009213] - -mean value: 0.9745343980293064 - -key: test_jcc -value: [0.85714286 0.625 0.65 0.71428571 0.76190476 0.76190476 - 0.83333333 0.83333333 0.83333333 0.80952381] - -mean value: 0.7679761904761905 - -key: train_jcc -value: [0.93452381 0.94674556 0.96385542 0.95209581 0.95209581 0.93529412 - 0.9408284 0.9760479 0.95266272 0.9408284 ] - -mean value: 0.9494977958573095 - -key: TN -value: 170 - -mean value: 170.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 155 - -mean value: 155.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.58 - -Accuracy on Blind test: 0.79 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.0196867 0.00965071 0.00936127 0.0091126 0.00908041 0.00904393 - 0.00924277 0.0090847 0.00900722 0.00930977] - -mean value: 0.010258007049560546 - -key: score_time -value: [0.01141858 0.0090394 0.00876427 0.00849509 0.00861621 0.00850248 - 0.00855446 0.00850534 0.00854087 0.00854468] - -mean value: 0.008898138999938965 - -key: test_mcc -value: [0.26462806 0.10540926 0.2773501 0.150005 0.07917923 0.37654316 - 0.41299552 0.24633537 0.30384671 0.29618896] - -mean value: 0.25124813548207314 - -key: train_mcc -value: [0.28357039 0.29551207 0.28375873 0.30930356 0.28634241 0.28549147 - 0.30931788 0.31543327 0.27399008 0.27357826] - -mean value: 0.29162981145990385 - -key: test_fscore -value: [0.61111111 0.56410256 0.5625 0.5 0.51428571 0.71428571 - 0.71794872 0.63157895 0.66666667 0.62857143] - -mean value: 0.6111050864340338 - -key: train_fscore -value: [0.63855422 0.64457831 0.64071856 0.64848485 0.64705882 0.63190184 - 0.64634146 0.65465465 0.63690476 0.62804878] - -mean value: 0.6417246265961647 - -key: test_precision -value: [0.64705882 0.55 0.69230769 0.61538462 0.52941176 0.625 - 0.66666667 0.6 0.61904762 0.64705882] - -mean value: 0.6191936005171299 - -key: train_precision -value: [0.63473054 0.64071856 0.63313609 0.64848485 0.63218391 0.64375 - 0.65432099 0.65269461 0.62941176 0.63580247] - -mean value: 0.6405233785276238 - -key: test_recall -value: [0.57894737 0.57894737 0.47368421 0.42105263 0.5 0.83333333 - 0.77777778 0.66666667 0.72222222 0.61111111] - -mean value: 0.6163742690058479 - -key: train_recall -value: [0.64242424 0.64848485 0.64848485 0.64848485 0.6626506 0.62048193 - 0.63855422 0.65662651 0.64457831 0.62048193] - -mean value: 0.6431252281854691 - -key: test_accuracy -value: [0.63157895 0.55263158 0.63157895 0.56756757 0.54054054 0.67567568 - 0.7027027 0.62162162 0.64864865 0.64864865] - -mean value: 0.6221194879089615 - -key: train_accuracy -value: [0.64179104 0.64776119 0.64179104 0.6547619 0.64285714 0.64285714 - 0.6547619 0.6577381 0.63690476 0.63690476] - -mean value: 0.6458128997867804 - -key: test_roc_auc -value: [0.63157895 0.55263158 0.63157895 0.57163743 0.53947368 0.67982456 - 0.70467836 0.62280702 0.6505848 0.64766082] - -mean value: 0.6232456140350877 - -key: train_roc_auc -value: [0.64180036 0.64777184 0.64188948 0.65465178 0.64309001 0.64259391 - 0.65457123 0.65772502 0.63699504 0.63671155] - -mean value: 0.6457800203534845 - -key: test_jcc -value: [0.44 0.39285714 0.39130435 0.33333333 0.34615385 0.55555556 - 0.56 0.46153846 0.5 0.45833333] - -mean value: 0.443907602059776 - -key: train_jcc -value: [0.46902655 0.47555556 0.47136564 0.47982063 0.47826087 0.46188341 - 0.47747748 0.48660714 0.46724891 0.45777778] - -mean value: 0.472502395484364 - -key: TN -value: 119 - -mean value: 119.0 - -key: FP -value: 71 - -mean value: 71.0 - -key: FN -value: 70 - -mean value: 70.0 - -key: TP -value: 113 - -mean value: 113.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.24 - -Accuracy on Blind test: 0.61 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01772475 0.02563572 0.0224402 0.01842952 0.018013 0.02219248 - 0.02028799 0.02085257 0.02107739 0.02067995] - -mean value: 0.020733356475830078 - -key: score_time -value: [0.00943851 0.01123762 0.01191664 0.01214385 0.01184177 0.01187062 - 0.01194668 0.01191354 0.01190233 0.01195765] - -mean value: 0.011616921424865723 - -key: test_mcc -value: [0.84327404 0.79388419 0.85280287 0.51319869 0.68035483 0.78362573 - 0.74044197 0.83871328 0.89181287 0.78764146] - -mean value: 0.7725749929465128 - -key: train_mcc -value: [0.94085799 0.96423353 0.92429469 0.85579123 0.89881828 0.92453529 - 0.93453573 0.87639734 0.93016273 0.85374151] - -mean value: 0.9103368313098772 - -key: test_fscore -value: [0.92307692 0.88888889 0.91428571 0.76923077 0.84210526 0.88888889 - 0.87179487 0.91428571 0.94444444 0.88235294] - -mean value: 0.8839354419230581 - -key: train_fscore -value: [0.9691358 0.98170732 0.95924765 0.92795389 0.94894895 0.95950156 - 0.96696697 0.92903226 0.96273292 0.91558442] - -mean value: 0.9520811725386947 - -key: test_precision -value: [0.9 0.94117647 1. 0.75 0.8 0.88888889 - 0.80952381 0.94117647 0.94444444 0.9375 ] - -mean value: 0.8912710084033615 - -key: train_precision -value: [0.98742138 0.98773006 0.99350649 0.88461538 0.94610778 0.99354839 - 0.96407186 1. 0.99358974 0.99295775] - -mean value: 0.9743548841003324 - -key: test_recall -value: [0.94736842 0.84210526 0.84210526 0.78947368 0.88888889 0.88888889 - 0.94444444 0.88888889 0.94444444 0.83333333] - -mean value: 0.8809941520467837 - -key: train_recall -value: [0.95151515 0.97575758 0.92727273 0.97575758 0.95180723 0.92771084 - 0.96987952 0.86746988 0.93373494 0.84939759] - -mean value: 0.933030303030303 - -key: test_accuracy -value: [0.92105263 0.89473684 0.92105263 0.75675676 0.83783784 0.89189189 - 0.86486486 0.91891892 0.94594595 0.89189189] - -mean value: 0.8844950213371264 - -key: train_accuracy -value: [0.97014925 0.98208955 0.96119403 0.92559524 0.94940476 0.96130952 - 0.9672619 0.93452381 0.96428571 0.92261905] - -mean value: 0.9538432835820896 - -key: test_roc_auc -value: [0.92105263 0.89473684 0.92105263 0.75584795 0.83918129 0.89181287 - 0.86695906 0.91812865 0.94590643 0.89035088] - -mean value: 0.8845029239766082 - -key: train_roc_auc -value: [0.96987522 0.98199643 0.96069519 0.92647528 0.94943303 0.96091425 - 0.9672927 0.93373494 0.96392629 0.92175762] - -mean value: 0.9536100947556057 - -key: test_jcc -value: [0.85714286 0.8 0.84210526 0.625 0.72727273 0.8 - 0.77272727 0.84210526 0.89473684 0.78947368] - -mean value: 0.7950563909774436 - -key: train_jcc -value: [0.94011976 0.96407186 0.92168675 0.8655914 0.90285714 0.92215569 - 0.93604651 0.86746988 0.92814371 0.84431138] - -mean value: 0.9092454074050117 - -key: TN -value: 168 - -mean value: 168.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 162 - -mean value: 162.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.87 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01717734 0.01819205 0.01881456 0.01838446 0.0160675 0.01694727 - 0.01886439 0.01757789 0.01953673 0.01713896] - -mean value: 0.017870116233825683 - -key: score_time -value: [0.01196671 0.01214671 0.01187873 0.01187468 0.01199245 0.01222515 - 0.01214051 0.01231623 0.01242709 0.01222563] - -mean value: 0.012119388580322266 - -key: test_mcc -value: [0.84327404 0.63245553 0.85280287 0.62280702 0.58248237 0.58342636 - 0.78764146 0.78362573 0.89736456 0.63309535] - -mean value: 0.7218975292771441 - -key: train_mcc -value: [0.94131886 0.85384939 0.92274791 0.91154317 0.60745896 0.85117392 - 0.89585866 0.90332233 0.90582911 0.58007684] - -mean value: 0.8373179151535057 - -key: test_fscore -value: [0.92307692 0.82051282 0.91428571 0.81081081 0.66666667 0.8 - 0.88235294 0.88888889 0.94736842 0.81818182] - -mean value: 0.8472145004652745 - -key: train_fscore -value: [0.9689441 0.92655367 0.96 0.95548961 0.69803922 0.9244713 - 0.94339623 0.94603175 0.95294118 0.8 ] - -mean value: 0.907586704963595 - -key: test_precision -value: [0.9 0.8 1. 0.83333333 1. 0.72727273 - 0.9375 0.88888889 0.9 0.69230769] - -mean value: 0.8679302641802641 - -key: train_precision -value: [0.99363057 0.86772487 0.975 0.93604651 1. 0.92727273 - 0.98684211 1. 0.93103448 0.67213115] - -mean value: 0.9289682415436673 - -key: test_recall -value: [0.94736842 0.84210526 0.84210526 0.78947368 0.5 0.88888889 - 0.83333333 0.88888889 1. 1. ] - -mean value: 0.8532163742690058 - -key: train_recall -value: [0.94545455 0.99393939 0.94545455 0.97575758 0.53614458 0.92168675 - 0.90361446 0.89759036 0.97590361 0.98795181] - -mean value: 0.908349762687112 - -key: test_accuracy -value: [0.92105263 0.81578947 0.92105263 0.81081081 0.75675676 0.78378378 - 0.89189189 0.89189189 0.94594595 0.78378378] - -mean value: 0.8522759601706971 - -key: train_accuracy -value: [0.97014925 0.92238806 0.96119403 0.95535714 0.77083333 0.92559524 - 0.94642857 0.94940476 0.95238095 0.75595238] - -mean value: 0.9109683724235964 - -key: test_roc_auc -value: [0.92105263 0.81578947 0.92105263 0.81140351 0.75 0.78654971 - 0.89035088 0.89181287 0.94736842 0.78947368] - -mean value: 0.852485380116959 - -key: train_roc_auc -value: [0.9697861 0.92344029 0.96096257 0.95571505 0.76807229 0.92554926 - 0.94592488 0.94879518 0.95265769 0.75868179] - -mean value: 0.9109585070745501 - -key: test_jcc -value: [0.85714286 0.69565217 0.84210526 0.68181818 0.5 0.66666667 - 0.78947368 0.8 0.9 0.69230769] - -mean value: 0.7425166519216864 - -key: train_jcc -value: [0.93975904 0.86315789 0.92307692 0.91477273 0.53614458 0.85955056 - 0.89285714 0.89759036 0.91011236 0.66666667] - -mean value: 0.8403688251862231 - -key: TN -value: 161 - -mean value: 161.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 28 - -mean value: 28.0 - -key: TP -value: 157 - -mean value: 157.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.69 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.16296864 0.14803147 0.14598346 0.14642167 0.14693999 0.14598942 - 0.14558363 0.14578176 0.14869595 0.14427948] - -mean value: 0.14806754589080812 - -key: score_time -value: [0.0151248 0.0154357 0.01523304 0.0154736 0.01536322 0.01520228 - 0.01531553 0.01593757 0.01542759 0.01519585] - -mean value: 0.015370917320251466 - -key: test_mcc -value: [0.9486833 0.89473684 0.89973541 0.83918129 0.89736456 0.89181287 - 0.94736842 0.94721815 1. 0.94736842] - -mean value: 0.9213469256799961 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97435897 0.94736842 0.94444444 0.91891892 0.94736842 0.94444444 - 0.97297297 0.97142857 1. 0.97297297] - -mean value: 0.9594278141646564 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95 0.94736842 1. 0.94444444 0.9 0.94444444 - 0.94736842 1. 1. 0.94736842] - -mean value: 0.9580994152046782 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94736842 0.89473684 0.89473684 1. 0.94444444 - 1. 0.94444444 1. 1. ] - -mean value: 0.9625730994152046 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97368421 0.94736842 0.94736842 0.91891892 0.94594595 0.94594595 - 0.97297297 0.97297297 1. 0.97297297] - -mean value: 0.959815078236131 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.94736842 0.94736842 0.91959064 0.94736842 0.94590643 - 0.97368421 0.97222222 1. 0.97368421] - -mean value: 0.9600877192982455 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95 0.9 0.89473684 0.85 0.9 0.89473684 - 0.94736842 0.94444444 1. 0.94736842] - -mean value: 0.9228654970760234 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 177 - -mean value: 177.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.96 - -Accuracy on Blind test: 0.98 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03617811 0.04358745 0.05344653 0.06370902 0.05442739 0.05186844 - 0.04430819 0.04543853 0.04257369 0.0515883 ] - -mean value: 0.04871256351470947 - -key: score_time -value: [0.02090907 0.02855325 0.02550173 0.03161693 0.02718139 0.01966619 - 0.02337337 0.01886177 0.01960397 0.0228889 ] - -mean value: 0.02381565570831299 - -key: test_mcc -value: [0.9486833 0.84327404 0.80757285 0.89736456 0.89736456 0.83918129 - 0.94736842 0.89181287 1. 0.94736842] - -mean value: 0.9019990312849734 - -key: train_mcc -value: [0.99404571 1. 0.98812541 1. 1. 0.98816193 - 0.99406397 0.98816193 0.98229327 0.98229327] - -mean value: 0.9917145485492469 - -key: test_fscore -value: [0.97435897 0.91891892 0.88235294 0.94444444 0.94736842 0.91891892 - 0.97297297 0.94444444 1. 0.97297297] - -mean value: 0.9476753009260751 - -key: train_fscore -value: [0.99696049 1. 0.99390244 1. 1. 0.99393939 - 0.99697885 0.99393939 0.99088146 0.99088146] - -mean value: 0.9957483483122245 - -key: test_precision -value: [0.95 0.94444444 1. 1. 0.9 0.89473684 - 0.94736842 0.94444444 1. 0.94736842] - -mean value: 0.9528362573099416 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.89473684 0.78947368 0.89473684 1. 0.94444444 - 1. 0.94444444 1. 1. ] - -mean value: 0.9467836257309941 - -key: train_recall -value: [0.99393939 1. 0.98787879 1. 1. 0.98795181 - 0.9939759 0.98795181 0.98192771 0.98192771] - -mean value: 0.9915553121577219 - -key: test_accuracy -value: [0.97368421 0.92105263 0.89473684 0.94594595 0.94594595 0.91891892 - 0.97297297 0.94594595 1. 0.97297297] - -mean value: 0.9492176386913229 - -key: train_accuracy -value: [0.99701493 1. 0.99402985 1. 1. 0.99404762 - 0.99702381 0.99404762 0.99107143 0.99107143] - -mean value: 0.9958306680881307 - -key: test_roc_auc -value: [0.97368421 0.92105263 0.89473684 0.94736842 0.94736842 0.91959064 - 0.97368421 0.94590643 1. 0.97368421] - -mean value: 0.9497076023391813 - -key: train_roc_auc -value: [0.9969697 1. 0.99393939 1. 1. 0.9939759 - 0.99698795 0.9939759 0.99096386 0.99096386] - -mean value: 0.9957776560788607 - -key: test_jcc -value: [0.95 0.85 0.78947368 0.89473684 0.9 0.85 - 0.94736842 0.89473684 1. 0.94736842] - -mean value: 0.9023684210526316 - -key: train_jcc -value: [0.99393939 1. 0.98787879 1. 1. 0.98795181 - 0.9939759 0.98795181 0.98192771 0.98192771] - -mean value: 0.9915553121577219 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 174 - -mean value: 174.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.04756403 0.08437514 0.08841991 0.07621384 0.10933518 0.13751149 - 0.09798574 0.09430599 0.09290719 0.10316086] - -mean value: 0.0931779384613037 - -key: score_time -value: [0.01393867 0.02669024 0.02506423 0.02192998 0.03957915 0.02184892 - 0.02248669 0.02537942 0.0275445 0.02663779] - -mean value: 0.02510995864868164 - -key: test_mcc -value: [0.47368421 0.21081851 0.22645541 0.24633537 0.35087719 0.07917923 - 0.09040246 0.29766651 0.19005848 0.24269006] - -mean value: 0.2408167429797062 - -key: train_mcc -value: [0.94053244 0.92239413 0.92239413 0.92862966 0.94656062 0.92262991 - 0.91665485 0.91670804 0.94054284 0.91689137] - -mean value: 0.9273937989058956 - -key: test_fscore -value: [0.73684211 0.59459459 0.51612903 0.61111111 0.66666667 0.51428571 - 0.58536585 0.60606061 0.59459459 0.61111111] - -mean value: 0.6036761389604157 - -key: train_fscore -value: [0.96932515 0.96072508 0.96072508 0.96385542 0.97264438 0.96096096 - 0.95783133 0.95757576 0.97005988 0.95731707] - -mean value: 0.9631020100266253 - -key: test_precision -value: [0.73684211 0.61111111 0.66666667 0.64705882 0.66666667 0.52941176 - 0.52173913 0.66666667 0.57894737 0.61111111] - -mean value: 0.6236221414576508 - -key: train_precision -value: [0.98136646 0.95783133 0.95783133 0.95808383 0.98159509 0.95808383 - 0.95783133 0.96341463 0.96428571 0.9691358 ] - -mean value: 0.9649459343127333 - -key: test_recall -value: [0.73684211 0.57894737 0.42105263 0.57894737 0.66666667 0.5 - 0.66666667 0.55555556 0.61111111 0.61111111] - -mean value: 0.5926900584795322 - -key: train_recall -value: [0.95757576 0.96363636 0.96363636 0.96969697 0.96385542 0.96385542 - 0.95783133 0.95180723 0.97590361 0.94578313] - -mean value: 0.9613581599123767 - -key: test_accuracy -value: [0.73684211 0.60526316 0.60526316 0.62162162 0.67567568 0.54054054 - 0.54054054 0.64864865 0.59459459 0.62162162] - -mean value: 0.6190611664295875 - -key: train_accuracy -value: [0.97014925 0.96119403 0.96119403 0.96428571 0.97321429 0.96130952 - 0.95833333 0.95833333 0.9702381 0.95833333] - -mean value: 0.9636584932480456 - -key: test_roc_auc -value: [0.73684211 0.60526316 0.60526316 0.62280702 0.6754386 0.53947368 - 0.54385965 0.64619883 0.59502924 0.62134503] - -mean value: 0.6191520467836258 - -key: train_roc_auc -value: [0.96996435 0.96122995 0.96122995 0.96438065 0.97310418 0.96133948 - 0.95832743 0.95825656 0.97030475 0.95818568] - -mean value: 0.9636322963304573 - -key: test_jcc -value: [0.58333333 0.42307692 0.34782609 0.44 0.5 0.34615385 - 0.4137931 0.43478261 0.42307692 0.44 ] - -mean value: 0.4352042824741475 - -key: train_jcc -value: [0.94047619 0.9244186 0.9244186 0.93023256 0.94674556 0.92485549 - 0.91907514 0.91860465 0.94186047 0.91812865] - -mean value: 0.9288815927136209 - -key: TN -value: 122 - -mean value: 122.0 - -key: FP -value: 75 - -mean value: 75.0 - -key: FN -value: 67 - -mean value: 67.0 - -key: TP -value: 109 - -mean value: 109.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.66 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.55127311 0.54282188 0.54563117 0.53473759 0.53946614 0.54847288 - 0.53915787 0.54031396 0.54281473 0.53808832] - -mean value: 0.5422777652740478 - -key: score_time -value: [0.00911593 0.00913954 0.00919604 0.00920296 0.0092597 0.00947762 - 0.00919342 0.00968838 0.00928783 0.00931954] - -mean value: 0.00928809642791748 - -key: test_mcc -value: [0.9486833 0.84327404 0.89973541 0.89181287 0.89736456 0.89181287 - 0.94736842 0.89181287 1. 0.89736456] - -mean value: 0.9109228893996735 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97435897 0.91891892 0.94444444 0.94736842 0.94736842 0.94444444 - 0.97297297 0.94444444 1. 0.94736842] - -mean value: 0.9541689462742096 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.95 0.94444444 1. 0.94736842 0.9 0.94444444 - 0.94736842 0.94444444 1. 0.9 ] - -mean value: 0.9478070175438598 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.89473684 0.89473684 0.94736842 1. 0.94444444 - 1. 0.94444444 1. 1. ] - -mean value: 0.9625730994152046 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97368421 0.92105263 0.94736842 0.94594595 0.94594595 0.94594595 - 0.97297297 0.94594595 1. 0.94594595] - -mean value: 0.9544807965860598 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.92105263 0.94736842 0.94590643 0.94736842 0.94590643 - 0.97368421 0.94590643 1. 0.94736842] - -mean value: 0.9548245614035087 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.95 0.85 0.89473684 0.9 0.9 0.89473684 - 0.94736842 0.89473684 1. 0.9 ] - -mean value: 0.9131578947368422 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 177 - -mean value: 177.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02308536 0.0274148 0.02618098 0.02852201 0.0261507 0.02576995 - 0.03638959 0.03567195 0.0266583 0.11564684] - -mean value: 0.0371490478515625 - -key: score_time -value: [0.01236296 0.01250434 0.01247954 0.01251459 0.01351309 0.01356077 - 0.01335907 0.01956606 0.01478219 0.02465653] - -mean value: 0.014929914474487304 - -key: test_mcc -value: [0.48454371 0.21081851 0.29012943 0.21229278 0.40780312 0.30307132 - 0.01873172 0.29618896 0.31335022 0.13424397] - -mean value: 0.267117373695006 - -key: train_mcc -value: [0.79818268 0.6245587 0.65742177 0.64385204 0.9087817 0.85527622 - 0.63070556 0.72037264 0.88709482 0.69174267] - -mean value: 0.7417988816021179 - -key: test_fscore -value: [0.70588235 0.59459459 0.53333333 0.51612903 0.66666667 0.58064516 - 0.4 0.62857143 0.55172414 0.46666667] - -mean value: 0.5644213374253289 - -key: train_fscore -value: [0.87372014 0.71595331 0.75 0.73563218 0.94936709 0.91503268 - 0.72307692 0.81003584 0.93589744 0.78388278] - -mean value: 0.8192598381317021 - -key: test_precision -value: [0.8 0.61111111 0.72727273 0.66666667 0.73333333 0.69230769 - 0.5 0.64705882 0.72727273 0.58333333] - -mean value: 0.6688356414827002 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.63157895 0.57894737 0.42105263 0.42105263 0.61111111 0.5 - 0.33333333 0.61111111 0.44444444 0.38888889] - -mean value: 0.49415204678362573 - -key: train_recall -value: [0.77575758 0.55757576 0.6 0.58181818 0.90361446 0.84337349 - 0.56626506 0.68072289 0.87951807 0.64457831] - -mean value: 0.7033223804308142 - -key: test_accuracy -value: [0.73684211 0.60526316 0.63157895 0.59459459 0.7027027 0.64864865 - 0.51351351 0.64864865 0.64864865 0.56756757] - -mean value: 0.6298008534850641 - -key: train_accuracy -value: [0.88955224 0.78208955 0.80298507 0.79464286 0.95238095 0.92261905 - 0.78571429 0.8422619 0.94047619 0.82440476] - -mean value: 0.8537126865671641 - -key: test_roc_auc -value: [0.73684211 0.60526316 0.63157895 0.5994152 0.7002924 0.64473684 - 0.50877193 0.64766082 0.64327485 0.5628655 ] - -mean value: 0.6280701754385964 - -key: train_roc_auc -value: [0.88787879 0.77878788 0.8 0.79090909 0.95180723 0.92168675 - 0.78313253 0.84036145 0.93975904 0.82228916] - -mean value: 0.8516611902154072 - -key: test_jcc -value: [0.54545455 0.42307692 0.36363636 0.34782609 0.5 0.40909091 - 0.25 0.45833333 0.38095238 0.30434783] - -mean value: 0.39827183685879336 - -key: train_jcc -value: [0.77575758 0.55757576 0.6 0.58181818 0.90361446 0.84337349 - 0.56626506 0.68072289 0.87951807 0.64457831] - -mean value: 0.7033223804308142 - -key: TN -value: 137 - -mean value: 137.0 - -key: FP -value: 87 - -mean value: 87.0 - -key: FN -value: 52 - -mean value: 52.0 - -key: TP -value: 97 - -mean value: 97.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.31 - -Accuracy on Blind test: 0.65 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02411008 0.03330731 0.03919506 0.03617144 0.03644395 0.03603029 - 0.03654194 0.03618646 0.03594947 0.03615928] - -mean value: 0.0350095272064209 - -key: score_time -value: [0.02242112 0.02337098 0.02189803 0.02041101 0.02282977 0.02142215 - 0.02394748 0.02409768 0.02103877 0.02395391] - -mean value: 0.022539091110229493 - -key: test_mcc -value: [0.84327404 0.63245553 0.69989647 0.69356297 0.83871328 0.73099415 - 0.83871328 0.83918129 0.94721815 0.89679028] - -mean value: 0.7960799452357656 - -key: train_mcc -value: [0.94758521 0.93444619 0.93509103 0.93488293 0.92337258 0.91765006 - 0.92337258 0.92388744 0.91144985 0.9352953 ] - -mean value: 0.9287033165154334 - -key: test_fscore -value: [0.92307692 0.82051282 0.82352941 0.82352941 0.91428571 0.86486486 - 0.91428571 0.91891892 0.97142857 0.94117647] - -mean value: 0.8915608821491174 - -key: train_fscore -value: [0.97196262 0.96636086 0.96594427 0.96615385 0.96 0.95679012 - 0.96 0.95975232 0.95384615 0.96615385] - -mean value: 0.9626964037129424 - -key: test_precision -value: [0.9 0.8 0.93333333 0.93333333 0.94117647 0.84210526 - 0.94117647 0.89473684 1. 1. ] - -mean value: 0.9185861713106295 - -key: train_precision -value: [1. 0.97530864 0.98734177 0.98125 0.98113208 0.98101266 - 0.98113208 0.98726115 0.97484277 0.98742138] - -mean value: 0.9836702520738664 - -key: test_recall -value: [0.94736842 0.84210526 0.73684211 0.73684211 0.88888889 0.88888889 - 0.88888889 0.94444444 0.94444444 0.88888889] - -mean value: 0.8707602339181285 - -key: train_recall -value: [0.94545455 0.95757576 0.94545455 0.95151515 0.93975904 0.93373494 - 0.93975904 0.93373494 0.93373494 0.94578313] - -mean value: 0.9426506024096385 - -key: test_accuracy -value: [0.92105263 0.81578947 0.84210526 0.83783784 0.91891892 0.86486486 - 0.91891892 0.91891892 0.97297297 0.94594595] - -mean value: 0.8957325746799432 - -key: train_accuracy -value: [0.97313433 0.96716418 0.96716418 0.9672619 0.96130952 0.95833333 - 0.96130952 0.96130952 0.95535714 0.9672619 ] - -mean value: 0.9639605543710023 - -key: test_roc_auc -value: [0.92105263 0.81578947 0.84210526 0.84064327 0.91812865 0.86549708 - 0.91812865 0.91959064 0.97222222 0.94444444] - -mean value: 0.8957602339181285 - -key: train_roc_auc -value: [0.97272727 0.96702317 0.96684492 0.96698565 0.96105599 0.95804394 - 0.96105599 0.96098512 0.95510276 0.96700921] - -mean value: 0.9636834023400102 - -key: test_jcc -value: [0.85714286 0.69565217 0.7 0.7 0.84210526 0.76190476 - 0.84210526 0.85 0.94444444 0.88888889] - -mean value: 0.8082243652609785 - -key: train_jcc -value: [0.94545455 0.93491124 0.93413174 0.93452381 0.92307692 0.91715976 - 0.92307692 0.92261905 0.91176471 0.93452381] - -mean value: 0.9281242506601519 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.72 - -Accuracy on Blind test: 0.86 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.24539566 0.2591064 0.25696278 0.28513432 0.25428987 0.25425673 - 0.25858641 0.24959421 0.25876379 0.25151706] - -mean value: 0.25736072063446047 - -key: score_time -value: [0.02299428 0.022506 0.01668525 0.02229381 0.02280021 0.02412319 - 0.02380776 0.02125835 0.02067757 0.02340889] - -mean value: 0.022055530548095705 - -key: test_mcc -value: [0.84327404 0.63245553 0.69989647 0.63129316 0.83871328 0.73099415 - 0.83871328 0.83918129 0.94721815 0.89679028] - -mean value: 0.7898529638576932 - -key: train_mcc -value: [0.94758521 0.93444619 0.93509103 0.9465455 0.92337258 0.91765006 - 0.92337258 0.92388744 0.91144985 0.9352953 ] - -mean value: 0.9298695741839158 - -key: test_fscore -value: [0.92307692 0.82051282 0.82352941 0.8 0.91428571 0.86486486 - 0.91428571 0.91891892 0.97142857 0.94117647] - -mean value: 0.8892079409726469 - -key: train_fscore -value: [0.97196262 0.96636086 0.96594427 0.97247706 0.96 0.95679012 - 0.96 0.95975232 0.95384615 0.96615385] - -mean value: 0.963328725519576 - -key: test_precision -value: [0.9 0.8 0.93333333 0.875 0.94117647 0.84210526 - 0.94117647 0.89473684 1. 1. ] - -mean value: 0.9127528379772961 - -key: train_precision -value: [1. 0.97530864 0.98734177 0.98148148 0.98113208 0.98101266 - 0.98113208 0.98726115 0.97484277 0.98742138] - -mean value: 0.9836934002220146 - -key: test_recall -value: [0.94736842 0.84210526 0.73684211 0.73684211 0.88888889 0.88888889 - 0.88888889 0.94444444 0.94444444 0.88888889] - -mean value: 0.8707602339181285 - -key: train_recall -value: [0.94545455 0.95757576 0.94545455 0.96363636 0.93975904 0.93373494 - 0.93975904 0.93373494 0.93373494 0.94578313] - -mean value: 0.9438627236217597 - -key: test_accuracy -value: [0.92105263 0.81578947 0.84210526 0.81081081 0.91891892 0.86486486 - 0.91891892 0.91891892 0.97297297 0.94594595] - -mean value: 0.8930298719772404 - -key: train_accuracy -value: [0.97313433 0.96716418 0.96716418 0.97321429 0.96130952 0.95833333 - 0.96130952 0.96130952 0.95535714 0.9672619 ] - -mean value: 0.9645557924662402 - -key: test_roc_auc -value: [0.92105263 0.81578947 0.84210526 0.8128655 0.91812865 0.86549708 - 0.91812865 0.91959064 0.97222222 0.94444444] - -mean value: 0.8929824561403509 - -key: train_roc_auc -value: [0.97272727 0.96702317 0.96684492 0.97304625 0.96105599 0.95804394 - 0.96105599 0.96098512 0.95510276 0.96700921] - -mean value: 0.9642894629460708 - -key: test_jcc -value: [0.85714286 0.69565217 0.7 0.66666667 0.84210526 0.76190476 - 0.84210526 0.85 0.94444444 0.88888889] - -mean value: 0.8048910319276452 - -key: train_jcc -value: [0.94545455 0.93491124 0.93413174 0.94642857 0.92307692 0.91715976 - 0.92307692 0.92261905 0.91176471 0.93452381] - -mean value: 0.929314726850628 - -key: TN -value: 173 - -mean value: 173.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 16 - -mean value: 16.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key:/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: 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 - baseline_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: 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 - baseline_CV['Resampling'] = rs_none -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: 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 - baseline_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: 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 - baseline_BT['Resampling'] = rs_none -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.72 - -Accuracy on Blind test: 0.86 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.0332737 0.04178619 0.03603601 0.03498602 0.03617787 0.03529143 - 0.03457618 0.03600287 0.03520012 0.03592062] - -mean value: 0.035925102233886716 - -key: score_time -value: [0.01437902 0.01449084 0.01436162 0.01326323 0.01320291 0.01321411 - 0.01328373 0.0131619 0.01328635 0.01307774] - -mean value: 0.013572144508361816 - -key: test_mcc -value: [0.84327404 0.85280287 0.58218174 0.89973541 0.68421053 0.68803296 - 0.74620251 0.74620251 0.57184997 0.73020842] - -mean value: 0.7344700942135327 - -key: train_mcc -value: [0.83581486 0.8472934 0.83552544 0.85413899 0.85366518 0.85331034 - 0.8479983 0.86543987 0.89448516 0.86693246] - -mean value: 0.8554603991785104 - -key: test_fscore -value: [0.92307692 0.91428571 0.77777778 0.94444444 0.84210526 0.85 - 0.85714286 0.87804878 0.77777778 0.85714286] - -mean value: 0.862180239529405 - -key: train_fscore -value: [0.91616766 0.92261905 0.91666667 0.9244713 0.92492492 0.92537313 - 0.92168675 0.93093093 0.94674556 0.9305136 ] - -mean value: 0.9260099572518534 - -key: test_precision -value: [0.9 1. 0.82352941 1. 0.84210526 0.80952381 - 0.9375 0.81818182 0.82352941 0.88235294] - -mean value: 0.8836722655569405 - -key: train_precision -value: [0.93292683 0.93373494 0.92771084 0.95031056 0.94478528 0.93939394 - 0.94444444 0.95092025 0.95238095 0.9625 ] - -mean value: 0.9439108029098764 - -key: test_recall -value: [0.94736842 0.84210526 0.73684211 0.89473684 0.84210526 0.89473684 - 0.78947368 0.94736842 0.73684211 0.83333333] - -mean value: 0.8464912280701753 - -key: train_recall -value: [0.9 0.91176471 0.90588235 0.9 0.90588235 0.91176471 - 0.9 0.91176471 0.94117647 0.9005848 ] - -mean value: 0.9088820089439285 - -key: test_accuracy -value: [0.92105263 0.92105263 0.78947368 0.94736842 0.84210526 0.84210526 - 0.86842105 0.86842105 0.78378378 0.86486486] - -mean value: 0.8648648648648649 - -key: train_accuracy -value: [0.91764706 0.92352941 0.91764706 0.92647059 0.92647059 0.92647059 - 0.92352941 0.93235294 0.94721408 0.93255132] - -mean value: 0.9273883042953251 - -key: test_roc_auc -value: [0.92105263 0.92105263 0.78947368 0.94736842 0.84210526 0.84210526 - 0.86842105 0.86842105 0.78508772 0.86403509] - -mean value: 0.8649122807017544 - -key: train_roc_auc -value: [0.91764706 0.92352941 0.91764706 0.92647059 0.92647059 0.92647059 - 0.92352941 0.93235294 0.94719642 0.93264534] - -mean value: 0.9273959408324733 - -key: test_jcc -value: [0.85714286 0.84210526 0.63636364 0.89473684 0.72727273 0.73913043 - 0.75 0.7826087 0.63636364 0.75 ] - -mean value: 0.7615724092840799 - -key: train_jcc -value: [0.84530387 0.85635359 0.84615385 0.85955056 0.8603352 0.86111111 - 0.8547486 0.87078652 0.8988764 0.8700565 ] - -mean value: 0.8623276195032383 - -key: TN -value: 167 - -mean value: 167.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.66 - -Accuracy on Blind test: 0.83 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.76346016 0.88995171 0.76627493 0.87296605 0.76096249 0.76881576 - 0.93877697 0.82977033 0.77097201 0.88680196] - -mean value: 0.8248752355575562 - -key: score_time -value: [0.01473117 0.01348615 0.01342273 0.01514244 0.01359129 0.01361775 - 0.01396012 0.01402617 0.01494908 0.01492143] - -mean value: 0.014184832572937012 - -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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.9486833 0.9486833 0.84327404 1. 0.79388419 0.79388419 - 0.73786479 0.74620251 0.63129316 0.83918129] - -mean value: 0.8282950748873722 - -key: train_mcc -value: [0.9707394 0.97653817 0.9707394 0.9707394 0.9707394 1. - 0.9707394 0.9707394 0.98242114 0.97680982] - -mean value: 0.9760205528994824 - -key: test_fscore -value: [0.97435897 0.97297297 0.91891892 1. 0.88888889 0.88888889 - 0.86486486 0.87804878 0.8 0.91891892] - -mean value: 0.9105861208300233 - -key: train_fscore -value: [0.9851632 0.98816568 0.9851632 0.9851632 0.9851632 1. - 0.9851632 0.9851632 0.99115044 0.98816568] - -mean value: 0.9878461031911268 - -key: test_precision -value: [0.95 1. 0.94444444 1. 0.94117647 0.94117647 - 0.88888889 0.81818182 0.875 0.89473684] - -mean value: 0.9253604934796886 - -key: train_precision -value: [0.99401198 0.99404762 0.99401198 0.99401198 0.99401198 1. - 0.99401198 0.99401198 0.99408284 1. ] - -mean value: 0.995220231557173 - -key: test_recall -value: [1. 0.94736842 0.89473684 1. 0.84210526 0.84210526 - 0.84210526 0.94736842 0.73684211 0.94444444] - -mean value: 0.8997076023391812 - -key: train_recall -value: [0.97647059 0.98235294 0.97647059 0.97647059 0.97647059 1. - 0.97647059 0.97647059 0.98823529 0.97660819] - -mean value: 0.9806019951840386 - -key: test_accuracy -value: [0.97368421 0.97368421 0.92105263 1. 0.89473684 0.89473684 - 0.86842105 0.86842105 0.81081081 0.91891892] - -mean value: 0.9124466571834994 - -key: train_accuracy -value: [0.98529412 0.98823529 0.98529412 0.98529412 0.98529412 1. - 0.98529412 0.98529412 0.99120235 0.98826979] - -mean value: 0.9879472140762463 - -key: test_roc_auc -value: [0.97368421 0.97368421 0.92105263 1. 0.89473684 0.89473684 - 0.86842105 0.86842105 0.8128655 0.91959064] - -mean value: 0.9127192982456138 - -key: train_roc_auc -value: [0.98529412 0.98823529 0.98529412 0.98529412 0.98529412 1. - 0.98529412 0.98529412 0.99119367 0.98830409] - -mean value: 0.9879497764017888 - -key: test_jcc -value: [0.95 0.94736842 0.85 1. 0.8 0.8 - 0.76190476 0.7826087 0.66666667 0.85 ] - -mean value: 0.8408548545276234 - -key: train_jcc -value: [0.97076023 0.97660819 0.97076023 0.97076023 0.97076023 1. - 0.97076023 0.97076023 0.98245614 0.97660819] - -mean value: 0.9760233918128653 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 170 - -mean value: 170.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01329303 0.01307821 0.01086497 0.00986385 0.00930023 0.01085114 - 0.00984693 0.01015639 0.00974154 0.01038742] - -mean value: 0.010738372802734375 - -key: score_time -value: [0.01237607 0.00949025 0.00951648 0.00916052 0.00937176 0.01099992 - 0.00985885 0.00924683 0.00946188 0.00924993] - -mean value: 0.009873247146606446 - -key: test_mcc -value: [0.47633051 0.37047929 0.49923018 0.58218174 0.31622777 0.21320072 - 0.31980107 0.52704628 0.35087719 0.57857577] - -mean value: 0.423395050515194 - -key: train_mcc -value: [0.45933241 0.43532424 0.44527052 0.4192824 0.43211912 0.48644347 - 0.443958 0.46428571 0.45509099 0.41429852] - -mean value: 0.44554053940478766 - -key: test_fscore -value: [0.72222222 0.66666667 0.6875 0.77777778 0.66666667 0.63414634 - 0.62857143 0.76923077 0.68421053 0.75 ] - -mean value: 0.6986992398914735 - -key: train_fscore -value: [0.72289157 0.71597633 0.70031546 0.69538462 0.69781931 0.72327044 - 0.70404984 0.70700637 0.71903323 0.69879518] - -mean value: 0.7084542352331991 - -key: test_precision -value: [0.76470588 0.70588235 0.84615385 0.82352941 0.65 0.59090909 - 0.6875 0.75 0.68421053 0.85714286] - -mean value: 0.7360033967580407 - -key: train_precision -value: [0.74074074 0.7202381 0.75510204 0.72903226 0.74172185 0.77702703 - 0.74834437 0.77083333 0.73913043 0.72049689] - -mean value: 0.7442667049578148 - -key: test_recall -value: [0.68421053 0.63157895 0.57894737 0.73684211 0.68421053 0.68421053 - 0.57894737 0.78947368 0.68421053 0.66666667] - -mean value: 0.6719298245614035 - -key: train_recall -value: [0.70588235 0.71176471 0.65294118 0.66470588 0.65882353 0.67647059 - 0.66470588 0.65294118 0.7 0.67836257] - -mean value: 0.6766597867217062 - -key: test_accuracy -value: [0.73684211 0.68421053 0.73684211 0.78947368 0.65789474 0.60526316 - 0.65789474 0.76315789 0.67567568 0.78378378] - -mean value: 0.7091038406827881 - -key: train_accuracy -value: [0.72941176 0.71764706 0.72058824 0.70882353 0.71470588 0.74117647 - 0.72058824 0.72941176 0.72727273 0.70674487] - -mean value: 0.721637053648439 - -key: test_roc_auc -value: [0.73684211 0.68421053 0.73684211 0.78947368 0.65789474 0.60526316 - 0.65789474 0.76315789 0.6754386 0.78070175] - -mean value: 0.7087719298245614 - -key: train_roc_auc -value: [0.72941176 0.71764706 0.72058824 0.70882353 0.71470588 0.74117647 - 0.72058824 0.72941176 0.72719298 0.70682835] - -mean value: 0.7216374269005847 - -key: test_jcc -value: [0.56521739 0.5 0.52380952 0.63636364 0.5 0.46428571 - 0.45833333 0.625 0.52 0.6 ] - -mean value: 0.5393009599096554 - -key: train_jcc -value: [0.56603774 0.55760369 0.53883495 0.53301887 0.53588517 0.56650246 - 0.54326923 0.54679803 0.56132075 0.53703704] - -mean value: 0.5486307924464042 - -key: TN -value: 141 - -mean value: 141.0 - -key: FP -value: 62 - -mean value: 62.0 - -key: FN -value: 48 - -mean value: 48.0 - -key: TP -value: 127 - -mean value: 127.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.4 - -Accuracy on Blind test: 0.7 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.01244974 0.00942373 0.00947642 0.00952387 0.00949097 0.00953722 - 0.0094533 0.00950742 0.00944638 0.00945306] - -mean value: 0.00977621078491211 - -key: score_time -value: [0.00869083 0.00884414 0.0086751 0.00868297 0.00893283 0.00892234 - 0.00869989 0.00873208 0.00888801 0.0088892 ] - -mean value: 0.008795738220214844 - -key: test_mcc -value: [ 0.10540926 -0.21821789 0.16151457 0.37047929 0.21081851 0.10660036 - -0.21081851 0.37686733 0.07739329 0.35087719] - -mean value: 0.13309233979955062 - -key: train_mcc -value: [0.31298546 0.32142857 0.26471046 0.32420883 0.28884059 0.28284271 - 0.29140351 0.31333532 0.30257652 0.26797761] - -mean value: 0.29703095819330205 - -key: test_fscore -value: [0.54054054 0.46511628 0.52941176 0.66666667 0.61538462 0.58536585 - 0.41025641 0.71428571 0.58536585 0.66666667] - -mean value: 0.5779060364893336 - -key: train_fscore -value: [0.67042254 0.68306011 0.63126844 0.67236467 0.65527066 0.63030303 - 0.66849315 0.67226891 0.65902579 0.65181058] - -mean value: 0.6594287870189205 - -key: test_precision -value: [0.55555556 0.41666667 0.6 0.70588235 0.6 0.54545455 - 0.4 0.65217391 0.54545455 0.66666667] - -mean value: 0.5687854245782634 - -key: train_precision -value: [0.64324324 0.6377551 0.63313609 0.6519337 0.63535912 0.65 - 0.62564103 0.64171123 0.6424581 0.62234043] - -mean value: 0.6383578039316298 - -key: test_recall -value: [0.52631579 0.52631579 0.47368421 0.63157895 0.63157895 0.63157895 - 0.42105263 0.78947368 0.63157895 0.66666667] - -mean value: 0.5929824561403509 - -key: train_recall -value: [0.7 0.73529412 0.62941176 0.69411765 0.67647059 0.61176471 - 0.71764706 0.70588235 0.67647059 0.68421053] - -mean value: 0.6831269349845202 - -key: test_accuracy -value: [0.55263158 0.39473684 0.57894737 0.68421053 0.60526316 0.55263158 - 0.39473684 0.68421053 0.54054054 0.67567568] - -mean value: 0.5663584637268848 - -key: train_accuracy -value: [0.65588235 0.65882353 0.63235294 0.66176471 0.64411765 0.64117647 - 0.64411765 0.65588235 0.65102639 0.63343109] - -mean value: 0.6478575125064688 - -key: test_roc_auc -value: [0.55263158 0.39473684 0.57894737 0.68421053 0.60526316 0.55263158 - 0.39473684 0.68421053 0.5380117 0.6754386 ] - -mean value: 0.5660818713450293 - -key: train_roc_auc -value: [0.65588235 0.65882353 0.63235294 0.66176471 0.64411765 0.64117647 - 0.64411765 0.65588235 0.65110079 0.63328173] - -mean value: 0.6478500171998623 - -key: test_jcc -value: [0.37037037 0.3030303 0.36 0.5 0.44444444 0.4137931 - 0.25806452 0.55555556 0.4137931 0.5 ] - -mean value: 0.4119051396426257 - -key: train_jcc -value: [0.50423729 0.5186722 0.4612069 0.50643777 0.48728814 0.46017699 - 0.50205761 0.50632911 0.49145299 0.48347107] - -mean value: 0.49213300717673797 - -key: TN -value: 102 - -mean value: 102.0 - -key: FP -value: 77 - -mean value: 77.0 - -key: FN -value: 87 - -mean value: 87.0 - -key: TP -value: 112 - -mean value: 112.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.15 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.0090425 0.01017594 0.01024008 0.0100944 0.01011252 0.01036167 - 0.00928473 0.01044202 0.00966597 0.01043653] - -mean value: 0.009985637664794923 - -key: score_time -value: [0.01101398 0.01368165 0.01282763 0.01226711 0.01299071 0.01227021 - 0.01205111 0.01257086 0.01200032 0.01729155] - -mean value: 0.012896513938903809 - -key: test_mcc -value: [0.31980107 0.10540926 0.21081851 0.26462806 0.33968311 0.37047929 - 0.15877684 0.15877684 0.13450292 0.30307132] - -mean value: 0.23659472208430649 - -key: train_mcc -value: [0.5119862 0.52944841 0.50644343 0.45882353 0.46471392 0.48824374 - 0.45885529 0.48831133 0.5075007 0.42002512] - -mean value: 0.4834351672071378 - -key: test_fscore -value: [0.62857143 0.54054054 0.59459459 0.61111111 0.71111111 0.7 - 0.55555556 0.55555556 0.57894737 0.58064516] - -mean value: 0.6056632426751272 - -key: train_fscore -value: [0.75942029 0.76608187 0.75862069 0.72941176 0.73156342 0.74336283 - 0.72781065 0.74635569 0.75581395 0.7027027 ] - -mean value: 0.7421143861458315 - -key: test_precision -value: [0.6875 0.55555556 0.61111111 0.64705882 0.61538462 0.66666667 - 0.58823529 0.58823529 0.57894737 0.69230769] - -mean value: 0.6231002421211399 - -key: train_precision -value: [0.74857143 0.76162791 0.74157303 0.72941176 0.73372781 0.74556213 - 0.73214286 0.73988439 0.74712644 0.72222222] - -mean value: 0.7401849984000595 - -key: test_recall -value: [0.57894737 0.52631579 0.57894737 0.57894737 0.84210526 0.73684211 - 0.52631579 0.52631579 0.57894737 0.5 ] - -mean value: 0.5973684210526317 - -key: train_recall -value: [0.77058824 0.77058824 0.77647059 0.72941176 0.72941176 0.74117647 - 0.72352941 0.75294118 0.76470588 0.68421053] - -mean value: 0.7443034055727554 - -key: test_accuracy -value: [0.65789474 0.55263158 0.60526316 0.63157895 0.65789474 0.68421053 - 0.57894737 0.57894737 0.56756757 0.64864865] - -mean value: 0.6163584637268847 - -key: train_accuracy -value: [0.75588235 0.76470588 0.75294118 0.72941176 0.73235294 0.74411765 - 0.72941176 0.74411765 0.75366569 0.70967742] - -mean value: 0.7416284284974988 - -key: test_roc_auc -value: [0.65789474 0.55263158 0.60526316 0.63157895 0.65789474 0.68421053 - 0.57894737 0.57894737 0.56725146 0.64473684] - -mean value: 0.6159356725146199 - -key: train_roc_auc -value: [0.75588235 0.76470588 0.75294118 0.72941176 0.73235294 0.74411765 - 0.72941176 0.74411765 0.75369797 0.70975232] - -mean value: 0.741639146886825 - -key: test_jcc -value: [0.45833333 0.37037037 0.42307692 0.44 0.55172414 0.53846154 - 0.38461538 0.38461538 0.40740741 0.40909091] - -mean value: 0.43676953889022857 - -key: train_jcc -value: [0.61214953 0.62085308 0.61111111 0.57407407 0.57674419 0.5915493 - 0.57209302 0.59534884 0.60747664 0.54166667] - -mean value: 0.5903066442931147 - -key: TN -value: 120 - -mean value: 120.0 - -key: FP -value: 76 - -mean value: 76.0 - -key: FN -value: 69 - -mean value: 69.0 - -key: TP -value: 113 - -mean value: 113.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.63 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SVC(random_state=42))]) - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -fit_time -value: [0.01673365 0.01617885 0.01566529 0.01583219 0.01580167 0.01587462 - 0.01614952 0.01621389 0.0158546 0.01602769] - -mean value: 0.016033196449279787 - -key: score_time -value: [0.01104999 0.01085353 0.01082468 0.01059842 0.01070094 0.01058435 - 0.01057363 0.01047254 0.01092243 0.01061535] - -mean value: 0.010719585418701171 - -key: test_mcc -value: [0.53300179 0.58218174 0.57894737 0.68421053 0.52704628 0.4061812 - 0.36842105 0.52704628 0.29824561 0.57857577] - -mean value: 0.5083857606438198 - -key: train_mcc -value: [0.69416569 0.70001211 0.76555402 0.69585343 0.69411765 0.71769673 - 0.67077394 0.72504884 0.71317436 0.74278665] - -mean value: 0.7119183416603204 - -key: test_fscore -value: [0.74285714 0.77777778 0.78947368 0.84210526 0.76923077 0.73913043 - 0.68421053 0.75675676 0.64864865 0.75 ] - -mean value: 0.7500191003737914 - -key: train_fscore -value: [0.84795322 0.84955752 0.87951807 0.84146341 0.84705882 0.85798817 - 0.83333333 0.85714286 0.85285285 0.86826347] - -mean value: 0.8535131731014287 - -key: test_precision -value: [0.8125 0.82352941 0.78947368 0.84210526 0.75 0.62962963 - 0.68421053 0.77777778 0.66666667 0.85714286] - -mean value: 0.7633035816665847 - -key: train_precision -value: [0.84302326 0.85207101 0.90123457 0.87341772 0.84705882 0.86309524 - 0.84337349 0.88679245 0.87116564 0.88957055] - -mean value: 0.8670802755901097 - -key: test_recall -value: [0.68421053 0.73684211 0.78947368 0.84210526 0.78947368 0.89473684 - 0.68421053 0.73684211 0.63157895 0.66666667] - -mean value: 0.7456140350877193 - -key: train_recall -value: [0.85294118 0.84705882 0.85882353 0.81176471 0.84705882 0.85294118 - 0.82352941 0.82941176 0.83529412 0.84795322] - -mean value: 0.8406776745786033 - -key: test_accuracy -value: [0.76315789 0.78947368 0.78947368 0.84210526 0.76315789 0.68421053 - 0.68421053 0.76315789 0.64864865 0.78378378] - -mean value: 0.7511379800853486 - -key: train_accuracy -value: [0.84705882 0.85 0.88235294 0.84705882 0.84705882 0.85882353 - 0.83529412 0.86176471 0.85630499 0.87096774] - -mean value: 0.855668449197861 - -key: test_roc_auc -value: [0.76315789 0.78947368 0.78947368 0.84210526 0.76315789 0.68421053 - 0.68421053 0.76315789 0.64912281 0.78070175] - -mean value: 0.750877192982456 - -key: train_roc_auc -value: [0.84705882 0.85 0.88235294 0.84705882 0.84705882 0.85882353 - 0.83529412 0.86176471 0.85624355 0.87103543] - -mean value: 0.8556690746474029 - -key: test_jcc -value: [0.59090909 0.63636364 0.65217391 0.72727273 0.625 0.5862069 - 0.52 0.60869565 0.48 0.6 ] - -mean value: 0.602662191631457 - -key: train_jcc -value: [0.73604061 0.73846154 0.78494624 0.72631579 0.73469388 0.75129534 - 0.71428571 0.75 0.7434555 0.76719577] - -mean value: 0.7446690366833684 - -key: TN -value: 143 - -mean value: 143.0 - -key: FP -value: 48 - -mean value: 48.0 - -key: FN -value: 46 - -mean value: 46.0 - -key: TP -value: 141 - -mean value: 141.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.29675078 1.69036794 1.45674276 1.29581451 1.38615084 1.27672696 - 1.46914959 1.49457574 1.28470659 1.40993524] - -mean value: 1.406092095375061 - -key: score_time -value: [0.01507092 0.01379061 0.01387954 0.01390195 0.01387835 0.01387501 - 0.01388478 0.01414752 0.01408958 0.01385117] - -mean value: 0.014036941528320312 - -key: test_mcc -value: [0.73786479 0.68421053 0.63245553 0.85280287 0.73786479 0.73786479 - 0.79388419 0.78947368 0.41299552 0.73099415] - -mean value: 0.711041083133732 - -key: train_mcc -value: [1. 1. 1. 0.99413485 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994134846772434 - -key: test_fscore -value: [0.87179487 0.84210526 0.82051282 0.91428571 0.86486486 0.86486486 - 0.9 0.89473684 0.68571429 0.86486486] - -mean value: 0.8523744392165444 - -key: train_fscore -value: [1. 1. 1. 0.99705015 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997050147492625 - -key: test_precision -value: [0.85 0.84210526 0.8 1. 0.88888889 0.88888889 - 0.85714286 0.89473684 0.75 0.84210526] - -mean value: 0.8613868003341688 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 0.84210526 0.84210526 0.84210526 0.84210526 0.84210526 - 0.94736842 0.89473684 0.63157895 0.88888889] - -mean value: 0.8467836257309942 - -key: train_recall -value: [1. 1. 1. 0.99411765 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: test_accuracy -value: [0.86842105 0.84210526 0.81578947 0.92105263 0.86842105 0.86842105 - 0.89473684 0.89473684 0.7027027 0.86486486] - -mean value: 0.8541251778093883 - -key: train_accuracy -value: [1. 1. 1. 0.99705882 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997058823529411 - -key: test_roc_auc -value: [0.86842105 0.84210526 0.81578947 0.92105263 0.86842105 0.86842105 - 0.89473684 0.89473684 0.70467836 0.86549708] - -mean value: 0.8543859649122807 - -key: train_roc_auc -value: [1. 1. 1. 0.99705882 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997058823529411 - -key: test_jcc -value: [0.77272727 0.72727273 0.69565217 0.84210526 0.76190476 0.76190476 - 0.81818182 0.80952381 0.52173913 0.76190476] - -mean value: 0.7472916480925633 - -key: train_jcc -value: [1. 1. 1. 0.99411765 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: TN -value: 163 - -mean value: 163.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 26 - -mean value: 26.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.6 - -Accuracy on Blind test: 0.8 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02621365 0.017555 0.01615143 0.0167098 0.01575971 0.01611233 - 0.01574588 0.01579618 0.01575065 0.0148294 ] - -mean value: 0.01706240177154541 - -key: score_time -value: [0.0125668 0.00924015 0.0087862 0.00866389 0.00870657 0.00893092 - 0.00884199 0.00876665 0.00891376 0.00878024] - -mean value: 0.009219717979431153 - -key: test_mcc -value: [1. 0.9486833 0.84327404 0.9486833 0.89473684 0.89973541 - 0.84327404 0.84327404 0.83918129 0.89181287] - -mean value: 0.8952655129230216 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97297297 0.91891892 0.97435897 0.94736842 0.94444444 - 0.92307692 0.92307692 0.91891892 0.94444444] - -mean value: 0.9467580941265152 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.94444444 0.95 0.94736842 1. - 0.9 0.9 0.94444444 0.94444444] - -mean value: 0.9530701754385966 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94736842 0.89473684 1. 0.94736842 0.89473684 - 0.94736842 0.94736842 0.89473684 0.94444444] - -mean value: 0.941812865497076 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97368421 0.92105263 0.97368421 0.94736842 0.94736842 - 0.92105263 0.92105263 0.91891892 0.94594595] - -mean value: 0.9470128022759601 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97368421 0.92105263 0.97368421 0.94736842 0.94736842 - 0.92105263 0.92105263 0.91959064 0.94590643] - -mean value: 0.9470760233918127 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.94736842 0.85 0.95 0.9 0.89473684 - 0.85714286 0.85714286 0.85 0.89473684] - -mean value: 0.9001127819548872 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 11 - -mean value: 11.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 178 - -mean value: 178.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.88 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.11378217 0.11851716 0.11258173 0.11372399 0.12818909 0.11349511 - 0.11403131 0.11479163 0.11690593 0.11565852] - -mean value: 0.11616766452789307 - -key: score_time -value: [0.01751447 0.01824856 0.01766467 0.01868176 0.02479267 0.01747727 - 0.01788139 0.01806259 0.01758528 0.01756859] - -mean value: 0.018547725677490235 - -key: test_mcc -value: [0.68803296 0.58218174 0.42163702 0.78947368 0.73786479 0.68803296 - 0.79388419 0.68421053 0.40643275 0.73821295] - -mean value: 0.6529963559970604 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.83333333 0.77777778 0.7027027 0.89473684 0.86486486 0.85 - 0.88888889 0.84210526 0.7027027 0.84848485] - -mean value: 0.8205597224018277 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.88235294 0.82352941 0.72222222 0.89473684 0.88888889 0.80952381 - 0.94117647 0.84210526 0.72222222 0.93333333] - -mean value: 0.8460091404983044 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.78947368 0.73684211 0.68421053 0.89473684 0.84210526 0.89473684 - 0.84210526 0.84210526 0.68421053 0.77777778] - -mean value: 0.7988304093567251 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.84210526 0.78947368 0.71052632 0.89473684 0.86842105 0.84210526 - 0.89473684 0.84210526 0.7027027 0.86486486] - -mean value: 0.8251778093883356 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.84210526 0.78947368 0.71052632 0.89473684 0.86842105 0.84210526 - 0.89473684 0.84210526 0.70321637 0.8625731 ] - -mean value: 0.825 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.71428571 0.63636364 0.54166667 0.80952381 0.76190476 0.73913043 - 0.8 0.72727273 0.54166667 0.73684211] - -mean value: 0.7008656522729748 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 161 - -mean value: 161.0 - -key: FP -value: 38 - -mean value: 38.0 - -key: FN -value: 28 - -mean value: 28.0 - -key: TP -value: 151 - -mean value: 151.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.5 - -Accuracy on Blind test: 0.75 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.00967932 0.01013827 0.00966501 0.00955963 0.01017594 0.0094707 - 0.00996065 0.01011372 0.01056314 0.00966978] - -mean value: 0.009899616241455078 - -key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -score_time -value: [0.00874996 0.00857186 0.00861192 0.00872397 0.00944138 0.00895977 - 0.00904632 0.00916934 0.00933981 0.00943565] - -mean value: 0.009004998207092284 - -key: test_mcc -value: [ 0.10540926 0.42163702 0.26462806 0.47633051 0.52704628 0.47368421 - 0.36842105 0.26919095 -0.08187135 0.29618896] - -mean value: 0.31206649530607145 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.56410256 0.7027027 0.61111111 0.72222222 0.75675676 0.73684211 - 0.68421053 0.58823529 0.47368421 0.62857143] - -mean value: 0.6468438921689696 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.55 0.72222222 0.64705882 0.76470588 0.77777778 0.73684211 - 0.68421053 0.66666667 0.47368421 0.64705882] - -mean value: 0.6670227038183695 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.57894737 0.68421053 0.57894737 0.68421053 0.73684211 0.73684211 - 0.68421053 0.52631579 0.47368421 0.61111111] - -mean value: 0.62953216374269 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.55263158 0.71052632 0.63157895 0.73684211 0.76315789 0.73684211 - 0.68421053 0.63157895 0.45945946 0.64864865] - -mean value: 0.6555476529160741 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.55263158 0.71052632 0.63157895 0.73684211 0.76315789 0.73684211 - 0.68421053 0.63157895 0.45906433 0.64766082] - -mean value: 0.6554093567251462 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.39285714 0.54166667 0.44 0.56521739 0.60869565 0.58333333 - 0.52 0.41666667 0.31034483 0.45833333] - -mean value: 0.4837115013921611 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 129 - -mean value: 129.0 - -key: FP -value: 70 - -mean value: 70.0 - -key: FN -value: 60 - -mean value: 60.0 - -key: TP -value: 119 - -mean value: 119.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.51 - -Accuracy on Blind test: 0.76 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.55050349 1.54180455 1.54141712 1.54411578 1.53240228 1.53040624 - 1.52713609 1.64760947 1.56103945 1.5545857 ] - -mean value: 1.5531020164489746 - -key: score_time -value: [0.09159446 0.09441328 0.09017587 0.09086132 0.09102893 0.09094095 - 0.09430623 0.09099174 0.09195733 0.09637094] - -mean value: 0.09226410388946533 - -key: test_mcc -value: [1. 0.85280287 0.73786479 0.89473684 0.78947368 0.89973541 - 0.9486833 0.78947368 0.67849265 0.89181287] - -mean value: 0.8483076082890996 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.91428571 0.86486486 0.94736842 0.89473684 0.94444444 - 0.97297297 0.89473684 0.85 0.94444444] - -mean value: 0.9227854546275598 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.88888889 0.94736842 0.89473684 1. - 1. 0.89473684 0.80952381 0.94444444] - -mean value: 0.9379699248120301 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.84210526 0.84210526 0.94736842 0.89473684 0.89473684 - 0.94736842 0.89473684 0.89473684 0.94444444] - -mean value: 0.9102339181286551 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.97368421 0.89473684 0.83783784 0.94594595] - -mean value: 0.9231152204836416 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.97368421 0.89473684 0.83625731 0.94590643] - -mean value: 0.9229532163742691 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.84210526 0.76190476 0.9 0.80952381 0.89473684 - 0.94736842 0.80952381 0.73913043 0.89473684] - -mean value: 0.8599030184156042 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 172 - -mean value: 172.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.93 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.88449192 0.93275809 0.89632082 0.91076493 0.89204955 0.91438007 - 0.98439193 0.89490986 0.91231918 0.96720266] - -mean value: 0.9189589023590088 - -key: score_time -value: [0.17622328 0.2140038 0.22708845 0.24604774 0.21113658 0.18967485 - 0.15859175 0.19707727 0.18973684 0.20282483] - -mean value: 0.20124053955078125 - -key: test_mcc -value: [1. 0.85280287 0.73786479 0.89473684 0.78947368 0.89973541 - 0.79388419 0.79388419 0.67849265 0.89181287] - -mean value: 0.8332687472704846 - -key: train_mcc -value: [0.9707394 0.95897286 0.9707394 0.96470588 0.97060503 0.97060503 - 0.97060503 0.96497304 0.98242174 0.95896113] - -mean value: 0.9683328537519588 - -key: test_fscore -value: [1. 0.91428571 0.86486486 0.94736842 0.89473684 0.94444444 - 0.88888889 0.9 0.85 0.94444444] - -mean value: 0.9149033620086252 - -key: train_fscore -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( -[0.9851632 0.97922849 0.9851632 0.98235294 0.98525074 0.98533724 - 0.98525074 0.98214286 0.99120235 0.97947214] - -mean value: 0.9840563899593292 - -key: test_precision -value: [1. 1. 0.88888889 0.94736842 0.89473684 1. - 0.94117647 0.85714286 0.80952381 0.94444444] - -mean value: 0.9283281733746132 - -key: train_precision -value: [0.99401198 0.98802395 0.99401198 0.98235294 0.98816568 0.98245614 - 0.98816568 0.9939759 0.98830409 0.98235294] - -mean value: 0.988182128502389 - -key: test_recall -value: [1. 0.84210526 0.84210526 0.94736842 0.89473684 0.89473684 - 0.84210526 0.94736842 0.89473684 0.94444444] - -mean value: 0.9049707602339181 - -key: train_recall -value: [0.97647059 0.97058824 0.97647059 0.98235294 0.98235294 0.98823529 - 0.98235294 0.97058824 0.99411765 0.97660819] - -mean value: 0.9800137598899209 - -key: test_accuracy -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.89473684 0.89473684 0.83783784 0.94594595] - -mean value: 0.9152204836415363 - -key: train_accuracy -value: [0.98529412 0.97941176 0.98529412 0.98235294 0.98529412 0.98529412 - 0.98529412 0.98235294 0.99120235 0.97947214] - -mean value: 0.9841262722097639 - -key: test_roc_auc -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.89473684 0.89473684 0.83625731 0.94590643] - -mean value: 0.9150584795321638 - -key: train_roc_auc -value: [0.98529412 0.97941176 0.98529412 0.98235294 0.98529412 0.98529412 - 0.98529412 0.98235294 0.99121087 0.97948056] - -mean value: 0.9841279669762644 - -key: test_jcc -value: [1. 0.84210526 0.76190476 0.9 0.80952381 0.89473684 - 0.8 0.81818182 0.73913043 0.89473684] - -mean value: 0.846031977176142 - -key: train_jcc -value: [0.97076023 0.95930233 0.97076023 0.96531792 0.97093023 0.97109827 - 0.97093023 0.96491228 0.98255814 0.95977011] - -mean value: 0.9686339978684197 - -key: TN -value: 175 - -mean value: 175.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 171 - -mean value: 171.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.07452631 0.05661058 0.05810285 0.05777645 0.05764389 0.0566597 - 0.06467175 0.0585196 0.05686641 0.06147242] - -mean value: 0.060284996032714845 - -key: score_time -value: [0.0104022 0.01035857 0.01051092 0.01051521 0.01035166 0.01051712 - 0.01070046 0.01051188 0.01068878 0.01052642] - -mean value: 0.010508322715759277 - -key: test_mcc -value: [1. 1. 1. 0.9486833 0.89473684 0.89973541 - 0.84327404 0.9486833 0.78362573 0.89181287] - -mean value: 0.9210551488251524 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.97435897 0.94736842 0.94444444 - 0.92307692 0.97435897 0.89473684 0.94444444] - -mean value: 0.9602789023841656 - -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. 0.95 0.94736842 1. - 0.9 0.95 0.89473684 0.94444444] - -mean value: 0.9586549707602339 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.89473684 - 0.94736842 1. 0.89473684 0.94444444] - -mean value: 0.9628654970760234 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.97368421 0.94736842 0.94736842 - 0.92105263 0.97368421 0.89189189 0.94594595] - -mean value: 0.960099573257468 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.97368421 0.94736842 0.94736842 - 0.92105263 0.97368421 0.89181287 0.94590643] - -mean value: 0.9600877192982458 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.95 0.9 0.89473684 - 0.85714286 0.95 0.80952381 0.89473684] - -mean value: 0.9256140350877194 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 182 - -mean value: 182.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03860354 0.0713439 0.06382632 0.06391168 0.07470369 0.09007788 - 0.07325983 0.0648284 0.09072328 0.08979583] - -mean value: 0.07210743427276611 - -key: score_time -value: [0.02338386 0.02084994 0.0242784 0.02525091 0.02362394 0.02095866 - 0.02176857 0.02476358 0.02270174 0.02344894] - -mean value: 0.023102855682373045 - -key: test_mcc -value: [0.85280287 0.68421053 0.74620251 0.9486833 0.68803296 0.53300179 - 0.68421053 0.79388419 0.41299552 0.73020842] - -mean value: 0.7074232603104091 - -key: train_mcc -value: [0.94143711 0.94143711 0.94720632 0.94143711 0.95923851 0.95884012 - 0.95320508 0.94720632 0.95314274 0.96507709] - -mean value: 0.9508227529399663 - -key: test_fscore -value: [0.92682927 0.84210526 0.85714286 0.97435897 0.83333333 0.74285714 - 0.84210526 0.88888889 0.68571429 0.85714286] - -mean value: 0.8450478134046812 - -key: train_fscore -value: [0.9702381 0.9702381 0.97329377 0.9702381 0.97910448 0.97935103 - 0.97619048 0.97329377 0.97633136 0.98224852] - -mean value: 0.9750527690713872 - -key: test_precision -value: [0.86363636 0.84210526 0.9375 0.95 0.88235294 0.8125 - 0.84210526 0.94117647 0.75 0.88235294] - -mean value: 0.870372924289333 - -key: train_precision -value: [0.98192771 0.98192771 0.98203593 0.98192771 0.99393939 0.98224852 - 0.98795181 0.98203593 0.98214286 0.99401198] - -mean value: 0.9850149543886676 - -key: test_recall -value: [1. 0.84210526 0.78947368 1. 0.78947368 0.68421053 - 0.84210526 0.84210526 0.63157895 0.83333333] - -mean value: 0.8254385964912281 - -key: train_recall -value: [0.95882353 0.95882353 0.96470588 0.95882353 0.96470588 0.97647059 - 0.96470588 0.96470588 0.97058824 0.97076023] - -mean value: 0.9653113175094598 - -key: test_accuracy -value: [0.92105263 0.84210526 0.86842105 0.97368421 0.84210526 0.76315789 - 0.84210526 0.89473684 0.7027027 0.86486486] - -mean value: 0.8514935988620198 - -key: train_accuracy -value: [0.97058824 0.97058824 0.97352941 0.97058824 0.97941176 0.97941176 - 0.97647059 0.97352941 0.97653959 0.98240469] - -mean value: 0.9753061928583749 - -key: test_roc_auc -value: [0.92105263 0.84210526 0.86842105 0.97368421 0.84210526 0.76315789 - 0.84210526 0.89473684 0.70467836 0.86403509] - -mean value: 0.8516081871345029 - -key: train_roc_auc -value: [0.97058824 0.97058824 0.97352941 0.97058824 0.97941176 0.97941176 - 0.97647059 0.97352941 0.97652219 0.98243894] - -mean value: 0.9753078775369799 - -key: test_jcc -value: [0.86363636 0.72727273 0.75 0.95 0.71428571 0.59090909 - 0.72727273 0.8 0.52173913 0.75 ] - -mean value: 0.7395115753811405 - -key: train_jcc -value: [0.94219653 0.94219653 0.94797688 0.94219653 0.95906433 0.95953757 - 0.95348837 0.94797688 0.95375723 0.96511628] - -mean value: 0.9513507128937189 - -key: TN -value: 166 - -mean value: 166.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 156 - -mean value: 156.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.6 - -Accuracy on Blind test: 0.8 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01296568 0.01266599 0.01078033 0.01031494 0.00997376 0.01013398 - 0.00964069 0.00951076 0.01031899 0.01042151] - -mean value: 0.010672664642333985 - -key: score_time -value: [0.01173091 0.00919127 0.00931907 0.00947118 0.00928521 0.00886512 - 0.00858426 0.00889182 0.00946736 0.00999355] - -mean value: 0.009479975700378418 - -key: test_mcc -value: [0.26919095 0.05292561 0.37686733 0.37047929 0.21821789 0.29012943 - 0.10910895 0.42640143 0.24408665 0.4163404 ] - -mean value: 0.27737479311693064 - -key: train_mcc -value: [0.294138 0.27126489 0.30607305 0.28847996 0.32366944 0.30012984 - 0.30622161 0.32353501 0.29631159 0.2790312 ] - -mean value: 0.2988854595429741 - -key: test_fscore -value: [0.58823529 0.55 0.64705882 0.66666667 0.65116279 0.69565217 - 0.48484848 0.73170732 0.66666667 0.64516129] - -mean value: 0.6327159507835347 - -key: train_fscore -value: [0.64912281 0.64772727 0.65895954 0.65129683 0.66666667 0.64477612 - 0.66091954 0.66076696 0.65116279 0.65155807] - -mean value: 0.6542956599591772 - -key: test_precision -value: [0.66666667 0.52380952 0.73333333 0.70588235 0.58333333 0.59259259 - 0.57142857 0.68181818 0.60869565 0.76923077] - -mean value: 0.6436790977328062 - -key: train_precision -value: [0.64534884 0.62637363 0.64772727 0.63841808 0.65714286 0.65454545 - 0.64606742 0.66272189 0.64367816 0.63186813] - -mean value: 0.6453891729103691 - -key: test_recall -value: [0.52631579 0.57894737 0.57894737 0.63157895 0.73684211 0.84210526 - 0.42105263 0.78947368 0.73684211 0.55555556] - -mean value: 0.639766081871345 - -key: train_recall -value: [0.65294118 0.67058824 0.67058824 0.66470588 0.67647059 0.63529412 - 0.67647059 0.65882353 0.65882353 0.67251462] - -mean value: 0.6637220502235983 - -key: test_accuracy -value: [0.63157895 0.52631579 0.68421053 0.68421053 0.60526316 0.63157895 - 0.55263158 0.71052632 0.62162162 0.7027027 ] - -mean value: 0.6350640113798008 - -key: train_accuracy -value: [0.64705882 0.63529412 0.65294118 0.64411765 0.66176471 0.65 - 0.65294118 0.66176471 0.64809384 0.63929619] - -mean value: 0.6493272382266689 - -key: test_roc_auc -value: [0.63157895 0.52631579 0.68421053 0.68421053 0.60526316 0.63157895 - 0.55263158 0.71052632 0.61842105 0.69883041] - -mean value: 0.6343567251461988 - -key: train_roc_auc -value: [0.64705882 0.63529412 0.65294118 0.64411765 0.66176471 0.65 - 0.65294118 0.66176471 0.64812521 0.63919849] - -mean value: 0.6493206054351565 - -key: test_jcc -value: [0.41666667 0.37931034 0.47826087 0.5 0.48275862 0.53333333 - 0.32 0.57692308 0.5 0.47619048] - -mean value: 0.46634433881960113 - -key: train_jcc -value: [0.48051948 0.4789916 0.49137931 0.48290598 0.5 0.47577093 - 0.49356223 0.49339207 0.48275862 0.48319328] - -mean value: 0.4862473495763896 - -key: TN -value: 119 - -mean value: 119.0 - -key: FP -value: 68 - -mean value: 68.0 - -key: FN -value: 70 - -mean value: 70.0 - -key: TP -value: 121 - -mean value: 121.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.62 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01405716 0.02360177 0.01937485 0.0186305 0.01952863 0.02076507 - 0.01877522 0.02132988 0.02373362 0.02087426] - -mean value: 0.02006709575653076 - -key: score_time -value: [0.00893259 0.01143122 0.01173043 0.01170659 0.01177454 0.01171827 - 0.01168418 0.01177335 0.01172948 0.01170611] - -mean value: 0.011418676376342774 - -key: test_mcc -value: [0.84327404 0.80757285 0.80757285 0.80757285 0.79388419 0.79388419 - 0.80757285 0.63828474 0.51319869 0.83918129] - -mean value: 0.765199853985027 - -key: train_mcc -value: [0.88825066 0.92546884 0.89716511 0.85562842 0.94720632 0.94786272 - 0.87137986 0.81787037 0.94762566 0.91813495] - -mean value: 0.901659290424066 - -key: test_fscore -value: [0.92307692 0.88235294 0.88235294 0.88235294 0.88888889 0.88888889 - 0.88235294 0.82608696 0.76923077 0.91891892] - -mean value: 0.874450311023201 - -key: train_fscore -value: [0.9439528 0.96048632 0.94478528 0.91772152 0.97329377 0.97297297 - 0.92789969 0.91056911 0.97391304 0.95953757] - -mean value: 0.948513206907229 - -key: test_precision -value: [0.9 1. 1. 1. 0.94117647 0.94117647 - 1. 0.7037037 0.75 0.89473684] - -mean value: 0.9130793486985438 - -key: train_precision -value: [0.94674556 0.99371069 0.98717949 0.99315068 0.98203593 0.99386503 - 0.99328859 0.84422111 0.96 0.94857143] - -mean value: 0.9642768509586723 - -key: test_recall -value: [0.94736842 0.78947368 0.78947368 0.78947368 0.84210526 0.84210526 - 0.78947368 1. 0.78947368 0.94444444] - -mean value: 0.8523391812865497 - -key: train_recall -value: [0.94117647 0.92941176 0.90588235 0.85294118 0.96470588 0.95294118 - 0.87058824 0.98823529 0.98823529 0.97076023] - -mean value: 0.9364877880976952 - -key: test_accuracy -value: [0.92105263 0.89473684 0.89473684 0.89473684 0.89473684 0.89473684 - 0.89473684 0.78947368 0.75675676 0.91891892] - -mean value: 0.8754623044096729 - -key: train_accuracy -value: [0.94411765 0.96176471 0.94705882 0.92352941 0.97352941 0.97352941 - 0.93235294 0.90294118 0.97360704 0.95894428] - -mean value: 0.9491374849059857 - -key: test_roc_auc -value: [0.92105263 0.89473684 0.89473684 0.89473684 0.89473684 0.89473684 - 0.89473684 0.78947368 0.75584795 0.91959064] - -mean value: 0.8754385964912281 - -key: train_roc_auc -value: [0.94411765 0.96176471 0.94705882 0.92352941 0.97352941 0.97352941 - 0.93235294 0.90294118 0.97364981 0.95890953] - -mean value: 0.9491382868937048 - -key: test_jcc -value: [0.85714286 0.78947368 0.78947368 0.78947368 0.8 0.8 - 0.78947368 0.7037037 0.625 0.85 ] - -mean value: 0.7793741297688666 - -key: train_jcc -value: [0.89385475 0.92397661 0.89534884 0.84795322 0.94797688 0.94736842 - 0.86549708 0.8358209 0.94915254 0.92222222] - -mean value: 0.9029171446180289 - -key: TN -value: 170 - -mean value: 170.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 19 - -mean value: 19.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.75 - -Accuracy on Blind test: 0.88 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01981235 0.02006984 0.02132463 0.01739812 0.01893473 0.017694 - 0.02019167 0.01976776 0.01872396 0.0207231 ] - -mean value: 0.01946401596069336 - -key: score_time -value: [0.01271915 0.01223636 0.01229072 0.01278162 0.01286674 0.01423645 - 0.01369119 0.01314783 0.01298761 0.01500344] - -mean value: 0.013196110725402832 - -key: test_mcc -value: [0.80757285 0.79388419 0.65465367 0.85280287 0.79388419 0.09759001 - 0.74620251 0.65465367 0.7163504 0.51121719] - -mean value: 0.6628811533362169 - -key: train_mcc -value: [0.87904457 0.92354539 0.85298238 0.88734098 0.88199656 0.31108551 - 0.90352405 0.83159022 0.80198777 0.53710285] - -mean value: 0.7810200289426288 - -key: test_fscore -value: [0.9047619 0.88888889 0.8372093 0.91428571 0.88888889 0.66666667 - 0.85714286 0.78787879 0.8125 0.76595745] - -mean value: 0.8324180457647801 - -key: train_fscore -value: [0.94050992 0.96187683 0.92777778 0.9378882 0.93457944 0.70833333 - 0.94769231 0.89967638 0.87788779 0.78440367] - -mean value: 0.8920625638580437 - -key: test_precision -value: [0.82608696 0.94117647 0.75 1. 0.94117647 0.51428571 - 0.9375 0.92857143 1. 0.62068966] - -mean value: 0.8459486695727767 - -key: train_precision -value: [0.90710383 0.95906433 0.87894737 0.99342105 0.99337748 0.5483871 - 0.99354839 1. 1. 0.64528302] - -mean value: 0.8919132559857225 - -key: test_recall -value: [1. 0.84210526 0.94736842 0.84210526 0.84210526 0.94736842 - 0.78947368 0.68421053 0.68421053 1. ] - -mean value: 0.8578947368421052 - -key: train_recall -value: [0.97647059 0.96470588 0.98235294 0.88823529 0.88235294 1. - 0.90588235 0.81764706 0.78235294 1. ] - -mean value: 0.9200000000000002 - -key: test_accuracy -value: [0.89473684 0.89473684 0.81578947 0.92105263 0.89473684 0.52631579 - 0.86842105 0.81578947 0.83783784 0.7027027 ] - -mean value: 0.8172119487908962 - -key: train_accuracy -value: [0.93823529 0.96176471 0.92352941 0.94117647 0.93823529 0.58823529 - 0.95 0.90882353 0.8914956 0.72434018] - -mean value: 0.87658357771261 - -key: test_roc_auc -value: [0.89473684 0.89473684 0.81578947 0.92105263 0.89473684 0.52631579 - 0.86842105 0.81578947 0.84210526 0.71052632] - -mean value: 0.8184210526315789 - -key: train_roc_auc -value: [0.93823529 0.96176471 0.92352941 0.94117647 0.93823529 0.58823529 - 0.95 0.90882353 0.89117647 0.72352941] - -mean value: 0.876470588235294 - -key: test_jcc -value: [0.82608696 0.8 0.72 0.84210526 0.8 0.5 - 0.75 0.65 0.68421053 0.62068966] - -mean value: 0.7193092401167837 - -key: train_jcc -value: [0.88770053 0.92655367 0.86528497 0.88304094 0.87719298 0.5483871 - 0.9005848 0.81764706 0.78235294 0.64528302] - -mean value: 0.8134028010261417 - -key: TN -value: 147 - -mean value: 147.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 42 - -mean value: 42.0 - -key: TP -value: 162 - -mean value: 162.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.61 - -Accuracy on Blind test: 0.79 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.16470337 0.14540505 0.14498448 0.14558506 0.14616489 0.14864588 - 0.14505124 0.14473462 0.14635205 0.14556193] - -mean value: 0.14771885871887208 - -key: score_time -value: [0.01508999 0.0156076 0.01552558 0.01492333 0.01503325 0.01512408 - 0.01488519 0.01495552 0.0155983 0.01502132] - -mean value: 0.01517641544342041 - -key: test_mcc -value: [1. 1. 1. 0.9486833 0.9486833 0.9486833 - 0.89973541 0.89473684 0.73099415 0.94736842] - -mean value: 0.9318884720198657 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.97435897 0.97297297 0.97297297 - 0.95 0.94736842 0.86486486 0.97297297] - -mean value: 0.9655511179195392 - -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. 0.95 1. 1. - 0.9047619 0.94736842 0.88888889 0.94736842] - -mean value: 0.9638387635756057 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.94736842 - 1. 0.94736842 0.84210526 1. ] - -mean value: 0.968421052631579 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.97368421 0.97368421 0.97368421 - 0.94736842 0.94736842 0.86486486 0.97297297] - -mean value: 0.965362731152205 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.97368421 0.97368421 0.97368421 - 0.94736842 0.94736842 0.86549708 0.97368421] - -mean value: 0.9654970760233917 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.95 0.94736842 0.94736842 - 0.9047619 0.9 0.76190476 0.94736842] - -mean value: 0.9358771929824561 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 182 - -mean value: 182.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03605485 0.04690123 0.04633117 0.048949 0.05641532 0.05372238 - 0.04046464 0.04024243 0.04414415 0.04762149] - -mean value: 0.04608466625213623 - -key: score_time -value: [0.02163911 0.02715111 0.03984332 0.02121711 0.02503157 0.01849675 - 0.02102757 0.01843333 0.02935457 0.02000165] - -mean value: 0.024219608306884764 - -key: test_mcc -value: [1. 0.9486833 1. 0.9486833 0.89473684 0.89973541 - 0.89473684 0.9486833 0.73099415 0.89736456] - -mean value: 0.9163617703675466 - -key: train_mcc -value: [1. 0.99413485 1. 1. 0.98830369 0.98830369 - 0.98823529 0.98830369 0.99415185 0.98833809] - -mean value: 0.9929771153408072 - -key: test_fscore -value: [1. 0.97297297 1. 0.97435897 0.94736842 0.94444444 - 0.94736842 0.97435897 0.86486486 0.94736842] - -mean value: 0.9573105494158126 - -key: train_fscore -value: [1. 0.99705015 1. 1. 0.99408284 0.99408284 - 0.99411765 0.99408284 0.99705015 0.99411765] - -mean value: 0.9964584109812957 - -key: test_precision -value: [1. 1. 1. 0.95 0.94736842 1. - 0.94736842 0.95 0.88888889 0.9 ] - -mean value: 0.9583625730994152 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.99411765 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: test_recall -value: [1. 0.94736842 1. 1. 0.94736842 0.89473684 - 0.94736842 1. 0.84210526 1. ] - -mean value: 0.9578947368421051 - -key: train_recall -value: [1. 0.99411765 1. 1. 0.98823529 0.98823529 - 0.99411765 0.98823529 0.99411765 0.98830409] - -mean value: 0.9935362917096663 - -key: test_accuracy -value: [1. 0.97368421 1. 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.86486486 0.94594595] - -mean value: 0.9573968705547653 - -key: train_accuracy -value: [1. 0.99705882 1. 1. 0.99411765 0.99411765 - 0.99411765 0.99411765 0.99706745 0.9941349 ] - -mean value: 0.9964731757805761 - -key: test_roc_auc -value: [1. 0.97368421 1. 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.86549708 0.94736842] - -mean value: 0.9576023391812866 - -key: train_roc_auc -value: [1. 0.99705882 1. 1. 0.99411765 0.99411765 - 0.99411765 0.99411765 0.99705882 0.99415205] - -mean value: 0.9964740282077743 - -key: test_jcc -value: [1. 0.94736842 1. 0.95 0.9 0.89473684 - 0.9 0.95 0.76190476 0.9 ] - -mean value: 0.9204010025062657 - -key: train_jcc -value: [1. 0.99411765 1. 1. 0.98823529 0.98823529 - 0.98830409 0.98823529 0.99411765 0.98830409] - -mean value: 0.992954936360509 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 181 - -mean value: 181.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.0540309 0.05687428 0.13293386 0.10850334 0.10855174 0.09396744 - 0.09760666 0.09473038 0.0959444 0.11276269] - -mean value: 0.09559056758880616 - -key: score_time -value: [0.01359248 0.01761127 0.02762008 0.02170277 0.02173662 0.02206397 - 0.0217979 0.02151656 0.02170968 0.02152681] - -mean value: 0.02108781337738037 - -key: test_mcc -value: [0.16151457 0.21320072 0.31622777 0.43643578 0.37686733 0.43643578 - 0.31980107 0.47633051 0.13450292 0.40780312] - -mean value: 0.3279119575559431 - -key: train_mcc -value: [0.92947609 0.9353103 0.91771057 0.92966915 0.92947609 0.92947609 - 0.93543979 0.9353103 0.92376759 0.92968454] - -mean value: 0.9295320507397296 - -key: test_fscore -value: [0.52941176 0.63414634 0.64864865 0.66666667 0.71428571 0.74418605 - 0.62857143 0.72222222 0.57894737 0.66666667] - -mean value: 0.6533752868163325 - -key: train_fscore -value: [0.96491228 0.96774194 0.95906433 0.96511628 0.96449704 0.96491228 - 0.96735905 0.96755162 0.96165192 0.96470588] - -mean value: 0.96475126174837 - -key: test_precision -value: [0.6 0.59090909 0.66666667 0.78571429 0.65217391 0.66666667 - 0.6875 0.76470588 0.57894737 0.73333333] - -mean value: 0.6726617207107515 - -key: train_precision -value: [0.95930233 0.96491228 0.95348837 0.95402299 0.9702381 0.95930233 - 0.9760479 0.9704142 0.96449704 0.9704142 ] - -mean value: 0.964263973568001 - -key: test_recall -value: [0.47368421 0.68421053 0.63157895 0.57894737 0.78947368 0.84210526 - 0.57894737 0.68421053 0.57894737 0.61111111] - -mean value: 0.6453216374269004 - -key: train_recall -value: [0.97058824 0.97058824 0.96470588 0.97647059 0.95882353 0.97058824 - 0.95882353 0.96470588 0.95882353 0.95906433] - -mean value: 0.9653181974544204 - -key: test_accuracy -value: [0.57894737 0.60526316 0.65789474 0.71052632 0.68421053 0.71052632 - 0.65789474 0.73684211 0.56756757 0.7027027 ] - -mean value: 0.6612375533428165 - -key: train_accuracy -value: [0.96470588 0.96764706 0.95882353 0.96470588 0.96470588 0.96470588 - 0.96764706 0.96764706 0.96187683 0.96480938] - -mean value: 0.9647274452302916 - -key: test_roc_auc -value: [0.57894737 0.60526316 0.65789474 0.71052632 0.68421053 0.71052632 - 0.65789474 0.73684211 0.56725146 0.7002924 ] - -mean value: 0.6609649122807018 - -key: train_roc_auc -value: [0.96470588 0.96764706 0.95882353 0.96470588 0.96470588 0.96470588 - 0.96764706 0.96764706 0.96186791 0.96482628] - -mean value: 0.9647282421740627 - -key: test_jcc -value: [0.36 0.46428571 0.48 0.5 0.55555556 0.59259259 - 0.45833333 0.56521739 0.40740741 0.5 ] - -mean value: 0.4883391994478951 - -key: train_jcc -value: [0.93220339 0.9375 0.92134831 0.93258427 0.93142857 0.93220339 - 0.93678161 0.93714286 0.92613636 0.93181818] - -mean value: 0.9319146947152056 - -key: TN -value: 128 - -mean value: 128.0 - -key: FP -value: 67 - -mean value: 67.0 - -key: FN -value: 61 - -mean value: 61.0 - -key: TP -value: 122 - -mean value: 122.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.68 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.56326413 0.54809475 0.54965138 0.55856133 0.55290723 0.54306698 - 0.55128074 0.54713964 0.54741049 0.54746938] - -mean value: 0.5508846044540405 - -key: score_time -value: [0.00924397 0.00917864 0.00928164 0.00914311 0.00922036 0.00939918 - 0.00979638 0.00922847 0.00914049 0.00922942] - -mean value: 0.009286165237426758 - -key: test_mcc -value: [1. 1. 0.9486833 0.9486833 0.89473684 0.89973541 - 0.89973541 0.9486833 0.78362573 0.89181287] - -mean value: 0.9215696154432906 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.97435897 0.97435897 0.94736842 0.94444444 - 0.95 0.97435897 0.89473684 0.94444444] - -mean value: 0.9604071075123708 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.95 0.95 0.94736842 1. - 0.9047619 0.95 0.89473684 0.94444444] - -mean value: 0.9541311612364245 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.89473684 - 1. 1. 0.89473684 0.94444444] - -mean value: 0.9681286549707602 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89189189 0.94594595] - -mean value: 0.960099573257468 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89181287 0.94590643] - -mean value: 0.9600877192982458 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.95 0.95 0.9 0.89473684 - 0.9047619 0.95 0.80952381 0.89473684] - -mean value: 0.9253759398496241 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 22 -Model_name: QDA -Model func: QuadraticDiscriminantAnalysis() - -Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02354813 0.02557588 0.02846742 0.02620196 0.02796721 0.02564716 - 0.02699709 0.0263803 0.04642153 0.0268445 ] - -mean value: 0.028405117988586425 - -key: score_time -value: [0.01284385 0.01259804 0.01268959 0.01371765 0.01261544 0.02071548 - 0.01390195 0.01384306 0.01386261 0.01391721] - -mean value: 0.014070487022399903 - -key: test_mcc -value: [0.37686733 0.26919095 0.05383819 0.45291081 0.63245553 0.26462806 - 0.21320072 0.47633051 0.19005848 0.59629297] - -mean value: 0.3525773554596864 - -key: train_mcc -value: [0.90992142 0.9262416 0.6638358 0.90453403 0.85714286 0.75767676 - 0.76249285 0.77216846 0.80198777 0.92101631] - -mean value: 0.8277017860373956 - -key: test_fscore -value: [0.64705882 0.58823529 0.47058824 0.64516129 0.81081081 0.61111111 - 0.57142857 0.72222222 0.59459459 0.73333333] - -mean value: 0.6394544286764401 - -key: train_fscore -value: [0.95061728 0.96024465 0.75912409 0.94736842 0.91719745 0.84353741 - 0.84745763 0.85521886 0.87788779 0.95731707] - -mean value: 0.8915970652394927 - -key: test_precision -value: [0.73333333 0.66666667 0.53333333 0.83333333 0.83333333 0.64705882 - 0.625 0.76470588 0.61111111 0.91666667] - -mean value: 0.716454248366013 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.57894737 0.52631579 0.42105263 0.52631579 0.78947368 0.57894737 - 0.52631579 0.68421053 0.57894737 0.61111111] - -mean value: 0.5821637426900585 - -key: train_recall -value: [0.90588235 0.92352941 0.61176471 0.9 0.84705882 0.72941176 - 0.73529412 0.74705882 0.78235294 0.91812865] - -mean value: 0.8100481596147231 - -key: test_accuracy -value: [0.68421053 0.63157895 0.52631579 0.71052632 0.81578947 0.63157895 - 0.60526316 0.73684211 0.59459459 0.78378378] - -mean value: 0.6720483641536272 - -key: train_accuracy -value: [0.95294118 0.96176471 0.80588235 0.95 0.92352941 0.86470588 - 0.86764706 0.87352941 0.8914956 0.95894428] - -mean value: 0.9050439882697946 - -key: test_roc_auc -value: [0.68421053 0.63157895 0.52631579 0.71052632 0.81578947 0.63157895 - 0.60526316 0.73684211 0.59502924 0.77923977] - -mean value: 0.6716374269005847 - -key: train_roc_auc -value: [0.95294118 0.96176471 0.80588235 0.95 0.92352941 0.86470588 - 0.86764706 0.87352941 0.89117647 0.95906433] - -mean value: 0.9050240798073614 - -key: test_jcc -value: [0.47826087 0.41666667 0.30769231 0.47619048 0.68181818 0.44 - 0.4 0.56521739 0.42307692 0.57894737] - -mean value: 0.47678701847351734 - -key: train_jcc -value: [0.90588235 0.92352941 0.61176471 0.9 0.84705882 0.72941176 - 0.73529412 0.74705882 0.78235294 0.91812865] - -mean value: 0.8100481596147231 - -key: TN -value: 140 - -mean value: 140.0 - -key: FP -value: 77 - -mean value: 77.0 - -key: FN -value: 49 - -mean value: 49.0 - -key: TP -value: 112 - -mean value: 112.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.35 - -Accuracy on Blind test: 0.67 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.01499367 0.01474309 0.01474953 0.03569078 0.03486943 0.01466131 - 0.01475739 0.03012466 0.03930235 0.03940415] - -mean value: 0.02532963752746582 - -key: score_time -value: [0.01208043 0.01203203 0.0120492 0.0219636 0.01202083 0.01196766 - 0.01203823 0.02358317 0.02308893 0.02078223] - -mean value: 0.01616063117980957 - -key: test_mcc -value: [0.85280287 0.85280287 0.76376262 0.9486833 0.73786479 0.74620251 - 0.84327404 0.84327404 0.47328975 0.89181287] - -mean value: 0.7953769642728366 - -key: train_mcc -value: [0.91304513 0.94176322 0.92482636 0.92482636 0.91821914 0.93044258 - 0.91821914 0.94222034 0.93626516 0.92454153] - -mean value: 0.9274368952395917 - -key: test_fscore -value: [0.92682927 0.91428571 0.84848485 0.97435897 0.86486486 0.85714286 - 0.91891892 0.91891892 0.70588235 0.94444444] - -mean value: 0.88741311626534 - -key: train_fscore -value: [0.95468278 0.97005988 0.96072508 0.96072508 0.95808383 0.96385542 - 0.95808383 0.96987952 0.96696697 0.96119403] - -mean value: 0.9624256412000524 - -key: test_precision -value: [0.86363636 1. 1. 0.95 0.88888889 0.9375 - 0.94444444 0.94444444 0.8 0.94444444] - -mean value: 0.9273358585858587 - -key: train_precision -value: [0.98136646 0.98780488 0.98757764 0.98757764 0.97560976 0.98765432 - 0.97560976 0.99382716 0.98773006 0.98170732] - -mean value: 0.9846464989278683 - -key: test_recall -value: [1. 0.84210526 0.73684211 1. 0.84210526 0.78947368 - 0.89473684 0.89473684 0.63157895 0.94444444] - -mean value: 0.8576023391812866 - -key: train_recall -value: [0.92941176 0.95294118 0.93529412 0.93529412 0.94117647 0.94117647 - 0.94117647 0.94705882 0.94705882 0.94152047] - -mean value: 0.9412108703130375 - -key: test_accuracy -value: [0.92105263 0.92105263 0.86842105 0.97368421 0.86842105 0.86842105 - 0.92105263 0.92105263 0.72972973 0.94594595] - -mean value: 0.8938833570412518 - -key: train_accuracy -value: [0.95588235 0.97058824 0.96176471 0.96176471 0.95882353 0.96470588 - 0.95882353 0.97058824 0.96774194 0.96187683] - -mean value: 0.9632559944799034 - -key: test_roc_auc -value: [0.92105263 0.92105263 0.86842105 0.97368421 0.86842105 0.86842105 - 0.92105263 0.92105263 0.73245614 0.94590643] - -mean value: 0.8941520467836257 - -key: train_roc_auc -value: [0.95588235 0.97058824 0.96176471 0.96176471 0.95882353 0.96470588 - 0.95882353 0.97058824 0.96768146 0.9619367 ] - -mean value: 0.9632559339525283 - -key: test_jcc -value: [0.86363636 0.84210526 0.73684211 0.95 0.76190476 0.75 - 0.85 0.85 0.54545455 0.89473684] - -mean value: 0.8044679881521987 - -key: train_jcc -value: [0.9132948 0.94186047 0.9244186 0.9244186 0.91954023 0.93023256 - 0.91954023 0.94152047 0.93604651 0.92528736] - -mean value: 0.9276159825802118 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 162 - -mean value: 162.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: 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 - smnc_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: 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 - smnc_CV['Resampling'] = rs_smnc -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: 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 - smnc_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: 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 - smnc_BT['Resampling'] = rs_smnc -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.24734879 0.15380907 0.22762704 0.14868951 0.28221512 0.36522841 - 0.42178607 0.40161061 0.28867221 0.24695277] - -mean value: 0.27839395999908445 - -key: score_time -value: [0.02241445 0.01206136 0.0240612 0.01210141 0.02428198 0.02394986 - 0.02357435 0.02378106 0.02274966 0.01356101] - -mean value: 0.020253634452819823 - -key: test_mcc -value: [0.85280287 0.73786479 0.76376262 0.9486833 0.73786479 0.65465367 - 0.84327404 0.84327404 0.47328975 0.89181287] - -mean value: 0.7747282728141887 - -key: train_mcc -value: [0.93569892 0.92966915 0.92482636 0.92482636 0.91821914 0.95320508 - 0.91821914 0.94222034 0.93626516 0.92454153] - -mean value: 0.9307691162999621 - -key: test_fscore -value: [0.92682927 0.86486486 0.84848485 0.97435897 0.86486486 0.78787879 - 0.91891892 0.91891892 0.70588235 0.94444444] - -mean value: 0.8755446243968482 - -key: train_fscore -value: [0.96716418 0.96428571 0.96072508 0.96072508 0.95808383 0.97619048 - 0.95808383 0.96987952 0.96696697 0.96119403] - -mean value: 0.964329870019873 - -key: test_precision -value: [0.86363636 0.88888889 1. 0.95 0.88888889 0.92857143 - 0.94444444 0.94444444 0.8 0.94444444] - -mean value: 0.9153318903318904 - -key: train_precision -value: [0.98181818 0.97590361 0.98757764 0.98757764 0.97560976 0.98795181 - 0.97560976 0.99382716 0.98773006 0.98170732] - -mean value: 0.9835312934119846 - -key: test_recall -value: [1. 0.84210526 0.73684211 1. 0.84210526 0.68421053 - 0.89473684 0.89473684 0.63157895 0.94444444] - -mean value: 0.847076023391813 - -key: train_recall -value: [0.95294118 0.95294118 0.93529412 0.93529412 0.94117647 0.96470588 - 0.94117647 0.94705882 0.94705882 0.94152047] - -mean value: 0.9459167526659786 - -key: test_accuracy -value: [0.92105263 0.86842105 0.86842105 0.97368421 0.86842105 0.81578947 - 0.92105263 0.92105263 0.72972973 0.94594595] - -mean value: 0.8833570412517782 - -key: train_accuracy -value: [0.96764706 0.96470588 0.96176471 0.96176471 0.95882353 0.97647059 - 0.95882353 0.97058824 0.96774194 0.96187683] - -mean value: 0.9650207003622562 - -key: test_roc_auc -value: [0.92105263 0.86842105 0.86842105 0.97368421 0.86842105 0.81578947 - 0.92105263 0.92105263 0.73245614 0.94590643] - -mean value: 0.8836257309941521 - -key: train_roc_auc -value: [0.96764706 0.96470588 0.96176471 0.96176471 0.95882353 0.97647059 - 0.95882353 0.97058824 0.96768146 0.9619367 ] - -mean value: 0.9650206398348814 - -key: test_jcc -value: [0.86363636 0.76190476 0.73684211 0.95 0.76190476 0.65 - 0.85 0.85 0.54545455 0.89473684] - -mean value: 0.7864479380268853 - -key: train_jcc -value: [0.93641618 0.93103448 0.9244186 0.9244186 0.91954023 0.95348837 - 0.91954023 0.94152047 0.93604651 0.92528736] - -mean value: 0.9311711044681186 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 29 - -mean value: 29.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 160 - -mean value: 160.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.05719495 0.07996392 0.03658104 0.07506204 0.037328 0.03613162 - 0.03498363 0.07007432 0.05941772 0.05326796] - -mean value: 0.05400052070617676 - -key: score_time -value: [0.01328087 0.01338768 0.01360393 0.01335931 0.01322341 0.0133729 - 0.01312041 0.01294351 0.01329732 0.01339221] - -mean value: 0.0132981538772583 - -key: test_mcc -value: [0.84327404 0.85280287 0.58218174 0.85280287 0.63245553 0.68803296 - 0.74620251 0.69989647 0.51461988 0.73020842] - -mean value: 0.714247728465443 - -key: train_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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.8479983 0.8472934 0.84154054 0.86031325 0.85366518 0.87113098 - 0.85413899 0.85935894 0.89466626 0.8663298 ] - -mean value: 0.8596435637317539 - -key: test_fscore -value: [0.92307692 0.91428571 0.77777778 0.91428571 0.81081081 0.85 - 0.85714286 0.85714286 0.75675676 0.85714286] - -mean value: 0.8518422268422269 - -key: train_fscore -value: [0.92168675 0.92261905 0.91940299 0.92727273 0.92492492 0.93413174 - 0.9244713 0.92814371 0.94642857 0.93093093] - -mean value: 0.9280012682434233 - -key: test_precision -value: [0.9 1. 0.82352941 1. 0.83333333 0.80952381 - 0.9375 0.7826087 0.77777778 0.88235294] - -mean value: 0.8746625969228271 - -key: train_precision -value: [0.94444444 0.93373494 0.93333333 0.95625 0.94478528 0.95121951 - 0.95031056 0.94512195 0.95783133 0.95679012] - -mean value: 0.9473821464789273 - -key: test_recall -value: [0.94736842 0.84210526 0.73684211 0.84210526 0.78947368 0.89473684 - 0.78947368 0.94736842 0.73684211 0.83333333] - -mean value: 0.8359649122807017 - -key: train_recall -value: [0.9 0.91176471 0.90588235 0.9 0.90588235 0.91764706 - 0.9 0.91176471 0.93529412 0.90643275] - -mean value: 0.9094668042655659 - -key: test_accuracy -value: [0.92105263 0.92105263 0.78947368 0.92105263 0.81578947 0.84210526 - 0.86842105 0.84210526 0.75675676 0.86486486] - -mean value: 0.854267425320057 - -key: train_accuracy -value: [0.92352941 0.92352941 0.92058824 0.92941176 0.92647059 0.93529412 - 0.92647059 0.92941176 0.94721408 0.93255132] - -mean value: 0.9294471278247369 - -key: test_roc_auc -value: [0.92105263 0.92105263 0.78947368 0.92105263 0.81578947 0.84210526 - 0.86842105 0.84210526 0.75730994 0.86403509] - -mean value: 0.8542397660818712 - -key: train_roc_auc -value: [0.92352941 0.92352941 0.92058824 0.92941176 0.92647059 0.93529412 - 0.92647059 0.92941176 0.94717922 0.93262814] - -mean value: 0.9294513243894048 - -key: test_jcc -value: [0.85714286 0.84210526 0.63636364 0.84210526 0.68181818 0.73913043 - 0.75 0.75 0.60869565 0.75 ] - -mean value: 0.7457361288596986 - -key: train_jcc -value: [0.8547486 0.85635359 0.85082873 0.86440678 0.8603352 0.87640449 - 0.85955056 0.86592179 0.89830508 0.87078652] - -mean value: 0.8657641344474657 - -key: TN -value: 165 - -mean value: 165.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 158 - -mean value: 158.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.83 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.81735826 0.9594543 1.00105953 0.75543022 0.93429232 1.04947948 - 0.78688312 0.86082625 0.76451874 0.93615556] - -mean value: 0.8865457773208618 - -key: score_time -value: [0.01341653 0.01339769 0.01345968 0.01326776 0.01332211 0.01216149 - 0.01333976 0.01460004 0.01330733 0.01222801] - -mean value: 0.013250041007995605 - -key: test_mcc -value: [0.89973541 0.9486833 0.78947368 0.9486833 0.79388419 0.79388419 - 0.78947368 0.74620251 0.68035483 0.83918129] - -mean value: 0.8229556372875612 - -key: train_mcc -value: [0.98823529 0.9707394 0.99413485 0.95897286 0.9707394 1. - 0.97653817 0.9707394 0.98826969 0.97680982] - -mean value: 0.9795178888527627 - -key: test_fscore -value: [0.95 0.97297297 0.89473684 0.97297297 0.88888889 0.88888889 - 0.89473684 0.87804878 0.83333333 0.91891892] - -mean value: 0.9093498440674308 - -key: train_fscore -value: [0.99411765 0.9851632 0.99706745 0.97922849 0.9851632 1. - 0.98816568 0.9851632 0.99411765 0.98816568] - -mean value: 0.9896352204634951 - -key: test_precision -value: [0.9047619 1. 0.89473684 1. 0.94117647 0.94117647 - 0.89473684 0.81818182 0.88235294 0.89473684] - -mean value: 0.9171860131612455 - -key: train_precision -value: [0.99411765 0.99401198 0.99415205 0.98802395 0.99401198 1. - 0.99404762 0.99401198 0.99411765 1. ] - -mean value: 0.9946494840188412 - -key: test_recall -value: [1. 0.94736842 0.89473684 0.94736842 0.84210526 0.84210526 - 0.89473684 0.94736842 0.78947368 0.94444444] - -mean value: 0.9049707602339181 - -key: train_recall -value: [0.99411765 0.97647059 1. 0.97058824 0.97647059 1. - 0.98235294 0.97647059 0.99411765 0.97660819] - -mean value: 0.984719642242862 - -key: test_accuracy -value: [0.94736842 0.97368421 0.89473684 0.97368421 0.89473684 0.89473684 - 0.89473684 0.86842105 0.83783784 0.91891892] - -mean value: 0.9098862019914652 - -key: train_accuracy -value: [0.99411765 0.98529412 0.99705882 0.97941176 0.98529412 1. - 0.98823529 0.98529412 0.9941349 0.98826979] - -mean value: 0.9897110574435054 - -key: test_roc_auc -value: [0.94736842 0.97368421 0.89473684 0.97368421 0.89473684 0.89473684 - 0.89473684 0.86842105 0.83918129 0.91959064] - -mean value: 0.9100877192982455 - -key: train_roc_auc -value: [0.99411765 0.98529412 0.99705882 0.97941176 0.98529412 1. - 0.98823529 0.98529412 0.99413485 0.98830409] - -mean value: 0.9897144822841417 - -key: test_jcc -value: [0.9047619 0.94736842 0.80952381 0.94736842 0.8 0.8 - 0.80952381 0.7826087 0.71428571 0.85 ] - -mean value: 0.8365440775852676 - -key: train_jcc -value: [0.98830409 0.97076023 0.99415205 0.95930233 0.97076023 1. - 0.97660819 0.97076023 0.98830409 0.97660819] - -mean value: 0.9795559635522915 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 18 - -mean value: 18.0 - -key: FN -value: 15 - -mean value: 15.0 - -key: TP -value: 171 - -mean value: 171.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01303005 0.01295352 0.00977802 0.00942612 0.00963712 0.01047492 - 0.01054525 0.01025343 0.01000714 0.00925016] - -mean value: 0.010535573959350586 - -key: score_time -value: [0.01185632 0.01058698 0.0089972 0.00878072 0.00879598 0.00952196 - 0.00945115 0.00978446 0.00985861 0.00873685] - -mean value: 0.009637022018432617 - -key: test_mcc -value: [0.52704628 0.31980107 0.49923018 0.58218174 0.26315789 0.26919095 - 0.31980107 0.52704628 0.35087719 0.57857577] - -mean value: 0.4236908421333621 - -key: train_mcc -value: [0.44755466 0.44708977 0.45643707 0.42497295 0.42592367 0.48572608 - 0.46086929 0.47068023 0.44311707 0.41402127] - -mean value: 0.4476392053223924 - -key: test_fscore -value: [0.75675676 0.62857143 0.6875 0.77777778 0.63157895 0.66666667 - 0.62857143 0.76923077 0.68421053 0.75 ] - -mean value: 0.6980864301259038 - -key: train_fscore -value: [0.71686747 0.72189349 0.70846395 0.6993865 0.69565217 0.725 - 0.71604938 0.70926518 0.71471471 0.7005988 ] - -mean value: 0.710789166337239 - -key: test_precision -value: [0.77777778 0.6875 0.84615385 0.82352941 0.63157895 0.60869565 - 0.6875 0.75 0.68421053 0.85714286] - -mean value: 0.735408901869731 - -key: train_precision -value: [0.7345679 0.72619048 0.75838926 0.73076923 0.73684211 0.77333333 - 0.75324675 0.77622378 0.73006135 0.71779141] - -mean value: 0.7437415598742458 - -key: test_recall -value: [0.73684211 0.57894737 0.57894737 0.73684211 0.63157895 0.73684211 - 0.57894737 0.78947368 0.68421053 0.66666667] - -mean value: 0.6719298245614035 - -key: train_recall -value: [0.7 0.71764706 0.66470588 0.67058824 0.65882353 0.68235294 - 0.68235294 0.65294118 0.7 0.68421053] - -mean value: 0.6813622291021673 - -key: test_accuracy -value: [0.76315789 0.65789474 0.73684211 0.78947368 0.63157895 0.63157895 - 0.65789474 0.76315789 0.67567568 0.78378378] - -mean value: 0.7091038406827881 - -key: train_accuracy -value: [0.72352941 0.72352941 0.72647059 0.71176471 0.71176471 0.74117647 - 0.72941176 0.73235294 0.72140762 0.70674487] - -mean value: 0.7228152492668622 - -key: test_roc_auc -value: [0.76315789 0.65789474 0.73684211 0.78947368 0.63157895 0.63157895 - 0.65789474 0.76315789 0.6754386 0.78070175] - -mean value: 0.7087719298245614 - -key: train_roc_auc -value: [0.72352941 0.72352941 0.72647059 0.71176471 0.71176471 0.74117647 - 0.72941176 0.73235294 0.72134503 0.70681115] - -mean value: 0.7228156174750602 - -key: test_jcc -value: [0.60869565 0.45833333 0.52380952 0.63636364 0.46153846 0.5 - 0.45833333 0.625 0.52 0.6 ] - -mean value: 0.5392073940552201 - -key: train_jcc -value: [0.55868545 0.56481481 0.54854369 0.53773585 0.53333333 0.56862745 - 0.55769231 0.54950495 0.55607477 0.53917051] - -mean value: 0.5514183114969862 - -key: TN -value: 141 - -mean value: 141.0 - -key: FP -value: 62 - -mean value: 62.0 - -key: FN -value: 48 - -mean value: 48.0 - -key: TP -value: 127 - -mean value: 127.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.0097084 0.01043057 0.01066208 0.01132846 0.01015615 0.01087976 - 0.01024461 0.01024652 0.01061416 0.010427 ] - -mean value: 0.010469770431518555 - -key: score_time -value: [0.00907683 0.00947785 0.0094254 0.00972223 0.00913501 0.00986004 - 0.00980425 0.00945067 0.00945234 0.00965858] - -mean value: 0.009506320953369141 - -key: test_mcc -value: [ 0.10540926 -0.21821789 0.16151457 0.21320072 0.15877684 0.26919095 - -0.10540926 0.31980107 0.02359974 0.29618896] - -mean value: 0.12240549605657833 - -key: train_mcc -value: [0.31241942 0.3322053 0.29430101 0.31773503 0.24712724 0.27755314 - 0.29542903 0.28824028 0.29622622 0.27346978] - -mean value: 0.2934706439382306 - -key: test_fscore -value: [0.54054054 0.46511628 0.52941176 0.57142857 0.55555556 0.66666667 - 0.43243243 0.68292683 0.55 0.62857143] - -mean value: 0.5622650068239138 - -key: train_fscore -value: [0.66666667 0.68508287 0.64071856 0.6547619 0.62790698 0.62153846 - 0.66292135 0.64306785 0.64912281 0.65168539] - -mean value: 0.6503472840711895 - -key: test_precision -value: [0.55555556 0.41666667 0.6 0.625 0.58823529 0.60869565 - 0.44444444 0.63636364 0.52380952 0.64705882] - -mean value: 0.5645829596660799 - -key: train_precision -value: [0.64640884 0.64583333 0.65243902 0.6626506 0.62068966 0.6516129 - 0.6344086 0.64497041 0.64534884 0.62702703] - -mean value: 0.6431389238898492 - -key: test_recall -value: [0.52631579 0.52631579 0.47368421 0.52631579 0.52631579 0.73684211 - 0.42105263 0.73684211 0.57894737 0.61111111] - -mean value: 0.5663742690058479 - -key: train_recall -value: [0.68823529 0.72941176 0.62941176 0.64705882 0.63529412 0.59411765 - 0.69411765 0.64117647 0.65294118 0.67836257] - -mean value: 0.6590127278981768 - -key: test_accuracy -value: [0.55263158 0.39473684 0.57894737 0.60526316 0.57894737 0.63157895 - 0.44736842 0.65789474 0.51351351 0.64864865] - -mean value: 0.5609530583214793 - -key: train_accuracy -value: [0.65588235 0.66470588 0.64705882 0.65882353 0.62352941 0.63823529 - 0.64705882 0.64411765 0.64809384 0.63636364] - -mean value: 0.6463869242711748 - -key: test_roc_auc -value: [0.55263158 0.39473684 0.57894737 0.60526316 0.57894737 0.63157895 - 0.44736842 0.65789474 0.51169591 0.64766082] - -mean value: 0.5606725146198831 - -key: train_roc_auc -value: [0.65588235 0.66470588 0.64705882 0.65882353 0.62352941 0.63823529 - 0.64705882 0.64411765 0.64810802 0.63624011] - -mean value: 0.646375988992088 - -key: test_jcc -value: [0.37037037 0.3030303 0.36 0.4 0.38461538 0.5 - 0.27586207 0.51851852 0.37931034 0.45833333] - -mean value: 0.3950040323661014 - -key: train_jcc -value: [0.5 0.5210084 0.47136564 0.48672566 0.45762712 0.45089286 - 0.49579832 0.47391304 0.48051948 0.48333333] - -mean value: 0.4821183858290409 - -key: TN -value: 105 - -mean value: 105.0 - -key: FP -value: 82 - -mean value: 82.0 - -key: FN -value: 84 - -mean value: 84.0 - -key: TP -value: 107 - -mean value: 107.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00879264 0.01016665 0.00998855 0.00992393 0.01000118 0.01004314 - 0.0100081 0.00987816 0.01006055 0.00884986] - -mean value: 0.009771275520324706 - -key: score_time -value: [0.01509356 0.01208496 0.01184869 0.01216817 0.01213288 0.01348615 - 0.01218462 0.01252317 0.01679111 0.01169109] - -mean value: 0.01300044059753418 - -key: test_mcc -value: [0.21821789 0.10540926 0.26462806 0.21320072 0.2773501 0.47633051 - 0.15877684 0.15877684 0.19005848 0.24975622] - -mean value: 0.23125049040470383 - -key: train_mcc -value: [0.50588235 0.52944841 0.5119862 0.46477826 0.47687503 0.49425448 - 0.45885529 0.47062081 0.49561359 0.42002512] - -mean value: 0.48283395301515936 - -key: test_fscore -value: [0.54545455 0.54054054 0.61111111 0.57142857 0.68181818 0.75 - 0.55555556 0.55555556 0.59459459 0.53333333] - -mean value: 0.5939391989391989 - -key: train_fscore -value: [0.75294118 0.76331361 0.75942029 0.72997033 0.73273273 0.74404762 - 0.72781065 0.73372781 0.74556213 0.7027027 ] - -mean value: 0.7392229048401642 - -key: test_precision -value: [0.64285714 0.55555556 0.64705882 0.625 0.6 0.71428571 - 0.58823529 0.58823529 0.61111111 0.66666667] - -mean value: 0.6239005602240897 - -key: train_precision -value: [0.75294118 0.76785714 0.74857143 0.73652695 0.74846626 0.75301205 - 0.73214286 0.73809524 0.75 0.72222222] - -mean value: 0.7449835317328745 - -key: test_recall -value: [0.47368421 0.52631579 0.57894737 0.52631579 0.78947368 0.78947368 - 0.52631579 0.52631579 0.57894737 0.44444444] - -mean value: 0.5760233918128655 - -key: train_recall -value: [0.75294118 0.75882353 0.77058824 0.72352941 0.71764706 0.73529412 - 0.72352941 0.72941176 0.74117647 0.68421053] - -mean value: 0.7337151702786378 - -key: test_accuracy -value: [0.60526316 0.55263158 0.63157895 0.60526316 0.63157895 0.73684211 - 0.57894737 0.57894737 0.59459459 0.62162162] - -mean value: 0.6137268847795163 - -key: train_accuracy -value: [0.75294118 0.76470588 0.75588235 0.73235294 0.73823529 0.74705882 - 0.72941176 0.73529412 0.74780059 0.70967742] - -mean value: 0.741336035880628 - -key: test_roc_auc -value: [0.60526316 0.55263158 0.63157895 0.60526316 0.63157895 0.73684211 - 0.57894737 0.57894737 0.59502924 0.61695906] - -mean value: 0.6133040935672514 - -key: train_roc_auc -value: [0.75294118 0.76470588 0.75588235 0.73235294 0.73823529 0.74705882 - 0.72941176 0.73529412 0.74778122 0.70975232] - -mean value: 0.7413415892672859 - -key: test_jcc -value: [0.375 0.37037037 0.44 0.4 0.51724138 0.6 - 0.38461538 0.38461538 0.42307692 0.36363636] - -mean value: 0.4258555805624771 - -key: train_jcc -value: [0.60377358 0.61722488 0.61214953 0.57476636 0.57819905 0.59241706 - 0.57209302 0.57943925 0.59433962 0.54166667] - -mean value: 0.5866069031783417 - -key: TN -value: 123 - -mean value: 123.0 - -key: FP -value: 80 - -mean value: 80.0 - -key: FN -value: 66 - -mean value: 66.0 - -key: TP -value: 109 - -mean value: 109.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.63 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.01992965 0.01708341 0.01713109 0.01723742 0.01700902 0.0162406 - 0.02007413 0.01609588 0.01625776 0.01613426] - -mean value: 0.017319321632385254 - -key: score_time -value: [0.01093984 0.01163554 0.01074052 0.01122308 0.01202011 0.01173377 - 0.01169991 0.01074719 0.01065326 0.01083255] - -mean value: 0.011222577095031739 - -key: test_mcc -value: [0.53300179 0.58218174 0.52704628 0.68421053 0.47368421 0.4061812 - 0.36842105 0.58218174 0.35087719 0.62170355] - -mean value: 0.5129489273176822 - -key: train_mcc -value: [0.68235294 0.71177702 0.75915195 0.72414357 0.71177702 0.71769673 - 0.64723801 0.72414357 0.77157126 0.74278665] - -mean value: 0.7192638724935875 - -key: test_fscore -value: [0.74285714 0.77777778 0.75675676 0.84210526 0.73684211 0.73913043 - 0.68421053 0.77777778 0.68421053 0.8 ] - -mean value: 0.7541668311004696 - -key: train_fscore -value: [0.84117647 0.85630499 0.87761194 0.85885886 0.85545723 0.85798817 - 0.82142857 0.85885886 0.88358209 0.86826347] - -mean value: 0.8579530640795523 - -key: test_precision -value: [0.8125 0.82352941 0.77777778 0.84210526 0.73684211 0.62962963 - 0.68421053 0.82352941 0.68421053 0.82352941] - -mean value: 0.7637864063754156 - -key: train_precision -value: [0.84117647 0.85380117 0.89090909 0.87730061 0.85798817 0.86309524 - 0.8313253 0.87730061 0.8969697 0.88957055] - -mean value: 0.8679436912179301 - -key: test_recall -value: [0.68421053 0.73684211 0.73684211 0.84210526 0.73684211 0.89473684 - 0.68421053 0.73684211 0.68421053 0.77777778] - -mean value: 0.7514619883040935 - -key: train_recall -value: [0.84117647 0.85882353 0.86470588 0.84117647 0.85294118 0.85294118 - 0.81176471 0.84117647 0.87058824 0.84795322] - -mean value: 0.8483247334021327 - -key: test_accuracy -value: [0.76315789 0.78947368 0.76315789 0.84210526 0.73684211 0.68421053 - 0.68421053 0.78947368 0.67567568 0.81081081] - -mean value: 0.7539118065433854 - -key: train_accuracy -value: [0.84117647 0.85588235 0.87941176 0.86176471 0.85588235 0.85882353 - 0.82352941 0.86176471 0.8856305 0.87096774] - -mean value: 0.8594833534586854 - -key: test_roc_auc -value: [0.76315789 0.78947368 0.76315789 0.84210526 0.73684211 0.68421053 - 0.68421053 0.78947368 0.6754386 0.80994152] - -mean value: 0.7538011695906432 - -key: train_roc_auc -value: [0.84117647 0.85588235 0.87941176 0.86176471 0.85588235 0.85882353 - 0.82352941 0.86176471 0.88558652 0.87103543] - -mean value: 0.8594857241142071 - -key: test_jcc -value: [0.59090909 0.63636364 0.60869565 0.72727273 0.58333333 0.5862069 - 0.52 0.63636364 0.52 0.66666667] - -mean value: 0.6075811639634728 - -key: train_jcc -value: [0.72588832 0.74871795 0.78191489 0.75263158 0.74742268 0.75129534 - 0.6969697 0.75263158 0.79144385 0.76719577] - -mean value: 0.7516111656735583 - -key: TN -value: 143 - -mean value: 143.0 - -key: FP -value: 47 - -mean value: 47.0 - -key: FN -value: 46 - -mean value: 46.0 - -key: TP -value: 142 - -mean value: 142.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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))])/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - - -key: fit_time -value: [1.67368555 1.53090692 1.27422905 1.44410563 1.65796185 1.88773346 - 1.5465126 1.52885222 1.83342266 1.6072557 ] - -mean value: 1.5984665632247925 - -key: score_time -value: [0.01557517 0.01500607 0.01368165 0.01852751 0.01508594 0.01264787 - 0.01411867 0.01251173 0.01386857 0.02397037] - -mean value: 0.015499353408813477 - -key: test_mcc -value: [0.73786479 0.68421053 0.63245553 0.89973541 0.73786479 0.78947368 - 0.79388419 0.84327404 0.51461988 0.68035483] - -mean value: 0.7313737671579481 - -key: train_mcc -value: [1. 1. 1. 0.99413485 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994134846772434 - -key: test_fscore -value: [0.87179487 0.84210526 0.82051282 0.94444444 0.86486486 0.89473684 - 0.9 0.92307692 0.75675676 0.84210526] - -mean value: 0.8660398049871734 - -key: train_fscore -value: [1. 1. 1. 0.99705015 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997050147492625 - -key: test_precision -value: [0.85 0.84210526 0.8 1. 0.88888889 0.89473684 - 0.85714286 0.9 0.77777778 0.8 ] - -mean value: 0.8610651629072683 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 0.84210526 0.84210526 0.89473684 0.84210526 0.89473684 - 0.94736842 0.94736842 0.73684211 0.88888889] - -mean value: 0.8730994152046783 - -key: train_recall -value: [1. 1. 1. 0.99411765 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: test_accuracy -value: [0.86842105 0.84210526 0.81578947 0.94736842 0.86842105 0.89473684 - 0.89473684 0.92105263 0.75675676 0.83783784] - -mean value: 0.8647226173541963 - -key: train_accuracy -value: [1. 1. 1. 0.99705882 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997058823529411 - -key: test_roc_auc -value: [0.86842105 0.84210526 0.81578947 0.94736842 0.86842105 0.89473684 - 0.89473684 0.92105263 0.75730994 0.83918129] - -mean value: 0.8649122807017544 - -key: train_roc_auc -value: [1. 1. 1. 0.99705882 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9997058823529411 - -key: test_jcc -value: [0.77272727 0.72727273 0.69565217 0.89473684 0.76190476 0.80952381 - 0.81818182 0.85714286 0.60869565 0.72727273] - -mean value: 0.7673110642218195 - -key: train_jcc -value: [1. 1. 1. 0.99411765 1. 1. - 1. 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: TN -value: 162 - -mean value: 162.0 - -key: FP -value: 24 - -mean value: 24.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 165 - -mean value: 165.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.79 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02371454 0.01926661 0.0162046 0.01766229 0.01725888 0.01582241 - 0.01577902 0.01603985 0.01573443 0.01620889] - -mean value: 0.01736915111541748 - -key: score_time -value: [0.0121851 0.00931263 0.00895357 0.00918937 0.00908518 0.0088377 - 0.00882578 0.00883746 0.00910497 0.00912642] - -mean value: 0.009345817565917968 - -key: test_mcc -value: [1. 0.9486833 0.84327404 0.89473684 0.9486833 0.89973541 - 0.84327404 0.84327404 0.78362573 0.89181287] - -mean value: 0.8897099573674658 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.97297297 0.91891892 0.94736842 0.97297297 0.94444444 - 0.92307692 0.92307692 0.89473684 0.94444444] - -mean value: 0.9442012863065494 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.94444444 0.94736842 1. 1. - 0.9 0.9 0.89473684 0.94444444] - -mean value: 0.9530994152046783 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94736842 0.89473684 0.94736842 0.94736842 0.89473684 - 0.94736842 0.94736842 0.89473684 0.94444444] - -mean value: 0.9365497076023391 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.97368421 0.92105263 0.94736842 0.97368421 0.94736842 - 0.92105263 0.92105263 0.89189189 0.94594595] - -mean value: 0.9443100995732573 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 0.97368421 0.92105263 0.94736842 0.97368421 0.94736842 - 0.92105263 0.92105263 0.89181287 0.94590643] - -mean value: 0.9442982456140351 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.94736842 0.85 0.9 0.94736842 0.89473684 - 0.85714286 0.85714286 0.80952381 0.89473684] - -mean value: 0.8958020050125315 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 177 - -mean value: 177.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.88 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.11310196 0.11431193 0.11325431 0.11411405 0.11202741 0.11501527 - 0.1152451 0.1139257 0.11364961 0.11417913] - -mean value: 0.1138824462890625 - -key: score_time -value: [0.01830697 0.01796031 0.01864362 0.0176723 0.01779985 0.01770353 - 0.01759005 0.01766253 0.0177803 0.01766658] - -mean value: 0.0178786039352417 - -key: test_mcc -value: [0.78947368 0.69989647 0.31622777 0.68803296 0.68421053 0.47633051 - 0.78947368 0.57894737 0.45906433 0.69007214] - -mean value: 0.6171729441074092 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.89473684 0.82352941 0.64864865 0.83333333 0.84210526 0.75 - 0.89473684 0.78947368 0.73684211 0.8125 ] - -mean value: 0.8025906130588792 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.89473684 0.93333333 0.66666667 0.88235294 0.84210526 0.71428571 - 0.89473684 0.78947368 0.73684211 0.92857143] - -mean value: 0.8283104820875717 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 0.73684211 0.63157895 0.78947368 0.84210526 0.78947368 - 0.89473684 0.78947368 0.73684211 0.72222222] - -mean value: 0.7827485380116959 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.89473684 0.84210526 0.65789474 0.84210526 0.84210526 0.73684211 - 0.89473684 0.78947368 0.72972973 0.83783784] - -mean value: 0.8067567567567568 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.89473684 0.84210526 0.65789474 0.84210526 0.84210526 0.73684211 - 0.89473684 0.78947368 0.72953216 0.83479532] - -mean value: 0.8064327485380117 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.80952381 0.7 0.48 0.71428571 0.72727273 0.6 - 0.80952381 0.65217391 0.58333333 0.68421053] - -mean value: 0.676032383329866 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 157 - -mean value: 157.0 - -key: FP -value: 41 - -mean value: 41.0 - -key: FN -value: 32 - -mean value: 32.0 - -key: TP -value: 148 - -mean value: 148.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.79 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.00948119 0.00948858 0.00939918 0.00939822 0.00950146 0.00964284 - 0.00946665 0.00949311 0.00962114 0.00975776] - -mean value: 0.009525012969970704 - -key: score_time -value: [0.00867772 0.00868273 0.0086689 0.00856876 0.00861859 0.00857091 - 0.00878644 0.00872993 0.00874972 0.00856972] - -mean value: 0.00866234302520752 - -key: test_mcc -value: [ 0.31622777 0.21081851 0.26462806 0.47633051 0.15877684 0.43328912 - 0.21320072 0.37047929 -0.03673592 0.24975622] - -mean value: 0.26567711116266257 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.66666667 0.61538462 0.61111111 0.72222222 0.6 0.75 - 0.57142857 0.7 0.55813953 0.53333333] - -mean value: 0.6328286055030241 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.65 0.6 0.64705882 0.76470588 0.57142857 0.62068966 - 0.625 0.66666667 0.5 0.66666667] - -mean value: 0.6312216265816673 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.68421053 0.63157895 0.57894737 0.68421053 0.63157895 0.94736842 - 0.52631579 0.73684211 0.63157895 0.44444444] - -mean value: 0.6497076023391812 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.65789474 0.60526316 0.63157895 0.73684211 0.57894737 0.68421053 - 0.60526316 0.68421053 0.48648649 0.62162162] - -mean value: 0.6292318634423898 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.65789474 0.60526316 0.63157895 0.73684211 0.57894737 0.68421053 - 0.60526316 0.68421053 0.48245614 0.61695906] - -mean value: 0.6283625730994152 - -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 0.44444444 0.44 0.56521739 0.42857143 0.6 - 0.4 0.53846154 0.38709677 0.36363636] - -mean value: 0.46674279406116703 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 115 - -mean value: 115.0 - -key: FP -value: 66 - -mean value: 66.0 - -key: FN -value: 74 - -mean value: 74.0 - -key: TP -value: 123 - -mean value: 123.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.6 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.54987144 1.56477642 1.60606122 1.5650034 1.55390358 1.52415061 - 1.54107833 1.53467798 1.51634812 1.54079008] - -mean value: 1.5496661186218261 - -key: score_time -value: [0.09949994 0.09195542 0.09117413 0.09948826 0.09089994 0.09789157 - 0.09298134 0.090868 0.09159446 0.09081507] - -mean value: 0.09371681213378906 - -key: test_mcc -value: [1. 0.80757285 0.73786479 0.89473684 0.78947368 0.89973541 - 0.9486833 0.89973541 0.67849265 0.89181287] - -mean value: 0.8548107797187713 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 0.88235294 0.86486486 0.94736842 0.89473684 0.94444444 - 0.97297297 0.95 0.85 0.94444444] - -mean value: 0.9251184931061092 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.88888889 0.94736842 0.89473684 1. - 1. 0.9047619 0.80952381 0.94444444] - -mean value: 0.9389724310776943 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.78947368 0.84210526 0.94736842 0.89473684 0.89473684 - 0.94736842 1. 0.89473684 0.94444444] - -mean value: 0.9154970760233917 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 0.89473684 0.86842105 0.94736842 0.89473684 0.94736842 - 0.97368421 0.94736842 0.83783784 0.94594595] - -mean value: 0.9257467994310099 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -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( -[1. 0.89473684 0.86842105 0.94736842 0.89473684 0.94736842 - 0.97368421 0.94736842 0.83625731 0.94590643] - -mean value: 0.9255847953216374 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 0.78947368 0.76190476 0.9 0.80952381 0.89473684 - 0.94736842 0.9047619 0.73913043 0.89473684] - -mean value: 0.8641636700446769 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 16 - -mean value: 16.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 173 - -mean value: 173.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.92 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.91334343 0.90369296 0.98667979 0.89436364 0.91808653 0.8976388 - 0.94835401 0.92297745 0.92215323 0.92128587] - -mean value: 0.9228575706481934 - -key: score_time -value: [0.16783261 0.20793104 0.17526579 0.19081378 0.21681404 0.20326996 - 0.2266438 0.19304991 0.21808624 0.19505501] - -mean value: 0.19947621822357178 - -key: test_mcc -value: [1. 0.85280287 0.73786479 0.89473684 0.78947368 0.89973541 - 0.89973541 0.79388419 0.67849265 0.89181287] - -mean value: 0.8438538697509838 - -key: train_mcc -value: [0.96477265 0.95300713 0.9707394 0.96470588 0.97060503 0.97060503 - 0.97060503 0.9707394 0.98242174 0.96487667] - -mean value: 0.9683077962291513 - -key: test_fscore -value: [1. 0.91428571 0.86486486 0.94736842 0.89473684 0.94444444 - 0.94444444 0.9 0.85 0.94444444] - -mean value: 0.9204589175641807 - -key: train_fscore -value: [0.98224852 0.97633136 0.9851632 0.98235294 0.98525074 0.98533724 - 0.98525074 0.9851632 0.99120235 0.98235294] - -mean value: 0.9840653237874364 - -key: test_precision -value: [1. 1. 0.88888889 0.94736842 0.89473684 1. - 1. 0.85714286 0.80952381 0.94444444] - -mean value: 0.9342105263157896 - -key: train_precision -value: [0.98809524 0.98214286 0.99401198 0.98235294 0.98816568 0.98245614 - 0.98816568 0.99401198 0.98830409 0.98816568] - -mean value: 0.9875872263848621 - -key: test_recall -value: [1. 0.84210526 0.84210526 0.94736842 0.89473684 0.89473684 - 0.89473684 0.94736842 0.89473684 0.94444444] - -mean value: 0.9102339181286551 - -key: train_recall -value: [0.97647059 0.97058824 0.97647059 0.98235294 0.98235294 0.98823529 - 0.98235294 0.97647059 0.99411765 0.97660819] - -mean value: 0.9806019951840386 - -key: test_accuracy -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.94736842 0.89473684 0.83783784 0.94594595] - -mean value: 0.9204836415362732 - -key: train_accuracy -value: [0.98235294 0.97647059 0.98529412 0.98235294 0.98529412 0.98529412 - 0.98529412 0.98529412 0.99120235 0.98240469] - -mean value: 0.9841254096946697 - -key: test_roc_auc -value: [1. 0.92105263 0.86842105 0.94736842 0.89473684 0.94736842 - 0.94736842 0.89473684 0.83625731 0.94590643] - -mean value: 0.9203216374269007 - -key: train_roc_auc -value: [0.98235294 0.97647059 0.98529412 0.98235294 0.98529412 0.98529412 - 0.98529412 0.98529412 0.99121087 0.98242174] - -mean value: 0.9841279669762641 - -key: test_jcc -value: [1. 0.84210526 0.76190476 0.9 0.80952381 0.89473684 - 0.89473684 0.81818182 0.73913043 0.89473684] - -mean value: 0.8555056613866683 - -key: train_jcc -value: [0.96511628 0.95375723 0.97076023 0.96531792 0.97093023 0.97109827 - 0.97093023 0.97076023 0.98255814 0.96531792] - -mean value: 0.9686546681036955 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 172 - -mean value: 172.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.08098817 0.05724502 0.0551033 0.05975389 0.06186581 0.06051755 - 0.05972028 0.06197834 0.06002688 0.0621531 ] - -mean value: 0.06193523406982422 - -key: score_time -value: [0.01122761 0.01038623 0.01057529 0.01031351 0.01116657 0.01128936 - 0.01072359 0.01048517 0.01075482 0.01056719] - -mean value: 0.010748934745788575 - -key: test_mcc -value: [1. 1. 1. 0.9486833 0.89473684 0.89973541 - 0.89973541 0.9486833 0.78362573 0.89181287] - -mean value: 0.9267012856382394 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.97435897 0.94736842 0.94444444 - 0.95 0.97435897 0.89473684 0.94444444] - -mean value: 0.9629712100764733 - -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. 0.95 0.94736842 1. - 0.9047619 0.95 0.89473684 0.94444444] - -mean value: 0.9591311612364244 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.89473684 - 1. 1. 0.89473684 0.94444444] - -mean value: 0.9681286549707602 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89189189 0.94594595] - -mean value: 0.9627311522048364 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89181287 0.94590643] - -mean value: 0.9627192982456141 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.95 0.9 0.89473684 - 0.9047619 0.95 0.80952381 0.89473684] - -mean value: 0.9303759398496242 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03812528 0.0861218 0.09059358 0.07849884 0.0696094 0.07042861 - 0.05222893 0.03578925 0.03512812 0.06888628] - -mean value: 0.06254100799560547 - -key: score_time -value: [0.02181602 0.02097034 0.02056694 0.02170205 0.01909971 0.021734 - 0.01239753 0.01244664 0.01322293 0.02397895] - -mean value: 0.018793511390686034 - -key: test_mcc -value: [0.85280287 0.68421053 0.74620251 0.9486833 0.63245553 0.48454371 - 0.73786479 0.79388419 0.51461988 0.73020842] - -mean value: 0.7125475715813612 - -key: train_mcc -value: [0.94143711 0.93569892 0.95320508 0.94143711 0.96497304 0.95884012 - 0.94746872 0.94720632 0.95314274 0.96507709] - -mean value: 0.9508486254862536 - -key: test_fscore -value: [0.92682927 0.84210526 0.85714286 0.97435897 0.81081081 0.70588235 - 0.87179487 0.88888889 0.75675676 0.85714286] - -mean value: 0.8491712901287771 - -key: train_fscore -value: [0.9702381 0.96716418 0.97619048 0.9702381 0.98214286 0.97935103 - 0.97313433 0.97329377 0.97633136 0.98224852] - -mean value: 0.9750332713923386 - -key: test_precision -value: [0.86363636 0.84210526 0.9375 0.95 0.83333333 0.8 - 0.85 0.94117647 0.77777778 0.88235294] - -mean value: 0.8677882149670074 - -key: train_precision -value: [0.98192771 0.98181818 0.98795181 0.98192771 0.9939759 0.98224852 - 0.98787879 0.98203593 0.98214286 0.99401198] - -mean value: 0.9855919384271624 - -key: test_recall -value: [1. 0.84210526 0.78947368 1. 0.78947368 0.63157895 - 0.89473684 0.84210526 0.73684211 0.83333333] - -mean value: 0.8359649122807017 - -key: train_recall -value: [0.95882353 0.95294118 0.96470588 0.95882353 0.97058824 0.97647059 - 0.95882353 0.96470588 0.97058824 0.97076023] - -mean value: 0.9647230822153421 - -key: test_accuracy -value: [0.92105263 0.84210526 0.86842105 0.97368421 0.81578947 0.73684211 - 0.86842105 0.89473684 0.75675676 0.86486486] - -mean value: 0.854267425320057 - -key: train_accuracy -value: [0.97058824 0.96764706 0.97647059 0.97058824 0.98235294 0.97941176 - 0.97352941 0.97352941 0.97653959 0.98240469] - -mean value: 0.9753061928583749 - -key: test_roc_auc -value: [0.92105263 0.84210526 0.86842105 0.97368421 0.81578947 0.73684211 - 0.86842105 0.89473684 0.75730994 0.86403509] - -mean value: 0.8542397660818712 - -key: train_roc_auc -value: [0.97058824 0.96764706 0.97647059 0.97058824 0.98235294 0.97941176 - 0.97352941 0.97352941 0.97652219 0.98243894] - -mean value: 0.9753078775369799 - -key: test_jcc -value: [0.86363636 0.72727273 0.75 0.95 0.68181818 0.54545455 - 0.77272727 0.8 0.60869565 0.75 ] - -mean value: 0.7449604743083004 - -key: train_jcc -value: [0.94219653 0.93641618 0.95348837 0.94219653 0.96491228 0.95953757 - 0.94767442 0.94797688 0.95375723 0.96511628] - -mean value: 0.9513272275324688 - -key: TN -value: 165 - -mean value: 165.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 24 - -mean value: 24.0 - -key: TP -value: 158 - -mean value: 158.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.58 - -Accuracy on Blind test: 0.79 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02059793 0.01004887 0.00978994 0.00940585 0.01050782 0.01005507 - 0.00994229 0.00919342 0.00953698 0.01041079] - -mean value: 0.010948896408081055 - -key: score_time -value: [0.01217175 0.00904155 0.00964379 0.00889254 0.00908017 0.00930786 - 0.00947952 0.00877833 0.00881815 0.00949812] - -mean value: 0.00947117805480957 - -key: test_mcc -value: [0.31980107 0.05292561 0.37686733 0.37047929 0.10910895 0.22645541 - 0.10910895 0.42640143 0.24408665 0.4163404 ] - -mean value: 0.26515750897917323 - -key: train_mcc -value: [0.28235294 0.25347236 0.30596706 0.28252897 0.32353501 0.30012984 - 0.30607305 0.31773503 0.30791194 0.29657127] - -mean value: 0.29762774583330065 - -key: test_fscore -value: [0.62857143 0.55 0.64705882 0.66666667 0.60465116 0.66666667 - 0.48484848 0.73170732 0.66666667 0.64516129] - -mean value: 0.6291998507135774 - -key: train_fscore -value: [0.64117647 0.63817664 0.65697674 0.64739884 0.6627566 0.64477612 - 0.65895954 0.6547619 0.65294118 0.65909091] - -mean value: 0.6517014942420667 - -key: test_precision -value: [0.6875 0.52380952 0.73333333 0.70588235 0.54166667 0.57692308 - 0.57142857 0.68181818 0.60869565 0.76923077] - -mean value: 0.6400288128325213 - -key: train_precision -value: [0.64117647 0.61878453 0.64942529 0.63636364 0.66081871 0.65454545 - 0.64772727 0.6626506 0.65294118 0.64088398] - -mean value: 0.6465317122198733 - -key: test_recall -value: [0.57894737 0.57894737 0.57894737 0.63157895 0.68421053 0.78947368 - 0.42105263 0.78947368 0.73684211 0.55555556] - -mean value: 0.6345029239766081 - -key: train_recall -value: [0.64117647 0.65882353 0.66470588 0.65882353 0.66470588 0.63529412 - 0.67058824 0.64705882 0.65294118 0.67836257] - -mean value: 0.6572480220158238 - -key: test_accuracy -value: [0.65789474 0.52631579 0.68421053 0.68421053 0.55263158 0.60526316 - 0.55263158 0.71052632 0.62162162 0.7027027 ] - -mean value: 0.629800853485064 - -key: train_accuracy -value: [0.64117647 0.62647059 0.65294118 0.64117647 0.66176471 0.65 - 0.65294118 0.65882353 0.65395894 0.64809384] - -mean value: 0.6487346903570813 - -key: test_roc_auc -value: [0.65789474 0.52631579 0.68421053 0.68421053 0.55263158 0.60526316 - 0.55263158 0.71052632 0.61842105 0.69883041] - -mean value: 0.629093567251462 - -key: train_roc_auc -value: [0.64117647 0.62647059 0.65294118 0.64117647 0.66176471 0.65 - 0.65294118 0.65882353 0.65395597 0.64800482] - -mean value: 0.6487254901960784 - -key: test_jcc -value: [0.45833333 0.37931034 0.47826087 0.5 0.43333333 0.5 - 0.32 0.57692308 0.5 0.47619048] - -mean value: 0.4622351434173024 - -key: train_jcc -value: [0.47186147 0.46861925 0.48917749 0.47863248 0.49561404 0.47577093 - 0.49137931 0.48672566 0.48471616 0.49152542] - -mean value: 0.4834022201726912 - -key: TN -value: 118 - -mean value: 118.0 - -key: FP -value: 69 - -mean value: 69.0 - -key: FN -value: 71 - -mean value: 71.0 - -key: TP -value: 120 - -mean value: 120.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.62 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01383066 0.01722169 0.01890635 0.01881194 0.01980543 0.02099442 - 0.01923275 0.02122712 0.02542329 0.01996803] - -mean value: 0.019542169570922852 - -key: score_time -value: [0.00900412 0.01116323 0.01140761 0.01198363 0.01194572 0.01202917 - 0.0118959 0.01189232 0.01198125 0.01195574] - -mean value: 0.011525869369506836 - -key: test_mcc -value: [0.85280287 0.85280287 0.74620251 0.80757285 0.73786479 0.79388419 - 0.85280287 0.76376262 0.75938069 0.64287856] - -mean value: 0.7809954803726716 - -key: train_mcc -value: [0.91215932 0.90896992 0.89635357 0.88199656 0.94720632 0.95923851 - 0.86610667 0.93569892 0.93120967 0.88886965] - -mean value: 0.912780911924659 - -key: test_fscore -value: [0.92682927 0.91428571 0.85714286 0.88235294 0.86486486 0.88888889 - 0.91428571 0.88372093 0.84848485 0.77419355] - -mean value: 0.8755049576041696 - -key: train_fscore -value: [0.95652174 0.95092025 0.94512195 0.93457944 0.97329377 0.97910448 - 0.9245283 0.96811594 0.96363636 0.9378882 ] - -mean value: 0.9533710427468896 - -key: test_precision -value: [0.86363636 1. 0.9375 1. 0.88888889 0.94117647 - 1. 0.79166667 1. 0.92307692] - -mean value: 0.9345945312857078 - -key: train_precision -value: [0.94285714 0.99358974 0.98101266 0.99337748 0.98203593 0.99393939 - 0.99324324 0.95428571 0.99375 1. ] - -mean value: 0.9828091307730509 - -key: test_recall -value: [1. 0.84210526 0.78947368 0.78947368 0.84210526 0.84210526 - 0.84210526 1. 0.73684211 0.66666667] - -mean value: 0.8350877192982455 - -key: train_recall -value: [0.97058824 0.91176471 0.91176471 0.88235294 0.96470588 0.96470588 - 0.86470588 0.98235294 0.93529412 0.88304094] - -mean value: 0.9271276229790162 - -key: test_accuracy -value: [0.92105263 0.92105263 0.86842105 0.89473684 0.86842105 0.89473684 - 0.92105263 0.86842105 0.86486486 0.81081081] - -mean value: 0.8833570412517779 - -key: train_accuracy -value: [0.95588235 0.95294118 0.94705882 0.93823529 0.97352941 0.97941176 - 0.92941176 0.96764706 0.96480938 0.94134897] - -mean value: 0.9550276004830085 - -key: test_roc_auc -value: [0.92105263 0.92105263 0.86842105 0.89473684 0.86842105 0.89473684 - 0.92105263 0.86842105 0.86842105 0.80701754] - -mean value: 0.8833333333333332 - -key: train_roc_auc -value: [0.95588235 0.95294118 0.94705882 0.93823529 0.97352941 0.97941176 - 0.92941176 0.96764706 0.96472308 0.94152047] - -mean value: 0.9550361197110424 - -key: test_jcc -value: [0.86363636 0.84210526 0.75 0.78947368 0.76190476 0.8 - 0.84210526 0.79166667 0.73684211 0.63157895] - -mean value: 0.7809313055365686 - -key: train_jcc -value: [0.91666667 0.90643275 0.89595376 0.87719298 0.94797688 0.95906433 - 0.85964912 0.93820225 0.92982456 0.88304094] - -mean value: 0.9114004228058402 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 31 - -mean value: 31.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 158 - -mean value: 158.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.0196104 0.0186727 0.01854467 0.01583815 0.01705647 0.01839828 - 0.0193193 0.01725936 0.01903057 0.01761365] - -mean value: 0.018134355545043945 - -key: score_time -value: [0.01171803 0.01168942 0.01174426 0.01166487 0.01176047 0.01166606 - 0.01205897 0.01166153 0.01174474 0.01171303] - -mean value: 0.011742138862609863 - -key: test_mcc -value: [0.80757285 0.80757285 0.76376262 0.67936622 0.52223297 0.52704628 - 0.5976143 0.34299717 0.51319869 0.89181287] - -mean value: 0.6453176815159228 - -key: train_mcc -value: [0.78951485 0.87667634 0.91444234 0.69199114 0.69776853 0.92966915 - 0.7673233 0.5274424 0.95334436 0.86759805] - -mean value: 0.8015770462195411 - -key: test_fscore -value: [0.9047619 0.88235294 0.84848485 0.84444444 0.7826087 0.76923077 - 0.68965517 0.71698113 0.76923077 0.94444444] - -mean value: 0.8152195121915089 - -key: train_fscore -value: [0.89655172 0.93125 0.95412844 0.85204082 0.85353535 0.96428571 - 0.85135135 0.77981651 0.97674419 0.93521127] - -mean value: 0.8994915367417468 - -key: test_precision -value: [0.82608696 1. 1. 0.73076923 0.66666667 0.75 - 1. 0.55882353 0.75 0.94444444] - -mean value: 0.8226790827813846 - -key: train_precision -value: [0.81642512 0.99333333 0.99363057 0.75225225 0.74778761 0.97590361 - 1. 0.63909774 0.96551724 0.90217391] - -mean value: 0.878612140346793 - -key: test_recall -value: [1. 0.78947368 0.73684211 1. 0.94736842 0.78947368 - 0.52631579 1. 0.78947368 0.94444444] - -mean value: 0.8523391812865496 - -key: train_recall -value: [0.99411765 0.87647059 0.91764706 0.98235294 0.99411765 0.95294118 - 0.74117647 1. 0.98823529 0.97076023] - -mean value: 0.941781905744754 - -key: test_accuracy -value: [0.89473684 0.89473684 0.86842105 0.81578947 0.73684211 0.76315789 - 0.76315789 0.60526316 0.75675676 0.94594595] - -mean value: 0.8044807965860598 - -key: train_accuracy -value: [0.88529412 0.93529412 0.95588235 0.82941176 0.82941176 0.96470588 - 0.87058824 0.71764706 0.97653959 0.93255132] - -mean value: 0.8897326203208555 - -key: test_roc_auc -value: [0.89473684 0.89473684 0.86842105 0.81578947 0.73684211 0.76315789 - 0.76315789 0.60526316 0.75584795 0.94590643] - -mean value: 0.8043859649122806 - -key: train_roc_auc -value: [0.88529412 0.93529412 0.95588235 0.82941176 0.82941176 0.96470588 - 0.87058824 0.71764706 0.97657379 0.93243894] - -mean value: 0.8897248022015823 - -key: test_jcc -value: [0.82608696 0.78947368 0.73684211 0.73076923 0.64285714 0.625 - 0.52631579 0.55882353 0.625 0.89473684] - -mean value: 0.6955905280612509 - -key: train_jcc -value: [0.8125 0.87134503 0.9122807 0.74222222 0.74449339 0.93103448 - 0.74117647 0.63909774 0.95454545 0.87830688] - -mean value: 0.822700237584695 - -key: TN -value: 143 - -mean value: 143.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 46 - -mean value: 46.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.72 - -Accuracy on Blind test: 0.86 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.16564989 0.14762282 0.14760208 0.15012002 0.15004492 0.14741898 - 0.14801168 0.14737749 0.14529824 0.14780712] - -mean value: 0.1496953248977661 - -key: score_time -value: [0.01533413 0.01543188 0.01512837 0.01557231 0.01544023 0.01623416 - 0.0151217 0.014956 0.01524425 0.01508498] - -mean value: 0.015354800224304199 - -key: test_mcc -value: [1. 1. 1. 0.9486833 0.9486833 0.9486833 - 0.79388419 0.89473684 0.78362573 0.94736842] - -mean value: 0.9265665074341033 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 1. 0.97435897 0.97297297 0.97297297 - 0.9 0.94736842 0.89473684 0.97297297] - -mean value: 0.963538315643579 - -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. 0.95 1. 1. - 0.85714286 0.94736842 0.89473684 0.94736842] - -mean value: 0.9596616541353384 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.94736842 - 0.94736842 0.94736842 0.89473684 1. ] - -mean value: 0.968421052631579 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 1. 0.97368421 0.97368421 0.97368421 - 0.89473684 0.94736842 0.89189189 0.97297297] - -mean value: 0.9628022759601708 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 1. 0.97368421 0.97368421 0.97368421 - 0.89473684 0.94736842 0.89181287 0.97368421] - -mean value: 0.9628654970760232 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 1. 0.95 0.94736842 0.94736842 - 0.81818182 0.9 0.80952381 0.94736842] - -mean value: 0.9319810890863524 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.97 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.0346179 0.04360032 0.06715274 0.05969524 0.06441498 0.04464555 - 0.04578137 0.04370594 0.04923081 0.0484879 ] - -mean value: 0.050133275985717776 - -key: score_time -value: [0.01677918 0.01960993 0.03374028 0.02136087 0.01999903 0.01861191 - 0.02503061 0.01971626 0.02421546 0.02401495] - -mean value: 0.022307848930358885 - -key: test_mcc -value: [1. 1. 0.9486833 0.9486833 0.89473684 0.89973541 - 0.89473684 0.9486833 0.78362573 0.89181287] - -mean value: 0.9210697585695733 - -key: train_mcc -value: [1. 0.99413485 1. 1. 0.98830369 0.98830369 - 0.98823529 0.98830369 0.99415185 0.98833809] - -mean value: 0.9929771153408072 - -key: test_fscore -value: [1. 1. 0.97435897 0.97435897 0.94736842 0.94444444 - 0.94736842 0.97435897 0.89473684 0.94444444] - -mean value: 0.9601439496176338 - -key: train_fscore -value: [1. 0.99705015 1. 1. 0.99408284 0.99408284 - 0.99411765 0.99408284 0.99705015 0.99411765] - -mean value: 0.9964584109812957 - -key: test_precision -value: [1. 1. 0.95 0.95 0.94736842 1. - 0.94736842 0.95 0.89473684 0.94444444] - -mean value: 0.9583918128654971 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. - 0.99411765 1. 1. 1. ] - -mean value: 0.9994117647058823 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.89473684 - 0.94736842 1. 0.89473684 0.94444444] - -mean value: 0.9628654970760234 - -key: train_recall -value: [1. 0.99411765 1. 1. 0.98823529 0.98823529 - 0.99411765 0.98823529 0.99411765 0.98830409] - -mean value: 0.9935362917096663 - -key: test_accuracy -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89189189 0.94594595] - -mean value: 0.960099573257468 - -key: train_accuracy -value: [1. 0.99705882 1. 1. 0.99411765 0.99411765 - 0.99411765 0.99411765 0.99706745 0.9941349 ] - -mean value: 0.9964731757805761 - -key: test_roc_auc -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.94736842 - 0.94736842 0.97368421 0.89181287 0.94590643] - -mean value: 0.9600877192982458 - -key: train_roc_auc -value: [1. 0.99705882 1. 1. 0.99411765 0.99411765 - 0.99411765 0.99411765 0.99705882 0.99415205] - -mean value: 0.9964740282077743 - -key: test_jcc -value: [1. 1. 0.95 0.95 0.9 0.89473684 - 0.9 0.95 0.80952381 0.89473684] - -mean value: 0.9248997493734337 - -key: train_jcc -value: [1. 0.99411765 1. 1. 0.98823529 0.98823529 - 0.98830409 0.98823529 0.99411765 0.98830409] - -mean value: 0.992954936360509 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 182 - -mean value: 182.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.9 - -Accuracy on Blind test: 0.95 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.04708862 0.06212234 0.09990549 0.07586265 0.05217052 0.0878098 - 0.09745502 0.09905672 0.10533142 0.12506104] - -mean value: 0.08518636226654053 - -key: score_time -value: [0.01401854 0.01384234 0.02174687 0.01413202 0.01422572 0.01446748 - 0.02187872 0.02225208 0.02189755 0.02447367] - -mean value: 0.01829349994659424 - -key: test_mcc -value: [0.21320072 0.21320072 0.26315789 0.37686733 0.37686733 0.43643578 - 0.37047929 0.52704628 0.18768409 0.40780312] - -mean value: 0.33727425458534505 - -key: train_mcc -value: [0.92367325 0.92947609 0.91771057 0.92966915 0.92947609 0.92947609 - 0.92947609 0.94124161 0.92376759 0.92968454] - -mean value: 0.9283651071166753 - -key: test_fscore -value: [0.57142857 0.63414634 0.63157895 0.64705882 0.71428571 0.74418605 - 0.66666667 0.75675676 0.61538462 0.66666667] - -mean value: 0.6648159150061868 - -key: train_fscore -value: [0.96209913 0.96491228 0.95906433 0.96511628 0.96449704 0.96491228 - 0.96449704 0.9704142 0.96165192 0.96470588] - -mean value: 0.9641870377103828 - -key: test_precision -value: [0.625 0.59090909 0.63157895 0.73333333 0.65217391 0.66666667 - 0.70588235 0.77777778 0.6 0.73333333] - -mean value: 0.6716655415373277 - -key: train_precision -value: [0.95375723 0.95930233 0.95348837 0.95402299 0.9702381 0.95930233 - 0.9702381 0.97619048 0.96449704 0.9704142 ] - -mean value: 0.9631451146465304 - -key: test_recall -value: [0.52631579 0.68421053 0.63157895 0.57894737 0.78947368 0.84210526 - 0.63157895 0.73684211 0.63157895 0.61111111] - -mean value: 0.6663742690058478 - -key: train_recall -value: [0.97058824 0.97058824 0.96470588 0.97647059 0.95882353 0.97058824 - 0.95882353 0.96470588 0.95882353 0.95906433] - -mean value: 0.9653181974544204 - -key: test_accuracy -value: [0.60526316 0.60526316 0.63157895 0.68421053 0.68421053 0.71052632 - 0.68421053 0.76315789 0.59459459 0.7027027 ] - -mean value: 0.6665718349928876 - -key: train_accuracy -value: [0.96176471 0.96470588 0.95882353 0.96470588 0.96470588 0.96470588 - 0.96470588 0.97058824 0.96187683 0.96480938] - -mean value: 0.9641392099361739 - -key: test_roc_auc -value: [0.60526316 0.60526316 0.63157895 0.68421053 0.68421053 0.71052632 - 0.68421053 0.76315789 0.59356725 0.7002924 ] - -mean value: 0.6662280701754385 - -key: train_roc_auc -value: [0.96176471 0.96470588 0.95882353 0.96470588 0.96470588 0.96470588 - 0.96470588 0.97058824 0.96186791 0.96482628] - -mean value: 0.964140006879945 - -key: test_jcc -value: [0.4 0.46428571 0.46153846 0.47826087 0.55555556 0.59259259 - 0.5 0.60869565 0.44444444 0.5 ] - -mean value: 0.5005373290155899 - -key: train_jcc -value: [0.92696629 0.93220339 0.92134831 0.93258427 0.93142857 0.93220339 - 0.93142857 0.94252874 0.92613636 0.93181818] - -mean value: 0.9308646080009384 - -key: TN -value: 126 - -mean value: 126.0 - -key: FP -value: 63 - -mean value: 63.0 - -key: FN -value: 63 - -mean value: 63.0 - -key: TP -value: 126 - -mean value: 126.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.67 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.57018256 0.57173681 0.55075383 0.57621026 0.55562973 0.55847478 - 0.55890369 0.56449723 0.56477332 0.5758698 ] - -mean value: 0.5647032022476196 - -key: score_time -value: [0.00927925 0.00941944 0.00985742 0.00972795 0.00931621 0.00947785 - 0.00971198 0.01008582 0.00939679 0.00960755] - -mean value: 0.009588027000427246 - -key: test_mcc -value: [1. 1. 0.9486833 0.9486833 0.89473684 0.9486833 - 0.89973541 0.9486833 0.78362573 0.89181287] - -mean value: 0.9264644041640983 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [1. 1. 0.97435897 0.97435897 0.94736842 0.97297297 - 0.95 0.97435897 0.89473684 0.94444444] - -mean value: 0.9632599603652237 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 1. 0.95 0.95 0.94736842 1. - 0.9047619 0.95 0.89473684 0.94444444] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -mean value: 0.9541311612364245 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 1. 1. 1. 0.94736842 0.94736842 - 1. 1. 0.89473684 0.94444444] - -mean value: 0.973391812865497 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.97368421 - 0.94736842 0.97368421 0.89189189 0.94594595] - -mean value: 0.9627311522048364 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [1. 1. 0.97368421 0.97368421 0.94736842 0.97368421 - 0.94736842 0.97368421 0.89181287 0.94590643] - -mean value: 0.9627192982456141 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [1. 1. 0.95 0.95 0.9 0.94736842 - 0.9047619 0.95 0.80952381 0.89473684] - -mean value: 0.930639097744361 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 5 - -mean value: 5.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 184 - -mean value: 184.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02358079 0.0327971 0.03421521 0.05197883 0.0313015 0.0442791 - 0.02697372 0.02759576 0.02706003 0.0274601 ] - -mean value: 0.03272421360015869 - -key: score_time -value: [0.01431394 0.01529551 0.01366639 0.01466751 0.01410532 0.01376867 - 0.01386666 0.01403141 0.01405978 0.0143199 ] - -mean value: 0.014209508895874023 - -key: test_mcc -value: [0.47368421 0.21081851 0.10660036 0.32732684 0.48454371 0.31622777 - 0.31622777 0.52704628 0.13274856 0.56934383] - -mean value: 0.3464567824676505 - -key: train_mcc -value: [0.98250594 0.94143711 0.71492035 0.97100831 0.9707394 0.82653997 - 0.91533482 0.95399809 0.94296223 0.89113245] - -mean value: 0.9110578681629908 - -key: test_fscore -value: [0.73684211 0.61538462 0.51428571 0.69767442 0.76190476 0.64864865 - 0.66666667 0.75675676 0.6 0.76470588] - -mean value: 0.6762869569867914 - -key: train_fscore -value: [0.99109792 0.97093023 0.80701754 0.98550725 0.98542274 0.8961039 - 0.95384615 0.97590361 0.96969697 0.94647887] - -mean value: 0.9482005193512334 - -key: test_precision -value: [0.73684211 0.6 0.5625 0.625 0.69565217 0.66666667 - 0.65 0.77777778 0.57142857 0.8125 ] - -mean value: 0.6698367295049217 - -key: train_precision -value: [1. 0.95977011 1. 0.97142857 0.97687861 1. - 1. 1. 1. 0.91304348] - -mean value: 0.9821120777348732 - -key: test_recall -value: [0.73684211 0.63157895 0.47368421 0.78947368 0.84210526 0.63157895 - 0.68421053 0.73684211 0.63157895 0.72222222] - -mean value: 0.6880116959064326 - -key: train_recall -value: [0.98235294 0.98235294 0.67647059 1. 0.99411765 0.81176471 - 0.91176471 0.95294118 0.94117647 0.98245614] - -mean value: 0.9235397316821465 - -key: test_accuracy -value: [0.73684211 0.60526316 0.55263158 0.65789474 0.73684211 0.65789474 - 0.65789474 0.76315789 0.56756757 0.78378378] - -mean value: 0.671977240398293 - -key: train_accuracy -value: [0.99117647 0.97058824 0.83823529 0.98529412 0.98529412 0.90588235 - 0.95588235 0.97647059 0.97067449 0.94428152] - -mean value: 0.952377954114197 - -key: test_roc_auc -value: [0.73684211 0.60526316 0.55263158 0.65789474 0.73684211 0.65789474 - 0.65789474 0.76315789 0.56578947 0.78216374] - -mean value: 0.6716374269005848 - -key: train_roc_auc -value: [0.99117647 0.97058824 0.83823529 0.98529412 0.98529412 0.90588235 - 0.95588235 0.97647059 0.97058824 0.94416925] - -mean value: 0.9523581011351908 - -key: test_jcc -value: [0.58333333 0.44444444 0.34615385 0.53571429 0.61538462 0.48 - 0.5 0.60869565 0.42857143 0.61904762] - -mean value: 0.5161345224823485 - -key: train_jcc -value: [0.98235294 0.94350282 0.67647059 0.97142857 0.97126437 0.81176471 - 0.91176471 0.95294118 0.94117647 0.89839572] - -mean value: 0.9061062074263848 - -key: TN -value: 128 - -mean value: 128.0 - -key: FP -value: 58 - -mean value: 58.0 - -key: FN -value: 61 - -mean value: 61.0 - -key: TP -value: 131 - -mean value: 131.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.67 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02306318 0.03567028 0.02913189 0.03163505 0.05307055 0.04880238 - 0.03774571 0.03748631 0.03827477 0.03649569] - -mean value: 0.03713757991790771 - -key: score_time -value: [0.01727629 0.02138758 0.02393579 0.02225113 0.0235672 0.02406406 - 0.02092433 0.0130291 0.02104759 0.02183938] - -mean value: 0.0209322452545166 - -key: test_mcc -value: [0.85280287 0.85280287 0.76376262 1. 0.73786479 0.74620251 - 0.89473684 0.84327404 0.63129316 0.83871328] - -mean value: 0.816145296675858 - -key: train_mcc -value: [0.91304513 0.94176322 0.93044258 0.92431333 0.91821914 0.93044258 - 0.92431333 0.94786272 0.94192596 0.93065259] - -mean value: 0.9302980583084139 - -key: test_fscore -value: [0.92682927 0.91428571 0.84848485 1. 0.86486486 0.85714286 - 0.94736842 0.91891892 0.8 0.91428571] - -mean value: 0.8992180607328233 - -key: train_fscore -value: [0.95468278 0.97005988 0.96385542 0.96096096 0.95808383 0.96385542 - 0.96096096 0.97297297 0.97005988 0.96407186] - -mean value: 0.963956396682638 - -key: test_precision -value: [0.86363636 1. 1. 1. 0.88888889 0.9375 - 0.94736842 0.94444444 0.875 0.94117647] - -mean value: 0.9398014588610565 - -key: train_precision -value: [0.98136646 0.98780488 0.98765432 0.98159509 0.97560976 0.98765432 - 0.98159509 0.99386503 0.98780488 0.98773006] - -mean value: 0.9852679889871379 - -key: test_recall -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: 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 - ros_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: 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 - ros_CV['Resampling'] = rs_ros -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: 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 - ros_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: 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 - ros_BT['Resampling'] = rs_ros -[1. 0.84210526 0.73684211 1. 0.84210526 0.78947368 - 0.94736842 0.89473684 0.73684211 0.88888889] - -mean value: 0.8678362573099415 - -key: train_recall -value: [0.92941176 0.95294118 0.94117647 0.94117647 0.94117647 0.94117647 - 0.94117647 0.95294118 0.95294118 0.94152047] - -mean value: 0.9435638114895081 - -key: test_accuracy -value: [0.92105263 0.92105263 0.86842105 1. 0.86842105 0.86842105 - 0.94736842 0.92105263 0.81081081 0.91891892] - -mean value: 0.9045519203413941 - -key: train_accuracy -value: [0.95588235 0.97058824 0.96470588 0.96176471 0.95882353 0.96470588 - 0.96176471 0.97352941 0.97067449 0.96480938] - -mean value: 0.9647248576850096 - -key: test_roc_auc -value: [0.92105263 0.92105263 0.86842105 1. 0.86842105 0.86842105 - 0.94736842 0.92105263 0.8128655 0.91812865] - -mean value: 0.9046783625730994 - -key: train_roc_auc -value: [0.95588235 0.97058824 0.96470588 0.96176471 0.95882353 0.96470588 - 0.96176471 0.97352941 0.97062264 0.96487788] - -mean value: 0.9647265221878225 - -key: test_jcc -value: [0.86363636 0.84210526 0.73684211 1. 0.76190476 0.75 - 0.9 0.85 0.66666667 0.84210526] - -mean value: 0.8213260423786739 - -key: train_jcc -value: [0.9132948 0.94186047 0.93023256 0.92485549 0.91954023 0.93023256 - 0.92485549 0.94736842 0.94186047 0.93063584] - -mean value: 0.9304736315946428 - -key: TN -value: 178 - -mean value: 178.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 164 - -mean value: 164.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.27701378 0.26186919 0.24811435 0.24859476 0.2535429 0.2831912 - 0.31991243 0.25316024 0.25401473 0.25029159] - -mean value: 0.2649705171585083 - -key: score_time -value: [0.02331662 0.02247548 0.01780939 0.02337408 0.01888537 0.02059937 - 0.02222824 0.01926541 0.01842356 0.02039099] - -mean value: 0.020676851272583008 - -key: test_mcc -value: [0.85280287 0.73786479 0.76376262 1. 0.73786479 0.61017022 - 0.89473684 0.84327404 0.63129316 0.83871328] - -mean value: 0.7910482597311874 - -key: train_mcc -value: [0.91304513 0.92966915 0.93044258 0.92431333 0.91821914 0.95320508 - 0.92431333 0.94786272 0.94192596 0.93065259] - -mean value: 0.9313649005021245 - -key: test_fscore -value: [0.92682927 0.86486486 0.84848485 1. 0.86486486 0.75 - 0.94736842 0.91891892 0.8 0.91428571] - -mean value: 0.8835616900764526 - -key: train_fscore -value: [0.95468278 0.96428571 0.96385542 0.96096096 0.95808383 0.97619048 - 0.96096096 0.97297297 0.97005988 0.96407186] - -mean value: 0.9646124855376301 - -key: test_precision -value: [0.86363636 0.88888889 1. 1. 0.88888889 0.92307692 - 0.94736842 0.94444444 0.875 0.94117647] - -mean value: 0.9272480400576377 - -key: train_precision -value: [0.98136646 0.97590361 0.98765432 0.98159509 0.97560976 0.98795181 - 0.98159509 0.99386503 0.98780488 0.98773006] - -mean value: 0.9841076112521691 - -key: test_recall -value: [1. 0.84210526 0.73684211 1. 0.84210526 0.63157895 - 0.94736842 0.89473684 0.73684211 0.88888889] - -mean value: 0.852046783625731 - -key: train_recall -value: [0.92941176 0.95294118 0.94117647 0.94117647 0.94117647 0.96470588 - 0.94117647 0.95294118 0.95294118 0.94152047] - -mean value: 0.9459167526659786 - -key: test_accuracy -value: [0.92105263 0.86842105 0.86842105 1. 0.86842105 0.78947368 - 0.94736842 0.92105263 0.81081081 0.91891892] - -mean value: 0.8913940256045519 - -key: train_accuracy -value: [0.95588235 0.96470588 0.96470588 0.96176471 0.95882353 0.97647059 - 0.96176471 0.97352941 0.97067449 0.96480938] - -mean value: 0.965313092979127 - -key: test_roc_auc -value: [0.92105263 0.86842105 0.86842105 1. 0.86842105 0.78947368 - 0.94736842 0.92105263 0.8128655 0.91812865] - -mean value: 0.8915204678362573 - -key: train_roc_auc -value: [0.95588235 0.96470588 0.96470588 0.96176471 0.95882353 0.97647059 - 0.96176471 0.97352941 0.97062264 0.96487788] - -mean value: 0.96531475748194 - -key: test_jcc -value: [0.86363636 0.76190476 0.73684211 1. 0.76190476 0.6 - 0.9 0.85 0.66666667 0.84210526] - -mean value: 0.7983059922533607 - -key: train_jcc -value: [0.9132948 0.93103448 0.93023256 0.92485549 0.91954023 0.95348837 - 0.92485549 0.94736842 0.94186047 0.93063584] - -mean value: 0.9317166147542257 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.032619 0.03256273 0.0348897 0.07011104 0.06926346 0.05668783 - 0.05581689 0.03852105 0.0337534 0.0391767 ] - -mean value: 0.04634017944335937 - -key: score_time -value: [0.01185489 0.01178741 0.01307511 0.01350832 0.02393889 0.01531935 - 0.01394129 0.01387501 0.01309395 0.01310873] - -mean value: 0.014350295066833496 - -key: test_mcc -value: [0.62170355 0.62280702 0.89679028 0.83918129 0.56934383 0.68035483 - 0.74044197 0.62280702 0.78262379 0.9459053 ] - -mean value: 0.7321958882228101 - -key: train_mcc -value: [0.88019928 0.8676531 0.8681022 0.84390805 0.86227946 0.83134227 - 0.85017486 0.84343625 0.88054121 0.85598101] - -mean value: 0.8583617698529198 - -key: test_fscore -value: [0.8 0.81081081 0.94117647 0.91891892 0.8 0.83333333 - 0.85714286 0.81081081 0.88235294 0.97297297] - -mean value: 0.8627519115754412 - -key: train_fscore -value: [0.9382716 0.93251534 0.93209877 0.91975309 0.92834891 0.91358025 - 0.92211838 0.91975309 0.9382716 0.92638037] - -mean value: 0.9271091390302828 - -key: test_precision -value: [0.82352941 0.78947368 1. 0.89473684 0.76190476 0.88235294 - 0.9375 0.83333333 0.9375 0.94736842] - -mean value: 0.8807699395547693 - -key: train_precision -value: [0.96202532 0.95 0.9556962 0.94303797 0.95512821 0.93081761 - 0.94871795 0.93710692 0.96202532 0.94375 ] - -mean value: 0.9488305492274621 - -key: test_recall -value: [0.77777778 0.83333333 0.88888889 0.94444444 0.84210526 0.78947368 - 0.78947368 0.78947368 0.83333333 1. ] - -mean value: 0.8488304093567252 - -key: train_recall -value: [0.91566265 0.91566265 0.90963855 0.89759036 0.9030303 0.8969697 - 0.8969697 0.9030303 0.91566265 0.90963855] - -mean value: 0.9063855421686748 - -key: test_accuracy -value: [0.81081081 0.81081081 0.94594595 0.91891892 0.78378378 0.83783784 - 0.86486486 0.81081081 0.88888889 0.97222222] - -mean value: 0.8644894894894894 - -key: train_accuracy -value: [0.93957704 0.93353474 0.93353474 0.92145015 0.9305136 0.91540785 - 0.9244713 0.92145015 0.93975904 0.92771084] - -mean value: 0.9287409456557345 - -key: test_roc_auc -value: [0.80994152 0.81140351 0.94444444 0.91959064 0.78216374 0.83918129 - 0.86695906 0.81140351 0.88888889 0.97222222] - -mean value: 0.8646198830409355 - -key: train_roc_auc -value: [0.93964951 0.9335889 0.93360716 0.92152245 0.93043081 0.91535232 - 0.92438846 0.92139467 0.93975904 0.92771084] - -mean value: 0.9287404162102956 - -key: test_jcc -value: [0.66666667 0.68181818 0.88888889 0.85 0.66666667 0.71428571 - 0.75 0.68181818 0.78947368 0.94736842] - -mean value: 0.7636986405407458 - -key: train_jcc -value: [0.88372093 0.87356322 0.87283237 0.85142857 0.86627907 0.84090909 - 0.85549133 0.85142857 0.88372093 0.86285714] - -mean value: 0.8642231224668704 - -key: TN -value: 162 - -mean value: 162.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 22 - -mean value: 22.0 - -key: TP -value: 156 - -mean value: 156.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.68 - -Accuracy on Blind test: 0.84 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.84434986 0.7982192 1.23400712 0.75540996 0.96277404 0.75669885 - 0.74814534 0.90700006 0.81799984 0.89407134] - -mean value: 0.871867561340332 - -key: score_time -value: [0.01352024 0.01340079 0.01650548 0.01338553 0.01493073 0.01359797 - 0.01477671 0.0151999 0.01473451 0.01355982] - -mean value: 0.014361166954040527 - -key: test_mcc -value: [0.6754386 0.67849265 0.84834956 0.80369958 0.62807634 0.78362573 - 0.7888597 0.78362573 0.83462233 0.89442719] - -mean value: 0.7719217391965397 - -key: train_mcc -value: [0.96994925 0.97590274 0.96403011 0.96994925 0.97590098 0.96994598 - 0.97590098 1. 0.97597445 0.96413537] - -mean value: 0.9741689112141751 - -key: test_fscore -value: [0.83333333 0.82352941 0.90909091 0.9 0.82926829 0.89473684 - 0.88888889 0.89473684 0.91428571 0.94736842] - -mean value: 0.8835238655309636 - -key: train_fscore -value: [0.98480243 0.98787879 0.98170732 0.98480243 0.98780488 0.98470948 - 0.98780488 1. 0.98787879 0.98170732] - -mean value: 0.9869096309345687 - -key: test_precision -value: [0.83333333 0.875 1. 0.81818182 0.77272727 0.89473684 - 0.94117647 0.89473684 0.94117647 0.9 ] - -mean value: 0.8871069049629421 - -key: train_precision -value: [0.99386503 0.99390244 0.99382716 0.99386503 0.99386503 0.99382716 - 0.99386503 1. 0.99390244 0.99382716] - -mean value: 0.9944746482229648 - -key: test_recall -value: [0.83333333 0.77777778 0.83333333 1. 0.89473684 0.89473684 - 0.84210526 0.89473684 0.88888889 1. ] - -mean value: 0.8859649122807017 - -key: train_recall -value: [0.97590361 0.98192771 0.96987952 0.97590361 0.98181818 0.97575758 - 0.98181818 1. 0.98192771 0.96987952] - -mean value: 0.9794815626140927 - -key: test_accuracy -value: [0.83783784 0.83783784 0.91891892 0.89189189 0.81081081 0.89189189 - 0.89189189 0.89189189 0.91666667 0.94444444] - -mean value: 0.8834084084084084 - -key: train_accuracy -value: [0.98489426 0.98791541 0.98187311 0.98489426 0.98791541 0.98489426 - 0.98791541 1. 0.98795181 0.98192771] - -mean value: 0.9870181632875916 - -key: test_roc_auc -value: [0.8377193 0.83625731 0.91666667 0.89473684 0.80847953 0.89181287 - 0.89327485 0.89181287 0.91666667 0.94444444] - -mean value: 0.8831871345029239 - -key: train_roc_auc -value: [0.9849215 0.98793355 0.98190946 0.9849215 0.98789704 0.98486674 - 0.98789704 1. 0.98795181 0.98192771] - -mean value: 0.9870226359985397 - -key: test_jcc -value: [0.71428571 0.7 0.83333333 0.81818182 0.70833333 0.80952381 - 0.8 0.80952381 0.84210526 0.9 ] - -mean value: 0.7935287081339714 - -key: train_jcc -value: [0.97005988 0.9760479 0.96407186 0.97005988 0.97590361 0.96987952 - 0.97590361 1. 0.9760479 0.96407186] - -mean value: 0.9742046028425078 - -key: TN -value: 161 -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( - -mean value: 161.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 163 - -mean value: 163.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01336503 0.0131321 0.01060534 0.00960517 0.00946927 0.00980926 - 0.00937319 0.00941658 0.00944281 0.00945592] - -mean value: 0.010367465019226075 - -key: score_time -value: [0.01216841 0.01018143 0.00914192 0.00898314 0.00889659 0.00883317 - 0.00889659 0.00906301 0.00914478 0.00891685] - -mean value: 0.009422588348388671 - -key: test_mcc -value: [0.46019501 0.02109391 0.51461988 0.35087719 0.35484024 0.29824561 - 0.42489158 0.35484024 0.61977979 0.39440532] - -mean value: 0.3793788765321226 - -key: train_mcc -value: [0.43880275 0.44445669 0.43291337 0.44758445 0.44141661 0.45599016 - 0.45817698 0.4286134 0.42245398 0.44111378] - -mean value: 0.4411522161885751 - -key: test_fscore -value: [0.70588235 0.4375 0.75675676 0.66666667 0.66666667 0.64864865 - 0.66666667 0.66666667 0.78787879 0.71794872] - -mean value: 0.6721281930840755 - -key: train_fscore -value: [0.7120743 0.71779141 0.70807453 0.70512821 0.69902913 0.69966997 - 0.7133758 0.69453376 0.70186335 0.70846395] - -mean value: 0.7060004409065255 - -key: test_precision -value: [0.75 0.5 0.73684211 0.66666667 0.70588235 0.66666667 - 0.78571429 0.70588235 0.86666667 0.66666667] - -mean value: 0.7050987763526464 - -key: train_precision -value: [0.73248408 0.73125 0.73076923 0.75342466 0.75 0.76811594 - 0.75167785 0.73972603 0.72435897 0.73856209] - -mean value: 0.742036885237408 - -key: test_recall -value: [0.66666667 0.38888889 0.77777778 0.66666667 0.63157895 0.63157895 - 0.57894737 0.63157895 0.72222222 0.77777778] - -mean value: 0.6473684210526316 - -key: train_recall -value: [0.69277108 0.70481928 0.68674699 0.6626506 0.65454545 0.64242424 - 0.67878788 0.65454545 0.68072289 0.68072289] - -mean value: 0.673873676524279 - -key: test_accuracy -value: [0.72972973 0.51351351 0.75675676 0.67567568 0.67567568 0.64864865 - 0.7027027 0.67567568 0.80555556 0.69444444] - -mean value: 0.6878378378378378 - -key: train_accuracy -value: [0.71903323 0.72205438 0.71601208 0.72205438 0.71903323 0.72507553 - 0.72809668 0.71299094 0.71084337 0.71987952] - -mean value: 0.7195073344738471 - -key: test_roc_auc -value: [0.72807018 0.51023392 0.75730994 0.6754386 0.67690058 0.64912281 - 0.70614035 0.67690058 0.80555556 0.69444444] - -mean value: 0.6880116959064327 - -key: train_roc_auc -value: [0.71911281 0.72210661 0.71610077 0.72223439 0.71883899 0.72482658 - 0.72794816 0.7128149 0.71084337 0.71987952] - -mean value: 0.7194706097115735 - -key: test_jcc -value: [0.54545455 0.28 0.60869565 0.5 0.5 0.48 - 0.5 0.5 0.65 0.56 ] - -mean value: 0.512415019762846 - -key: train_jcc -value: [0.55288462 0.55980861 0.54807692 0.54455446 0.53731343 0.53807107 - 0.55445545 0.5320197 0.54066986 0.54854369] - -mean value: 0.5456397800930715 - -key: TN -value: 134 - -mean value: 134.0 - -key: FP -value: 65 - -mean value: 65.0 - -key: FN -value: 50 - -mean value: 50.0 - -key: TP -value: 119 - -mean value: 119.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.4 - -Accuracy on Blind test: 0.7 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00953579 0.00970793 0.00979257 0.00957131 0.0095396 0.0095768 - 0.00947881 0.00938606 0.00950503 0.00949717] - -mean value: 0.009559106826782227 - -key: score_time -value: [0.00884414 0.00889468 0.00889063 0.00891185 0.00893068 0.00914407 - 0.00877166 0.0088675 0.0088985 0.00900674] - -mean value: 0.008916044235229492 - -key: test_mcc -value: [ 0.14287993 -0.02359974 0.08554907 0.19469789 0.29766651 0.13450292 - -0.13274856 0.29824561 0. 0.3354102 ] - -mean value: 0.13326038278434432 - -key: train_mcc -value: [0.29474984 0.28102287 0.29972296 0.33583386 0.33637194 0.28699748 - 0.30533238 0.29922583 0.27743079 0.3255139 ] - -mean value: 0.30422018586756716 - -key: test_fscore -value: [0.6 0.51282051 0.56410256 0.61538462 0.68292683 0.57894737 - 0.4 0.64864865 0.52631579 0.64705882] - -mean value: 0.5776205151648782 - -key: train_fscore -value: [0.66666667 0.64688427 0.6627907 0.67836257 0.67836257 0.6402439 - 0.65671642 0.65269461 0.64705882 0.66863905] - -mean value: 0.6598419591448714 - -key: test_precision -value: [0.54545455 0.47619048 0.52380952 0.57142857 0.63636364 0.57894737 - 0.4375 0.66666667 0.5 0.6875 ] - -mean value: 0.5623860788334472 - -key: train_precision -value: [0.63243243 0.6374269 0.64044944 0.65909091 0.65536723 0.64417178 - 0.64705882 0.64497041 0.63218391 0.65697674] - -mean value: 0.6450128581052524 - -key: test_recall -value: [0.66666667 0.55555556 0.61111111 0.66666667 0.73684211 0.57894737 - 0.36842105 0.63157895 0.55555556 0.61111111] - -mean value: 0.5982456140350877 - -key: train_recall -value: [0.70481928 0.65662651 0.68674699 0.69879518 0.7030303 0.63636364 - 0.66666667 0.66060606 0.6626506 0.68072289] - -mean value: 0.6757028112449799 - -key: test_accuracy -value: [0.56756757 0.48648649 0.54054054 0.59459459 0.64864865 0.56756757 - 0.43243243 0.64864865 0.5 0.66666667] - -mean value: 0.5653153153153153 - -key: train_accuracy -value: [0.64652568 0.64048338 0.64954683 0.66767372 0.66767372 0.64350453 - 0.65256798 0.64954683 0.63855422 0.6626506 ] - -mean value: 0.6518727477887382 - -key: test_roc_auc -value: [0.57017544 0.48830409 0.54239766 0.59649123 0.64619883 0.56725146 - 0.43421053 0.64912281 0.5 0.66666667] - -mean value: 0.5660818713450293 - -key: train_roc_auc -value: [0.64634903 0.64043447 0.6494341 0.66757941 0.66778021 0.64348302 - 0.65261044 0.64958014 0.63855422 0.6626506 ] - -mean value: 0.6518455640744797 - -key: test_jcc -value: [0.42857143 0.34482759 0.39285714 0.44444444 0.51851852 0.40740741 - 0.25 0.48 0.35714286 0.47826087] - -mean value: 0.4102030254713913 - -key: train_jcc -value: [0.5 0.47807018 0.49565217 0.51327434 0.51327434 0.47085202 - 0.48888889 0.48444444 0.47826087 0.50222222] - -mean value: 0.49249394649760053 - -key: TN -value: 98 - -mean value: 98.0 - -key: FP -value: 74 - -mean value: 74.0 - -key: FN -value: 86 - -mean value: 86.0 - -key: TP -value: 110 - -mean value: 110.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.17 - -Accuracy on Blind test: 0.58 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.00945854 0.00995374 0.00998521 0.00908518 0.00962687 0.0096693 - 0.00914121 0.00904846 0.01024342 0.00925326] - -mean value: 0.009546518325805664 - -key: score_time -value: [0.0121901 0.01287079 0.01248574 0.0124712 0.01203132 0.01259017 - 0.01387715 0.01182604 0.01560497 0.01178598] - -mean value: 0.012773346900939942 - -key: test_mcc -value: [ 0.13274856 0.24189738 0.1875299 0.29766651 0.35558302 0.18768409 - 0.35484024 -0.02109391 0.11111111 0.39440532] - -mean value: 0.224237222406324 - -key: train_mcc -value: [0.4806169 0.4803739 0.49859423 0.4683267 0.45692428 0.48046022 - 0.45014422 0.50458213 0.47591225 0.47018675] - -mean value: 0.4766121585270479 - -key: test_fscore -value: [0.52941176 0.58823529 0.54545455 0.60606061 0.71428571 0.61538462 - 0.66666667 0.42424242 0.55555556 0.71794872] - -mean value: 0.5963245904422375 - -key: train_fscore -value: [0.74556213 0.74251497 0.74772036 0.73333333 0.71875 0.73619632 - 0.72340426 0.75301205 0.73873874 0.73964497] - -mean value: 0.7378877129995635 - -key: test_precision -value: [0.5625 0.625 0.6 0.66666667 0.65217391 0.6 - 0.70588235 0.5 0.55555556 0.66666667] - -mean value: 0.6134445154873543 - -key: train_precision -value: [0.73255814 0.73809524 0.75460123 0.73780488 0.74193548 0.74534161 - 0.72560976 0.74850299 0.73652695 0.72674419] - -mean value: 0.7387720463714401 - -key: test_recall -value: [0.5 0.55555556 0.5 0.55555556 0.78947368 0.63157895 - 0.63157895 0.36842105 0.55555556 0.77777778] - -mean value: 0.5865497076023392 - -key: train_recall -value: [0.75903614 0.74698795 0.74096386 0.72891566 0.6969697 0.72727273 - 0.72121212 0.75757576 0.74096386 0.75301205] - -mean value: 0.7372909821102593 - -key: test_accuracy -value: [0.56756757 0.62162162 0.59459459 0.64864865 0.67567568 0.59459459 - 0.67567568 0.48648649 0.55555556 0.69444444] - -mean value: 0.6114864864864865 - -key: train_accuracy -value: [0.74018127 0.74018127 0.74924471 0.73413897 0.72809668 0.74018127 - 0.72507553 0.75226586 0.73795181 0.73493976] - -mean value: 0.7382257125177446 - -key: test_roc_auc -value: [0.56578947 0.61988304 0.59210526 0.64619883 0.67251462 0.59356725 - 0.67690058 0.48976608 0.55555556 0.69444444] - -mean value: 0.610672514619883 - -key: train_roc_auc -value: [0.74012413 0.74016064 0.74926981 0.7341548 0.72800292 0.74014239 - 0.72506389 0.75228185 0.73795181 0.73493976] - -mean value: 0.738209200438116 - -key: test_jcc -value: [0.36 0.41666667 0.375 0.43478261 0.55555556 0.44444444 - 0.5 0.26923077 0.38461538 0.56 ] - -mean value: 0.4300295429208473 - -key: train_jcc -value: [0.59433962 0.59047619 0.59708738 0.57894737 0.56097561 0.58252427 - 0.56666667 0.60386473 0.58571429 0.58685446] - -mean value: 0.5847450588554653 - -key: TN -value: 117 - -mean value: 117.0 - -key: FP -value: 76 - -mean value: 76.0 - -key: FN -value: 67 - -mean value: 67.0 - -key: TP -value: 108 - -mean value: 108.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.63 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.01642966 0.01525784 0.01508498 0.01526308 0.01534033 0.01553512 - 0.01536202 0.01535082 0.01552773 0.01546264] - -mean value: 0.015461421012878418 - -key: score_time -value: [0.01037836 0.01048398 0.01035833 0.01042557 0.01048684 0.01050973 - 0.01040649 0.01041794 0.01043272 0.01049089] - -mean value: 0.010439085960388183 - -key: test_mcc -value: [0.51319869 0.41299552 0.57857577 0.40469382 0.4670794 0.40469382 - 0.64788432 0.56725146 0.67082039 0.55901699] - -mean value: 0.522621018991172 - -key: train_mcc -value: [0.73419986 0.73503437 0.69185834 0.70405667 0.69242051 0.69185834 - 0.70393224 0.69794443 0.72294404 0.69278365] - -mean value: 0.706703246172192 - -key: test_fscore -value: [0.74285714 0.71794872 0.75 0.68571429 0.76190476 0.71794872 - 0.78787879 0.78947368 0.82352941 0.78947368] - -mean value: 0.7566729194438173 - -key: train_fscore -value: [0.86666667 0.86419753 0.84592145 0.85106383 0.84210526 0.84592145 - 0.85106383 0.84939759 0.86060606 0.84684685] - -mean value: 0.8523790518379695 - -key: test_precision -value: [0.76470588 0.66666667 0.85714286 0.70588235 0.69565217 0.7 - 0.92857143 0.78947368 0.875 0.75 ] - -mean value: 0.773309504579864 - -key: train_precision -value: [0.87195122 0.88607595 0.84848485 0.85889571 0.86075949 0.84337349 - 0.85365854 0.84431138 0.86585366 0.84431138] - -mean value: 0.8577675660145363 - -key: test_recall -value: [0.72222222 0.77777778 0.66666667 0.66666667 0.84210526 0.73684211 - 0.68421053 0.78947368 0.77777778 0.83333333] - -mean value: 0.7497076023391813 - -key: train_recall -value: [0.86144578 0.84337349 0.84337349 0.84337349 0.82424242 0.84848485 - 0.84848485 0.85454545 0.85542169 0.84939759] - -mean value: 0.8472143117926251 - -key: test_accuracy -value: [0.75675676 0.7027027 0.78378378 0.7027027 0.72972973 0.7027027 - 0.81081081 0.78378378 0.83333333 0.77777778] - -mean value: 0.7584084084084084 - -key: train_accuracy -value: [0.86706949 0.86706949 0.84592145 0.85196375 0.84592145 0.84592145 - 0.85196375 0.8489426 0.86144578 0.84638554] - -mean value: 0.8532604739198485 - -key: test_roc_auc -value: [0.75584795 0.70467836 0.78070175 0.70175439 0.72660819 0.70175439 - 0.81432749 0.78362573 0.83333333 0.77777778] - -mean value: 0.7580409356725146 - -key: train_roc_auc -value: [0.86708653 0.86714129 0.84592917 0.85198978 0.84585615 0.84592917 - 0.85195327 0.84895947 0.86144578 0.84638554] - -mean value: 0.8532676159182184 - -key: test_jcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( - -value: [0.59090909 0.56 0.6 0.52173913 0.61538462 0.56 - 0.65 0.65217391 0.7 0.65217391] - -mean value: 0.6102380662815446 - -key: train_jcc -value: [0.76470588 0.76086957 0.73298429 0.74074074 0.72727273 0.73298429 - 0.74074074 0.7382199 0.75531915 0.734375 ] - -mean value: 0.7428212286936103 - -key: TN -value: 141 - -mean value: 141.0 - -key: FP -value: 46 - -mean value: 46.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 138 - -mean value: 138.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.25266194 1.37133646 1.23652482 1.33965015 1.22840881 1.38763452 - 1.21685147 1.36628342 1.79508758 1.22440267] - -mean value: 1.341884183883667 - -key: score_time -value: [0.01284289 0.01348376 0.01358151 0.01387429 0.01508641 0.01499629 - 0.01510262 0.01513815 0.01270747 0.01256514] - -mean value: 0.013937854766845703 - -key: test_mcc -value: [0.68035483 0.56725146 0.64287856 0.89736456 0.57857577 0.62280702 - 0.68035483 0.83871328 0.61977979 0.9459053 ] - -mean value: 0.7073985409475353 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.84210526 0.77777778 0.77419355 0.94736842 0.80952381 0.81081081 - 0.83333333 0.92307692 0.78787879 0.97297297] - -mean value: 0.8479041647972039 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.8 0.77777778 0.92307692 0.9 0.73913043 0.83333333 - 0.88235294 0.9 0.86666667 0.94736842] - -mean value: 0.8569706497866413 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.88888889 0.77777778 0.66666667 1. 0.89473684 0.78947368 - 0.78947368 0.94736842 0.72222222 1. ] - -mean value: 0.8476608187134502 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.83783784 0.78378378 0.81081081 0.94594595 0.78378378 0.81081081 - 0.83783784 0.91891892 0.80555556 0.97222222] - -mean value: 0.8507507507507507 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.83918129 0.78362573 0.80701754 0.94736842 0.78070175 0.81140351 - 0.83918129 0.91812865 0.80555556 0.97222222] - -mean value: 0.8504385964912279 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.72727273 0.63636364 0.63157895 0.9 0.68 0.68181818 - 0.71428571 0.85714286 0.65 0.94736842] - -mean value: 0.742583048530417 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 157 - -mean value: 157.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 156 - -mean value: 156.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.62 - -Accuracy on Blind test: 0.81 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02364254 0.01857662 0.01500845 0.01494932 0.01587224 0.01571488 - 0.01555252 0.01523328 0.01550579 0.01587558] - -mean value: 0.016593122482299806 - -key: score_time -value: [0.01221228 0.00938725 0.01087761 0.00869012 0.0089097 0.00933743 - 0.00860524 0.00942445 0.00863528 0.00884032] - -mean value: 0.009491968154907226 - -key: test_mcc -value: [0.83918129 0.94721815 0.84834956 0.80369958 0.83871328 0.89736456 - 0.89181287 0.94721815 0.84515425 1. ] - -mean value: 0.8858711687463412 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.91891892 0.97142857 0.90909091 0.9 0.92307692 0.94444444 - 0.94736842 0.97435897 0.90909091 1. ] - -mean value: 0.9397778071462282 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.89473684 1. 1. 0.81818182 0.9 1. - 0.94736842 0.95 1. 1. ] - -mean value: 0.9510287081339713 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94444444 0.94444444 0.83333333 1. 0.94736842 0.89473684 - 0.94736842 1. 0.83333333 1. ] - -mean value: 0.9345029239766083 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.91891892 0.97297297 0.91891892 0.89189189 0.91891892 0.94594595 - 0.94594595 0.97297297 0.91666667 1. ] - -mean value: 0.9403153153153154 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.91959064 0.97222222 0.91666667 0.89473684 0.91812865 0.94736842 - 0.94590643 0.97222222 0.91666667 1. ] - -mean value: 0.9403508771929824 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85 0.94444444 0.83333333 0.81818182 0.85714286 0.89473684 - 0.9 0.95 0.83333333 1. ] - -mean value: 0.8881172628541047 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 174 - -mean value: 174.0 - -key: FP -value: 12 - -mean value: 12.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 172 - -mean value: 172.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.93 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.11738157 0.11824512 0.12080574 0.11937785 0.1163404 0.11580873 - 0.11400318 0.11562872 0.12440085 0.11563945] - -mean value: 0.11776316165924072 - -key: score_time -value: [0.01932335 0.01833892 0.01881862 0.01959372 0.01893997 0.01794648 - 0.01798677 0.01850343 0.01868439 0.01839876] - -mean value: 0.018653440475463866 - -key: test_mcc -value: [0.57184997 0.73099415 0.62280702 0.68035483 0.51793973 0.51461988 - 0.64788432 0.7163504 0.66666667 0.66666667] - -mean value: 0.6336133634918939 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.78947368 0.86486486 0.81081081 0.84210526 0.7804878 0.75675676 - 0.78787879 0.8125 0.83333333 0.83333333] - -mean value: 0.8111544639224357 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.75 0.84210526 0.78947368 0.8 0.72727273 0.77777778 - 0.92857143 1. 0.83333333 0.83333333] - -mean value: 0.8281867547657022 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.83333333 0.88888889 0.83333333 0.88888889 0.84210526 0.73684211 - 0.68421053 0.68421053 0.83333333 0.83333333] - -mean value: 0.8058479532163743 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.78378378 0.86486486 0.81081081 0.83783784 0.75675676 0.75675676 - 0.81081081 0.83783784 0.83333333 0.83333333] - -mean value: 0.8126126126126125 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.78508772 0.86549708 0.81140351 0.83918129 0.75438596 0.75730994 - 0.81432749 0.84210526 0.83333333 0.83333333] - -mean value: 0.81359649122807 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.65217391 0.76190476 0.68181818 0.72727273 0.64 0.60869565 - 0.65 0.68421053 0.71428571 0.71428571] - -mean value: 0.683464719110028 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 151 - -mean value: 151.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 33 - -mean value: 33.0 - -key: TP -value: 148 - -mean value: 148.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.79 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.00977969 0.01005006 0.01088262 0.00968432 0.00976276 0.00972199 - 0.00968623 0.00974798 0.00991845 0.00975585] - -mean value: 0.009898996353149414 - -key: score_time -value: [0.00873685 0.00901937 0.00925207 0.00900435 0.00882649 0.00879073 - 0.00877666 0.00888586 0.00864649 0.00883532] - -mean value: 0.008877420425415039 - -key: test_mcc -value: [0.35104619 0.13450292 0.24269006 0.07739329 0.51461988 0.18768409 - 0.26327408 0.20189884 0.4472136 0.17349448] - -mean value: 0.25938174334208364 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.64705882 0.55555556 0.61111111 0.48484848 0.75675676 0.61538462 - 0.5625 0.54545455 0.70588235 0.63414634] - -mean value: 0.6118698587045073 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.6875 0.55555556 0.61111111 0.53333333 0.77777778 0.6 - 0.69230769 0.64285714 0.75 0.56521739] - -mean value: 0.6415660004246961 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.61111111 0.55555556 0.61111111 0.44444444 0.73684211 0.63157895 - 0.47368421 0.47368421 0.66666667 0.72222222] - -mean value: 0.5926900584795322 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.67567568 0.56756757 0.62162162 0.54054054 0.75675676 0.59459459 - 0.62162162 0.59459459 0.72222222 0.58333333] - -mean value: 0.6278528528528529 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.67397661 0.56725146 0.62134503 0.5380117 0.75730994 0.59356725 - 0.62573099 0.59795322 0.72222222 0.58333333] - -mean value: 0.6280701754385964 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.47826087 0.38461538 0.44 0.32 0.60869565 0.44444444 - 0.39130435 0.375 0.54545455 0.46428571] - -mean value: 0.4452060958365306 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 122 - -mean value: 122.0 - -key: FP -value: 75 - -mean value: 75.0 - -key: FN -value: 62 - -mean value: 62.0 - -key: TP -value: 109 - -mean value: 109.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.5 - -Accuracy on Blind test: 0.75 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -[1.54283714 1.52550173 1.54197979 1.54365993 1.56115317 1.5545454 - 1.52332783 1.58352304 1.57969499 1.55690646] - -mean value: 1.5513129472732543 - -key: score_time -value: [0.09255362 0.09796834 0.09852648 0.09795308 0.09718537 0.09177136 - 0.09100032 0.09966326 0.09207082 0.09281731] - -mean value: 0.0951509952545166 - -key: test_mcc -value: [0.83918129 0.78764146 0.89679028 0.84959079 0.83871328 0.83918129 - 0.89736456 0.7888597 0.89442719 0.88888889] - -mean value: 0.8520638727624419 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.91891892 0.88235294 0.94117647 0.92307692 0.92307692 0.91891892 - 0.94444444 0.88888889 0.94117647 0.94444444] - -mean value: 0.9226475344122405 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.89473684 0.9375 1. 0.85714286 0.9 0.94444444 - 1. 0.94117647 1. 0.94444444] - -mean value: 0.9419445058725244 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94444444 0.83333333 0.88888889 1. 0.94736842 0.89473684 - 0.89473684 0.84210526 0.88888889 0.94444444] - -mean value: 0.9078947368421053 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.91891892 0.89189189 0.94594595 0.91891892 0.91891892 0.91891892 - 0.94594595 0.89189189 0.94444444 0.94444444] - -mean value: 0.9240240240240241 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.91959064 0.89035088 0.94444444 0.92105263 0.91812865 0.91959064 - 0.94736842 0.89327485 0.94444444 0.94444444] - -mean value: 0.9242690058479532 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85 0.78947368 0.88888889 0.85714286 0.85714286 0.85 - 0.89473684 0.8 0.88888889 0.89473684] - -mean value: 0.8571010860484545 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 173 - -mean value: 173.0 - -key: FP -value: 17 - -mean value: 17.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 167 - -mean value: 167.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.93 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.87559628 0.89195251 0.94412994 0.93624926 0.94331312 0.89627552 - 0.91266012 0.89829779 0.91013336 0.91155267] - -mean value: 0.9120160579681397 - -key: score_time -value: [0.19535828 0.17941213 0.18909216 0.23338938 0.15893197 0.17293549 - 0.26095557 0.20606256 0.20709252 0.20674348] - -mean value: 0.20099735260009766 - -key: test_mcc -value: [0.78362573 0.73099415 0.94721815 0.7888597 0.83871328 0.83918129 - 0.80369958 0.75938069 0.83462233 0.88888889] - -mean value: 0.8215183788666804 - -key: train_mcc -value: [0.9698065 0.96994925 0.97590274 0.96381759 0.9758306 0.9698054 - 0.9698065 0.9640249 0.97590361 0.96385542] - -mean value: 0.9698702504396571 - -key: test_fscore -value: [0.88888889 0.86486486 0.97142857 0.89473684 0.92307692 0.91891892 - 0.88235294 0.84848485 0.91428571 0.94444444] - -mean value: 0.9051482957674908 - -key: train_fscore -value: [0.98489426 0.98480243 0.98787879 0.98181818 0.98787879 0.98480243 - 0.98489426 0.98159509 0.98795181 0.98192771] - -mean value: 0.9848443750531934 - -key: test_precision -value: [0.88888889 0.84210526 1. 0.85 0.9 0.94444444 - 1. 1. 0.94117647 0.94444444] - -mean value: 0.9311059511523908 - -key: train_precision -value: [0.98787879 0.99386503 0.99390244 0.98780488 0.98787879 0.98780488 - 0.98192771 0.99378882 0.98795181 0.98192771] - -mean value: 0.9884730850345813 - -key: test_recall -value: [0.88888889 0.88888889 0.94444444 0.94444444 0.94736842 0.89473684 - 0.78947368 0.73684211 0.88888889 0.94444444] - -mean value: 0.8868421052631579 - -key: train_recall -value: [0.98192771 0.97590361 0.98192771 0.97590361 0.98787879 0.98181818 - 0.98787879 0.96969697 0.98795181 0.98192771] - -mean value: 0.9812814895947426 - -key: test_accuracy -value: [0.89189189 0.86486486 0.97297297 0.89189189 0.91891892 0.91891892 - 0.89189189 0.86486486 0.91666667 0.94444444] - -mean value: 0.9077327327327328 - -key: train_accuracy -value: [0.98489426 0.98489426 0.98791541 0.98187311 0.98791541 0.98489426 - 0.98489426 0.98187311 0.98795181 0.98192771] - -mean value: 0.9849033596622139 - -key: test_roc_auc -value: [0.89181287 0.86549708 0.97222222 0.89327485 0.91812865 0.91959064 - 0.89473684 0.86842105 0.91666667 0.94444444] - -mean value: 0.9084795321637426 - -key: train_roc_auc -value: [0.98490325 0.9849215 0.98793355 0.9818912 0.9879153 0.98488499 - 0.98490325 0.98183644 0.98795181 0.98192771] - -mean value: 0.9849069003285871 - -key: test_jcc -value: [0.8 0.76190476 0.94444444 0.80952381 0.85714286 0.85 - 0.78947368 0.73684211 0.84210526 0.89473684] - -mean value: 0.8286173767752715 - -key: train_jcc -value: [0.9702381 0.97005988 0.9760479 0.96428571 0.9760479 0.97005988 - 0.9702381 0.96385542 0.97619048 0.96449704] - -mean value: 0.9701520412921522 - -key: TN -value: 171 - -mean value: 171.0 - -key: FP -value: 21 - -mean value: 21.0 - -key: FN -value: 13 - -mean value: 13.0 - -key: TP -value: 163 - -mean value: 163.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.84 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.07352471 0.0521996 0.0538013 0.06291175 0.05890298 0.07934737 - 0.05688357 0.06139588 0.05790401 0.06046128] - -mean value: 0.061733245849609375 - -key: score_time -value: [0.01049018 0.01037455 0.01051855 0.01058316 0.01059937 0.01265669 - 0.0104084 0.01048875 0.01073813 0.01055479] - -mean value: 0.010741257667541504 - -key: test_mcc -value: [0.94736842 0.94721815 0.94721815 0.89736456 0.89181287 0.89736456 - 0.89181287 0.94721815 0.9459053 1. ] - -mean value: 0.9313283027498622 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97297297 0.97142857 0.97142857 0.94736842 0.94736842 0.94444444 - 0.94736842 0.97435897 0.97142857 1. ] - -mean value: 0.9648167369220001 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.94736842 1. 1. 0.9 0.94736842 1. - 0.94736842 0.95 1. 1. ] - -mean value: 0.9692105263157895 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94444444 0.94444444 1. 0.94736842 0.89473684 - 0.94736842 1. 0.94444444 1. ] - -mean value: 0.962280701754386 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97297297 0.97297297 0.97297297 0.94594595 0.94594595 0.94594595 - 0.94594595 0.97297297 0.97222222 1. ] - -mean value: 0.9647897897897897 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.97222222 0.97222222 0.94736842 0.94590643 0.94736842 - 0.94590643 0.97222222 0.97222222 1. ] - -mean value: 0.9649122807017543 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.94736842 0.94444444 0.94444444 0.9 0.9 0.89473684 - 0.9 0.95 0.94444444 1. ] - -mean value: 0.9325438596491228 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 178 - -mean value: 178.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 6 - -mean value: 6.0 - -key: TP -value: 177 - -mean value: 177.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.03493357 0.06719422 0.06436968 0.06092381 0.04703569 0.06343341 - 0.04682136 0.05608273 0.04545498 0.04727387] - -mean value: 0.05335233211517334 - -key: score_time -value: [0.02156854 0.02076554 0.02375484 0.02417326 0.02394223 0.0216763 - 0.0191443 0.01248741 0.02173638 0.01255107] - -mean value: 0.02017998695373535 - -key: test_mcc -value: [0.78362573 0.67849265 0.57857577 0.57184997 0.69356297 0.52960948 - 0.74044197 0.7888597 0.67082039 0.66666667] - -mean value: 0.670250529405086 - -key: train_mcc -value: [0.94020105 0.9581447 0.94702868 0.95166119 0.95785863 0.93368393 - 0.95785863 0.94645909 0.93982725 0.93982725] - -mean value: 0.9472550384640911 - -key: test_fscore -value: [0.88888889 0.82352941 0.75 0.78947368 0.82352941 0.79069767 - 0.85714286 0.88888889 0.84210526 0.83333333] - -mean value: 0.8287589413570405 - -key: train_fscore -value: [0.96932515 0.97859327 0.97213622 0.97590361 0.97859327 0.96636086 - 0.97859327 0.97213622 0.96969697 0.96969697] - -mean value: 0.973103582582931 - -key: test_precision -value: [0.88888889 0.875 0.85714286 0.75 0.93333333 0.70833333 - 0.9375 0.94117647 0.8 0.83333333] - -mean value: 0.8524708216619981 - -key: train_precision -value: [0.9875 0.99378882 1. 0.97590361 0.98765432 0.97530864 - 0.98765432 0.99367089 0.97560976 0.97560976] - -mean value: 0.9852700116555297 - -key: test_recall -value: [0.88888889 0.77777778 0.66666667 0.83333333 0.73684211 0.89473684 - 0.78947368 0.84210526 0.88888889 0.83333333] - -mean value: 0.8152046783625732 - -key: train_recall -value: [0.95180723 0.96385542 0.94578313 0.97590361 0.96969697 0.95757576 - 0.96969697 0.95151515 0.96385542 0.96385542] - -mean value: 0.9613545089448703 - -key: test_accuracy -value: [0.89189189 0.83783784 0.78378378 0.78378378 0.83783784 0.75675676 - 0.86486486 0.89189189 0.83333333 0.83333333] - -mean value: 0.8315315315315315 - -key: train_accuracy -value: [0.96978852 0.97885196 0.97280967 0.97583082 0.97885196 0.96676737 - 0.97885196 0.97280967 0.96987952 0.96987952] - -mean value: 0.9734320969679322 - -key: test_roc_auc -value: [0.89181287 0.83625731 0.78070175 0.78508772 0.84064327 0.75292398 - 0.86695906 0.89327485 0.83333333 0.83333333] - -mean value: 0.8314327485380117 - -key: train_roc_auc -value: [0.96984301 0.97889741 0.97289157 0.9758306 0.97882439 0.96673969 - 0.97882439 0.97274553 0.96987952 0.96987952] - -mean value: 0.9734355604235123 - -key: test_jcc -value: [0.8 0.7 0.6 0.65217391 0.7 0.65384615 - 0.75 0.8 0.72727273 0.71428571] - -mean value: 0.7097578508448075 - -key: train_jcc -value: [0.94047619 0.95808383 0.94578313 0.95294118 0.95808383 0.93491124 - 0.95808383 0.94578313 0.94117647 0.94117647] - -mean value: 0.947649931279303 - -key: TN -value: 156 - -mean value: 156.0 - -key: FP -value: 34 - -mean value: 34.0 - -key: FN -value: 28 - -mean value: 28.0 - -key: TP -value: 150 - -mean value: 150.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.61 - -Accuracy on Blind test: 0.8 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.02297425 0.00965118 0.00942397 0.00989389 0.00917554 0.00920296 - 0.00916243 0.00908923 0.00926065 0.00941253] - -mean value: 0.010724663734436035 - -key: score_time -value: [0.01208115 0.00897098 0.00965118 0.00850368 0.00844884 0.00861883 - 0.0085237 0.00866818 0.00850368 0.00876546] - -mean value: 0.009073567390441895 - -key: test_mcc -value: [ 0.19005848 -0.02631579 0.24633537 0.40643275 0.35104619 0.4633451 - 0.1378305 0.35484024 0.3354102 0.16903085] - -mean value: 0.2628013891938257 - -key: train_mcc -value: [0.28095221 0.28096699 0.29306316 0.29303735 0.3051417 0.27494523 - 0.30521781 0.31722905 0.3072791 0.27120739] - -mean value: 0.292903998727056 - -key: test_fscore -value: [0.59459459 0.48648649 0.63157895 0.7027027 0.7 0.72222222 - 0.55555556 0.66666667 0.64705882 0.61538462] - -mean value: 0.6322250614510676 - -key: train_fscore -value: [0.64264264 0.64477612 0.64652568 0.64864865 0.64831804 0.63190184 - 0.65465465 0.65443425 0.65671642 0.64094955] - -mean value: 0.6469567851982608 - -key: test_precision -value: [0.57894737 0.47368421 0.6 0.68421053 0.66666667 0.76470588 - 0.58823529 0.70588235 0.6875 0.57142857] - -mean value: 0.632126087277016 - -key: train_precision -value: [0.64071856 0.63905325 0.64848485 0.64670659 0.65432099 0.63975155 - 0.64880952 0.66049383 0.65088757 0.63157895] - -mean value: 0.6460805665375606 - -key: test_recall -value: [0.61111111 0.5 0.66666667 0.72222222 0.73684211 0.68421053 - 0.52631579 0.63157895 0.61111111 0.66666667] - -mean value: 0.6356725146198831 - -key: train_recall -value: [0.64457831 0.65060241 0.64457831 0.65060241 0.64242424 0.62424242 - 0.66060606 0.64848485 0.6626506 0.65060241] - -mean value: 0.6479372033588902 - -key: test_accuracy -value: [0.59459459 0.48648649 0.62162162 0.7027027 0.67567568 0.72972973 - 0.56756757 0.67567568 0.66666667 0.58333333] - -mean value: 0.6304054054054054 - -key: train_accuracy -value: [0.64048338 0.64048338 0.64652568 0.64652568 0.65256798 0.63746224 - 0.65256798 0.65861027 0.65361446 0.63554217] - -mean value: 0.6464383212608743 - -key: test_roc_auc -value: [0.59502924 0.48684211 0.62280702 0.70321637 0.67397661 0.73099415 - 0.56871345 0.67690058 0.66666667 0.58333333] - -mean value: 0.6308479532163742 - -key: train_roc_auc -value: [0.64047097 0.64045272 0.64653158 0.64651333 0.65253742 0.63742242 - 0.65259219 0.65857977 0.65361446 0.63554217] - -mean value: 0.6464257028112449 - -key: test_jcc -value: [0.42307692 0.32142857 0.46153846 0.54166667 0.53846154 0.56521739 - 0.38461538 0.5 0.47826087 0.44444444] - -mean value: 0.46587102511015555 - -key: train_jcc -value: [0.47345133 0.47577093 0.47767857 0.48 0.47963801 0.46188341 - 0.48660714 0.48636364 0.48888889 0.47161572] - -mean value: 0.478189762972754 - -key: TN -value: 115 - -mean value: 115.0 - -key: FP -value: 67 - -mean value: 67.0 - -key: FN -value: 69 - -mean value: 69.0 - -key: TP -value: 117 - -mean value: 117.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.25 - -Accuracy on Blind test: 0.62 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01696587 0.01653218 0.02142096 0.01792455 0.02694225 0.02063346 - 0.02318788 0.02602243 0.02007818 0.02081442] - -mean value: 0.021052217483520506 - -key: score_time -value: [0.00851464 0.01105928 0.01146054 0.01190424 0.0118134 0.01180387 - 0.01229978 0.01188207 0.01183915 0.01179409] - -mean value: 0.011437106132507324 - -key: test_mcc -value: [0.80369958 0.53638795 0.75614764 0.75938069 0.62807634 0.40611643 - 0.74044197 0.73099415 0.84515425 1. ] - -mean value: 0.7206399018197679 - -key: train_mcc -value: [0.92917693 0.84682202 0.91220349 0.81803466 0.95785863 0.66067017 - 0.92280249 0.9640249 0.83118654 0.90598477] - -mean value: 0.8748764596532459 - -key: test_fscore -value: [0.9 0.7804878 0.83870968 0.87804878 0.82926829 0.74509804 - 0.85714286 0.86486486 0.90909091 1. ] - -mean value: 0.8602711225782453 - -key: train_fscore -value: [0.96491228 0.92528736 0.95297806 0.91168091 0.97859327 0.83544304 - 0.95950156 0.98159509 0.90131579 0.95 ] - -mean value: 0.9361307354407398 - -key: test_precision -value: [0.81818182 0.69565217 1. 0.7826087 0.77272727 0.59375 - 0.9375 0.88888889 1. 1. ] - -mean value: 0.8489308849363197 - -key: train_precision -value: [0.9375 0.88461538 0.99346405 0.86486486 0.98765432 0.7173913 - 0.98717949 0.99378882 0.99275362 0.98701299] - -mean value: 0.9346224844359968 - -key: test_recall -value: [1. 0.88888889 0.72222222 1. 0.89473684 1. - 0.78947368 0.84210526 0.83333333 1. ] - -mean value: 0.8970760233918128 - -key: train_recall -value: [0.9939759 0.96987952 0.91566265 0.96385542 0.96969697 1. - 0.93333333 0.96969697 0.8253012 0.91566265] - -mean value: 0.9457064622124864 - -key: test_accuracy -value: [0.89189189 0.75675676 0.86486486 0.86486486 0.81081081 0.64864865 - 0.86486486 0.86486486 0.91666667 1. ] - -mean value: 0.8484234234234236 - -key: train_accuracy -value: [0.96374622 0.92145015 0.95468278 0.90634441 0.97885196 0.80362538 - 0.96072508 0.98187311 0.90963855 0.95180723] - -mean value: 0.9332744876788119 - -key: test_roc_auc -value: [0.89473684 0.76023392 0.86111111 0.86842105 0.80847953 0.63888889 - 0.86695906 0.86549708 0.91666667 1. ] - -mean value: 0.8480994152046784 - -key: train_roc_auc -value: [0.96365462 0.9213034 0.95480102 0.90617014 0.97882439 0.80421687 - 0.96064257 0.98183644 0.90963855 0.95180723] - -mean value: 0.9332895217232566 - -key: test_jcc -value: [0.81818182 0.64 0.72222222 0.7826087 0.70833333 0.59375 - 0.75 0.76190476 0.83333333 1. ] - -mean value: 0.7610334164627642 - -key: train_jcc -value: [0.93220339 0.86096257 0.91017964 0.83769634 0.95808383 0.7173913 - 0.92215569 0.96385542 0.82035928 0.9047619 ] - -mean value: 0.8827649365664213 - -key: TN -value: 147 - -mean value: 147.0 - -key: FP -value: 19 - -mean value: 19.0 - -key: FN -value: 37 - -mean value: 37.0 - -key: TP -value: 165 - -mean value: 165.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.77 - -Accuracy on Blind test: 0.89 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01752663 0.0168252 0.01800752 0.0179131 0.01694775 0.01577258 - 0.01671886 0.01936817 0.01759195 0.0189786 ] - -mean value: 0.017565035820007326 - -key: score_time -value: [0.01165342 0.01172328 0.01174808 0.01176715 0.01184464 0.01189899 - 0.01180577 0.01190758 0.0119617 0.01174545] - -mean value: 0.0118056058883667 - -key: test_mcc -value: [0.75614764 0.56725146 0.84834956 0.51121719 0.54996161 0.64788432 - 0.7888597 0.67849265 0.57735027 0.24253563] - -mean value: 0.6168050021841505 - -key: train_mcc -value: [0.81711118 0.92160515 0.93396646 0.6851384 0.79932028 0.90202253 - 0.8976805 0.88443588 0.76465394 0.36483668] - -mean value: 0.7970771000849938 - -key: test_fscore -value: [0.83870968 0.77777778 0.90909091 0.76595745 0.8 0.78787879 - 0.88888889 0.85 0.66666667 0.69230769] - -mean value: 0.7977277846838589 - -key: train_fscore -value: [0.88963211 0.96048632 0.96636086 0.84832905 0.90196078 0.9456869 - 0.94769231 0.94252874 0.85223368 0.72331155] - -mean value: 0.8978222286737749 - -key: test_precision -value: [1. 0.77777778 1. 0.62068966 0.69230769 0.92857143 - 0.94117647 0.80952381 1. 0.52941176] - -mean value: 0.829945859864724 - -key: train_precision -value: [1. 0.96932515 0.98136646 0.73991031 0.83854167 1. - 0.9625 0.89617486 0.992 0.5665529 ] - -mean value: 0.8946371357981441 - -key: test_recall -value: [0.72222222 0.77777778 0.83333333 1. 0.94736842 0.68421053 - 0.84210526 0.89473684 0.5 1. ] - -mean value: 0.8201754385964912 - -key: train_recall -value: [0.80120482 0.95180723 0.95180723 0.9939759 0.97575758 0.8969697 - 0.93333333 0.99393939 0.74698795 1. ] - -mean value: 0.924578313253012 - -key: test_accuracy -value: [0.86486486 0.78378378 0.91891892 0.7027027 0.75675676 0.81081081 - 0.89189189 0.83783784 0.75 0.55555556] - -mean value: 0.7873123123123122 - -key: train_accuracy -value: [0.90030211 0.96072508 0.96676737 0.82175227 0.89425982 0.94864048 - 0.94864048 0.93957704 0.87048193 0.61746988] - -mean value: 0.8868616459796892 - -key: test_roc_auc -value: [0.86111111 0.78362573 0.91666667 0.71052632 0.75146199 0.81432749 - 0.89327485 0.83625731 0.75 0.55555556] - -mean value: 0.7872807017543859 - -key: train_roc_auc -value: [0.90060241 0.9607521 0.96681271 0.82123038 0.89450529 0.94848485 - 0.94859438 0.93974078 0.87048193 0.61746988] - -mean value: 0.886867469879518 - -key: test_jcc -value: [0.72222222 0.63636364 0.83333333 0.62068966 0.66666667 0.65 - 0.8 0.73913043 0.5 0.52941176] - -mean value: 0.6697817713246763 - -key: train_jcc -value: [0.80120482 0.92397661 0.93491124 0.73660714 0.82142857 0.8969697 - 0.9005848 0.89130435 0.74251497 0.5665529 ] - -mean value: 0.8216055095554701 - -key: TN -value: 139 - -mean value: 139.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 45 - -mean value: 45.0 - -key: TP -value: 151 - -mean value: 151.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.46 - -Accuracy on Blind test: 0.68 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.15993071 0.14160872 0.14433384 0.14331055 0.14333391 0.1441431 - 0.14368558 0.14483571 0.1482718 0.14381099] - -mean value: 0.14572649002075194 - -key: score_time -value: [0.01509786 0.01506662 0.01515865 0.01495218 0.01499963 0.01516867 - 0.01496482 0.01519132 0.01555467 0.01488686] - -mean value: 0.015104126930236817 - -key: test_mcc -value: [0.94736842 0.94721815 0.94721815 0.89736456 0.89181287 0.83918129 - 0.89181287 0.94721815 0.9459053 1. ] - -mean value: 0.9255099751624151 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97297297 0.97142857 0.97142857 0.94736842 0.94736842 0.91891892 - 0.94736842 0.97435897 0.97142857 1. ] - -mean value: 0.9622641843694476 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.94736842 1. 1. 0.9 0.94736842 0.94444444 - 0.94736842 0.95 1. 1. ] - -mean value: 0.9636549707602338 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [1. 0.94444444 0.94444444 1. 0.94736842 0.89473684 - 0.94736842 1. 0.94444444 1. ] - -mean value: 0.962280701754386 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97297297 0.97297297 0.97297297 0.94594595 0.94594595 0.91891892 - 0.94594595 0.97297297 0.97222222 1. ] - -mean value: 0.962087087087087 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.97222222 0.97222222 0.94736842 0.94590643 0.91959064 - 0.94590643 0.97222222 0.97222222 1. ] - -mean value: 0.9621345029239766 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.94736842 0.94444444 0.94444444 0.9 0.9 0.85 - 0.9 0.95 0.94444444 1. ] - -mean value: 0.9280701754385966 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 7 - -mean value: 7.0 - -key: FN -value: 7 - -mean value: 7.0 - -key: TP -value: 177 - -mean value: 177.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03742456 0.04136753 0.04468369 0.05902505 0.04643774 0.04361725 - 0.04994798 0.05693603 0.04081082 0.05285883] - -mean value: 0.04731094837188721 - -key: score_time -value: [0.02205467 0.01702189 0.01927042 0.02232933 0.02413177 0.01916099 - 0.02937841 0.02085304 0.02483249 0.02223158] - -mean value: 0.02212646007537842 - -key: test_mcc -value: [0.94736842 0.94721815 0.94721815 0.89736456 0.83871328 0.84959079 - 0.89181287 0.94721815 0.84515425 0.9459053 ] - -mean value: 0.9057563928625993 - -key: train_mcc -value: [0.98798788 0.98203528 0.96381759 0.9879153 0.99397568 0.98798701 - 0.98189054 0.98798701 0.98795181 0.98802352] - -mean value: 0.984957161269568 - -key: test_fscore -value: [0.97297297 0.97142857 0.97142857 0.94736842 0.92307692 0.91428571 - 0.94736842 0.97435897 0.90909091 0.97297297] - -mean value: 0.9504352451720873 - -key: train_fscore -value: [0.99393939 0.99088146 0.98181818 0.9939759 0.99696049 0.99390244 - 0.99088146 0.99390244 0.9939759 0.99393939] - -mean value: 0.9924177059229986 - -key: test_precision -value: [0.94736842 1. 1. 0.9 0.9 1. - 0.94736842 0.95 1. 0.94736842] - -mean value: 0.9592105263157895 - -key: train_precision -value: [1. 1. 0.98780488 0.9939759 1. 1. - 0.99390244 1. 0.9939759 1. ] - -mean value: 0.9969659124302087 - -key: test_recall -value: [1. 0.94444444 0.94444444 1. 0.94736842 0.84210526 - 0.94736842 1. 0.83333333 1. ] - -mean value: 0.945906432748538 - -key: train_recall -value: [0.98795181 0.98192771 0.97590361 0.9939759 0.99393939 0.98787879 - 0.98787879 0.98787879 0.9939759 0.98795181] - -mean value: 0.9879262504563711 - -key: test_accuracy -value: [0.97297297 0.97297297 0.97297297 0.94594595 0.91891892 0.91891892 - 0.94594595 0.97297297 0.91666667 0.97222222] - -mean value: 0.951051051051051 - -key: train_accuracy -value: [0.9939577 0.99093656 0.98187311 0.9939577 0.99697885 0.9939577 - 0.99093656 0.9939577 0.9939759 0.9939759 ] - -mean value: 0.9924507698467588 - -key: test_roc_auc -value: [0.97368421 0.97222222 0.97222222 0.94736842 0.91812865 0.92105263 - 0.94590643 0.97222222 0.91666667 0.97222222] - -mean value: 0.9511695906432747 - -key: train_roc_auc -value: [0.9939759 0.99096386 0.9818912 0.99395765 0.9969697 0.99393939 - 0.99092735 0.99393939 0.9939759 0.9939759 ] - -mean value: 0.9924516246805404 - -key: test_jcc -value: [0.94736842 0.94444444 0.94444444 0.9 0.85714286 0.84210526 - 0.9 0.95 0.83333333 0.94736842] - -mean value: 0.9066207184628239 - -key: train_jcc -value: [0.98795181 0.98192771 0.96428571 0.98802395 0.99393939 0.98787879 - 0.98192771 0.98787879 0.98802395 0.98795181] - -mean value: 0.9849789624318879 - -key: TN -value: 176 - -mean value: 176.0 - -key: FP -value: 10 - -mean value: 10.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 174 - -mean value: 174.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.97 - -Running classifier: 20 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.08728218 0.10813236 0.0958004 0.06399155 0.18595147 0.12720704 - 0.17332816 0.12214851 0.13063812 0.06246138] - -mean value: 0.11569411754608154 - -key: score_time -value: [0.02227879 0.01623034 0.01406312 0.01530313 0.04132891 0.02922869 - 0.03126192 0.0268054 0.02188969 0.0137043 ] - -mean value: 0.023209428787231444 - -key: test_mcc -value: [0.13274856 0.40643275 0.24189738 0.1378305 0.35104619 0.18768409 - 0.45906433 0.14287993 0.16692447 0.3354102 ] - -mean value: 0.2561918392857961 - -key: train_mcc -value: [0.9640249 0.91547702 0.92146482 0.92749179 0.92756216 0.92749179 - 0.93355239 0.92146769 0.92777818 0.92170347] - -mean value: 0.9288014205507521 - -key: test_fscore -value: [0.52941176 0.7027027 0.58823529 0.57894737 0.7 0.61538462 - 0.73684211 0.52941176 0.59459459 0.68421053] - -mean value: 0.6259740736211324 - -key: train_fscore -value: [0.98214286 0.95757576 0.96096096 0.96385542 0.96385542 0.96363636 - 0.96676737 0.96072508 0.96363636 0.96096096] - -mean value: 0.9644116554416667 - -key: test_precision -value: [0.5625 0.68421053 0.625 0.55 0.66666667 0.6 - 0.73684211 0.6 0.57894737 0.65 ] - -mean value: 0.6254166666666666 - -key: train_precision -value: [0.97058824 0.96341463 0.95808383 0.96385542 0.95808383 0.96363636 - 0.96385542 0.95783133 0.9695122 0.95808383] - -mean value: 0.962694509387946 - -key: test_recall -value: [0.5 0.72222222 0.55555556 0.61111111 0.73684211 0.63157895 - 0.73684211 0.47368421 0.61111111 0.72222222] - -mean value: 0.6301169590643275 - -key: train_recall -value: [0.9939759 0.95180723 0.96385542 0.96385542 0.96969697 0.96363636 - 0.96969697 0.96363636 0.95783133 0.96385542] - -mean value: 0.9661847389558232 - -key: test_accuracy -value: [0.56756757 0.7027027 0.62162162 0.56756757 0.67567568 0.59459459 - 0.72972973 0.56756757 0.58333333 0.66666667] - -mean value: 0.6277027027027027 - -key: train_accuracy -value: [0.98187311 0.95770393 0.96072508 0.96374622 0.96374622 0.96374622 - 0.96676737 0.96072508 0.96385542 0.96084337] - -mean value: 0.9643732027809122 - -key: test_roc_auc -value: [0.56578947 0.70321637 0.61988304 0.56871345 0.67397661 0.59356725 - 0.72953216 0.57017544 0.58333333 0.66666667] - -mean value: 0.6274853801169591 - -key: train_roc_auc -value: [0.98183644 0.9577218 0.96071559 0.96374589 0.96376415 0.96374589 - 0.9667762 0.96073384 0.96385542 0.96084337] - -mean value: 0.9643738590726543 - -key: test_jcc -value: [0.36 0.54166667 0.41666667 0.40740741 0.53846154 0.44444444 - 0.58333333 0.36 0.42307692 0.52 ] - -mean value: 0.45950569800569807 - -key: train_jcc -value: [0.96491228 0.91860465 0.92485549 0.93023256 0.93023256 0.92982456 - 0.93567251 0.9244186 0.92982456 0.92485549] - -mean value: 0.9313433272880637 - -key: TN -value: 115 - -mean value: 115.0 - -key: FP -value: 68 - -mean value: 68.0 - -key: FN -value: 69 - -mean value: 69.0 - -key: TP -value: 116 - -mean value: 116.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -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/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.33 - -Accuracy on Blind test: 0.66 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.54823947 0.55045033 0.55409837 0.54611897 0.53697062 0.54081631 - 0.54240441 0.54380441 0.5367167 0.54200602] - -mean value: 0.5441625595092774 - -key: score_time -value: [0.00914979 0.00951767 0.00929976 0.00917625 0.00932455 0.00923324 - 0.00986409 0.00924659 0.00926423 0.00919986] - -mean value: 0.00932760238647461 - -key: test_mcc -value: [0.89181287 1. 0.94721815 0.80369958 0.83871328 0.94736842 - 0.89181287 0.94721815 0.9459053 1. ] - -mean value: 0.9213748616924337 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.94444444 1. 0.97142857 0.9 0.92307692 0.97297297 - 0.94736842 0.97435897 0.97142857 1. ] - -mean value: 0.9605078878763089 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.94444444 1. 1. 0.81818182 0.9 1. - 0.94736842 0.95 1. 1. ] - -mean value: 0.9559994683678894 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94444444 1. 0.94444444 1. 0.94736842 0.94736842 - 0.94736842 1. 0.94444444 1. ] - -mean value: 0.9675438596491228 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.94594595 1. 0.97297297 0.89189189 0.91891892 0.97297297 - 0.94594595 0.97297297 0.97222222 1. ] - -mean value: 0.9593843843843844 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.94590643 1. 0.97222222 0.89473684 0.91812865 0.97368421 - 0.94590643 0.97222222 0.97222222 1. ] - -mean value: 0.9595029239766081 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.89473684 1. 0.94444444 0.81818182 0.85714286 0.94736842 - 0.9 0.95 0.94444444 1. ] - -mean value: 0.9256318827371459 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 175 - -mean value: 175.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 178 - -mean value: 178.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02358007 0.0270009 0.02640438 0.0293076 0.02971387 0.02869248 - 0.02799392 0.02583647 0.0277698 0.02604294] - -mean value: 0.02723424434661865 - -key: score_time -value: [0.01248336 0.01238346 0.01278973 0.01239896 0.01331091 0.0134089 - 0.01262665 0.01353836 0.01246738 0.01345086] - -mean value: 0.012885856628417968 - -key: test_mcc -value: [0.40780312 0.31335022 0.18768409 0.19469789 0.35484024 0.24189738 - 0.13424397 0.19469789 0.55555556 0.24806947] - -mean value: 0.2832839816439415 - -key: train_mcc -value: [0.90208065 0.78162109 0.88436295 0.9396473 0.89105115 0.90754781 - 0.97611746 0.9698054 0.85087912 0.76220232] - -mean value: 0.8865315254921949 - -key: test_fscore -value: [0.66666667 0.55172414 0.57142857 0.61538462 0.66666667 0.65 - 0.63636364 0.57142857 0.77777778 0.68181818] - -mean value: 0.6389258825465721 - -key: train_fscore -value: [0.94603175 0.8630137 0.94285714 0.96969697 0.93890675 0.94904459 - 0.98802395 0.98480243 0.91612903 0.88297872] - -mean value: 0.9381485034983903 - -key: test_precision -value: [0.73333333 0.72727273 0.58823529 0.57142857 0.70588235 0.61904762 - 0.56 0.625 0.77777778 0.57692308] - -mean value: 0.6484900752841929 - -key: train_precision -value: [1. 1. 0.89673913 0.97560976 1. 1. - 0.97633136 0.98780488 0.98611111 0.79047619] - -mean value: 0.9613072427115172 - -key: test_recall -value: [0.61111111 0.44444444 0.55555556 0.66666667 0.63157895 0.68421053 - 0.73684211 0.52631579 0.77777778 0.83333333] - -mean value: 0.646783625730994 - -key: train_recall -value: [0.89759036 0.75903614 0.9939759 0.96385542 0.88484848 0.9030303 - 1. 0.98181818 0.85542169 1. ] - -mean value: 0.9239576487769259 - -key: test_accuracy -value: [0.7027027 0.64864865 0.59459459 0.59459459 0.67567568 0.62162162 - 0.56756757 0.59459459 0.77777778 0.61111111] - -mean value: 0.638888888888889 - -key: train_accuracy -value: [0.94864048 0.87915408 0.93957704 0.96978852 0.94259819 0.95166163 - 0.98791541 0.98489426 0.92168675 0.86746988] - -mean value: 0.9393386233756781 - -key: test_roc_auc -value: [0.7002924 0.64327485 0.59356725 0.59649123 0.67690058 0.61988304 - 0.5628655 0.59649123 0.77777778 0.61111111] - -mean value: 0.6378654970760234 - -key: train_roc_auc -value: [0.94879518 0.87951807 0.93941219 0.9698065 0.94242424 0.95151515 - 0.98795181 0.98488499 0.92168675 0.86746988] - -mean value: 0.9393464768163564 - -key: test_jcc -value: [0.5 0.38095238 0.4 0.44444444 0.5 0.48148148 - 0.46666667 0.4 0.63636364 0.51724138] - -mean value: 0.47271499892189556 - -key: train_jcc -value: [0.89759036 0.75903614 0.89189189 0.94117647 0.88484848 0.9030303 - 0.97633136 0.97005988 0.8452381 0.79047619] - -mean value: 0.8859679183283564 - -key: TN -value: 111 - -mean value: 111.0 - -key: FP -value: 69 - -mean value: 69.0 - -key: FN -value: 73 - -mean value: 73.0 - -key: TP -value: 115 - -mean value: 115.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.37 - -Accuracy on Blind test: 0.68 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02783775 0.04104495 0.03589368 0.01749587 0.01490116 0.02035332 - 0.03309083 0.03650165 0.04431796 0.06095028] - -mean value: 0.03323874473571777 - -key: score_time -value: [0.01984787 0.02393389 0.01246977 0.01224542 0.01215792 0.0245204 - 0.02235675 0.02211308 0.02456546 0.02167416] - -mean value: 0.019588470458984375 - -key: test_mcc -value: [0.78362573 0.73821295 0.80156851 0.7888597 0.67849265 0.6754386 - 0.80369958 0.73099415 0.89442719 0.77777778] - -mean value: 0.7673096828103866 - -key: train_mcc -value: [0.93437859 0.93492806 0.92920221 0.93396646 0.94018808 0.93368393 - 0.92856701 0.94066763 0.93456623 0.92879005] - -mean value: 0.9338938238702286 - -key: test_fscore -value: [0.88888889 0.84848485 0.875 0.89473684 0.85 0.84210526 - 0.88235294 0.86486486 0.94117647 0.88888889] - -mean value: 0.8776499008155355 - -key: train_fscore -value: [0.96615385 0.96594427 0.96273292 0.96636086 0.9691358 0.96636086 - 0.96273292 0.9689441 0.96615385 0.96296296] - -mean value: 0.9657482380612038 - -key: test_precision -value: [0.88888889 0.93333333 1. 0.85 0.80952381 0.84210526 - 1. 0.88888889 1. 0.88888889] - -mean value: 0.9101629072681705 - -key: train_precision -value: [0.98742138 0.99363057 0.99358974 0.98136646 0.98742138 0.97530864 - 0.98726115 0.99363057 0.98742138 0.98734177] - -mean value: 0.9874393061281307 - -key: test_recall -value: [0.88888889 0.77777778 0.77777778 0.94444444 0.89473684 0.84210526 - 0.78947368 0.84210526 0.88888889 0.88888889] - -mean value: 0.8535087719298247 - -key: train_recall -value: [0.94578313 0.93975904 0.93373494 0.95180723 0.95151515 0.95757576 - 0.93939394 0.94545455 0.94578313 0.93975904] - -mean value: 0.945056589996349 - -key: test_accuracy -value: [0.89189189 0.86486486 0.89189189 0.89189189 0.83783784 0.83783784 - 0.89189189 0.86486486 0.94444444 0.88888889] - -mean value: 0.8806306306306306 - -key: train_accuracy -value: [0.96676737 0.96676737 0.96374622 0.96676737 0.96978852 0.96676737 - 0.96374622 0.96978852 0.96686747 0.96385542] - -mean value: 0.9664861864375931 - -key: test_roc_auc -value: [0.89181287 0.8625731 0.88888889 0.89327485 0.83625731 0.8377193 - 0.89473684 0.86549708 0.94444444 0.88888889] - -mean value: 0.8804093567251462 - -key: train_roc_auc -value: [0.96683096 0.96684922 0.96383717 0.96681271 0.96973348 0.96673969 - 0.96367287 0.96971522 0.96686747 0.96385542] - -mean value: 0.9664914202263601 - -key: test_jcc -value: [0.8 0.73684211 0.77777778 0.80952381 0.73913043 0.72727273 - 0.78947368 0.76190476 0.88888889 0.8 ] - -mean value: 0.7830814189624259 - -key: train_jcc -value: [0.93452381 0.93413174 0.92814371 0.93491124 0.94011976 0.93491124 - 0.92814371 0.93975904 0.93452381 0.92857143] - -mean value: 0.9337739491126417 - -key: TN -value: 167 - -mean value: 167.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 157 - -mean value: 157.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.86 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.28012466 0.22199059 0.26628947 0.29443693 0.2635541 0.21547222 - 0.54660797 0.38113809 0.22990465 0.19141674] - -mean value: 0.289093542098999 - -key: score_time -value: [0.02156568 0.02149439 0.02389526 0.0214138 0.01203299 0.02061915 - 0.03657269 0.02318287 0.01198387 0.02346087] - -mean value: 0.02162215709686279 - -key: test_mcc -value: [0.78362573 0.73821295 0.80156851 0.7888597 0.67849265 0.6754386 - 0.80369958 0.73099415 0.89442719 0.77777778] - -mean value: 0.7673096828103866 - -key: train_mcc -value: [0.93437859 0.93492806 0.92920221 0.93396646 0.94018808 0.93368393 - 0.92856701 0.94066763 0.93456623 0.92879005] - -mean value: 0.9338938238702286 - -key: test_fscore -value: [0.88888889 0.84848485 0.875 0.89473684 0.85 0.84210526 - 0.88235294 0.86486486 0.94117647 0.88888889] - -mean value: 0.8776499008155355 - -key: train_fscore -value: [0.96615385 0.96594427 0.96273292 0.96636086 0.9691358 0.96636086 - 0.96273292 0.9689441 0.96615385 0.96296296] - -mean value: 0.9657482380612038 - -key: test_precision -value: [0.88888889 0.93333333 1. 0.85 0.80952381 0.84210526 - 1. 0.88888889 1. 0.88888889] - -mean value: 0.9101629072681705 - -key: train_precision -value: [0.98742138 0.99363057 0.99358974 0.98136646 0.98742138 0.97530864 - 0.98726115 0.99363057 0.98742138 0.98734177] - -mean value: 0.9874393061281307 - -key: test_recall -value: [0.88888889 0.77777778 0.77777778 0.94444444 0.89473684 0.84210526 - 0.78947368 0.84210526 0.88888889 0.88888889] - -mean value: 0.8535087719298247 - -key: train_recall -value: [0.94578313 0.93975904 0.93373494 0.95180723 0.95151515 0.95757576 - 0.93939394 0.94545455 0.94578313 0.93975904] - -mean value: 0.945056589996349 - -key: test_accuracy -value: [0.89189189 0.86486486 0.89189189 0.89189189 0.83783784 0.83783784 - 0.89189189 0.86486486 0.94444444 0.88888889] - -mean value: 0.8806306306306306 - -key: train_accuracy -value: [0.96676737 0.96676737 0.96374622 0.96676737 0.96978852 0.96676737 - 0.96374622 0.96978852 0.96686747 0.96385542] - -mean value: 0.9664861864375931 - -key: test_roc_auc -value: [0.89181287 0.8625731 0.88888889 0.89327485 0.83625731 0.8377193 - 0.89473684 0.86549708 0.94444444 0.88888889] - -mean value: 0.8804093567251462 - -key: train_roc_auc -value: [0.96683096 0.96684922 0.96383717 0.96681271 0.96973348 0.96673969 - 0.96367287 0.96971522 0.96686747 0.96385542] - -mean value: 0.9664914202263601 - -key: test_jcc -value: [0.8 0.73684211 0.77777778 0.80952381 0.73913043 0.72727273 - 0.78947368 0.76190476 0.88888889 0.8 ] - -mean value: 0.7830814189624259 - -key: train_jcc -value: [0.93452381 0.93413174 0.92814371 0.93491124 0.94011976 0.93491124 - 0.92814371 0.93975904 0.93452381 0.92857143] - -mean value: 0.9337739491126417 - -key: TN -value: 167 - -mean value: 167.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 17 - -mean value: 17.0 - -key: TP -value: 157 - -mean value: 157.0 - -key: trainingY_neg -value: 184 - -mean value: 184.0 - -key: trainingY_pos -value: 184 - -mean value: 184.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: 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 - rus_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: 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 - rus_CV['Resampling'] = rs_rus -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: 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 - rus_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: 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 - rus_BT['Resampling'] = rs_rus -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.86 - -PASS: sorting df by score that is mapped onto the order I want - -============================================================== - -Running several classification models (n): 24 -List of models: -('Logistic Regression', LogisticRegression(random_state=42)) -('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) -('Gaussian NB', GaussianNB()) -('Naive Bayes', BernoulliNB()) -('K-Nearest Neighbors', KNeighborsClassifier()) -('SVC', SVC(random_state=42)) -('MLP', MLPClassifier(max_iter=500, random_state=42)) -('Decision Tree', DecisionTreeClassifier(random_state=42)) -('Extra Trees', ExtraTreesClassifier(random_state=42)) -('Extra Tree', ExtraTreeClassifier(random_state=42)) -('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) -('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, oob_score=True, - 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=None, 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)) -('LDA', LinearDiscriminantAnalysis()) -('Multinomial', MultinomialNB()) -('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) -('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) -('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) -('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) -('Gaussian Process', GaussianProcessClassifier(random_state=42)) -('Gradient Boosting', GradientBoostingClassifier(random_state=42)) -('QDA', QuadraticDiscriminantAnalysis()) -('Ridge Classifier', RidgeClassifier(random_state=42)) -('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) - -================================================================ - - -Running classifier: 1 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.03290296 0.03450441 0.04199839 0.03690124 0.0331862 0.03373098 - 0.07654214 0.03495836 0.0519197 0.03621554] - -mean value: 0.04128599166870117 - -key: score_time -value: [0.0130477 0.01327848 0.01212001 0.01316905 0.01183128 0.0131731 - 0.01472235 0.01313138 0.01348996 0.01353669] - -mean value: 0.01315000057220459 - -key: test_mcc -value: [0.85280287 0.58218174 0.63245553 0.78947368 0.79388419 0.79388419 - 0.73786479 0.79388419 0.74044197 0.74044197] - -mean value: 0.7457315110884076 - -key: train_mcc -value: [0.86543987 0.85935894 0.83581486 0.85366518 0.86543987 0.88333157 - 0.88333157 0.85307402 0.86020287 0.87755705] - -mean value: 0.8637215781443464 - -key: test_fscore -value: [0.91428571 0.77777778 0.81081081 0.89473684 0.88888889 0.88888889 - 0.87179487 0.9 0.87179487 0.85714286] - -mean value: 0.8676121523489945 - -key: train_fscore -value: [0.93093093 0.92814371 0.91616766 0.92492492 0.93093093 0.93975904 - 0.93975904 0.92581602 0.92814371 0.93693694] - -mean value: 0.930151290957211 - -key: test_precision -value: [1. 0.82352941 0.83333333 0.89473684 0.94117647 0.94117647 - 0.85 0.85714286 0.80952381 0.9375 ] - -mean value: 0.8888119195046439 - -key: train_precision -value: [0.95092025 0.94512195 0.93292683 0.94478528 0.95092025 0.96296296 - 0.96296296 0.93413174 0.95092025 0.95705521] - -mean value: 0.9492707669934541 - -key: test_recall -value: [0.84210526 0.73684211 0.78947368 0.89473684 0.84210526 0.84210526 - 0.89473684 0.94736842 0.94444444 0.78947368] - -mean value: 0.8523391812865496 - -key: train_recall -value: [0.91176471 0.91176471 0.9 0.90588235 0.91176471 0.91764706 - 0.91764706 0.91764706 0.90643275 0.91764706] - -mean value: 0.9118197454420365 - -key: test_accuracy -value: [0.92105263 0.78947368 0.81578947 0.89473684 0.89473684 0.89473684 - 0.86842105 0.89473684 0.86486486 0.86486486] - -mean value: 0.8703413940256045 - -key: train_accuracy -value: [0.93235294 0.92941176 0.91764706 0.92647059 0.93235294 0.94117647 - 0.94117647 0.92647059 0.92961877 0.93841642] - -mean value: 0.9315094014145249 - -key: test_roc_auc -value: [0.92105263 0.78947368 0.81578947 0.89473684 0.89473684 0.89473684 - 0.86842105 0.89473684 0.86695906 0.86695906] - -mean value: 0.8707602339181285 - -key: train_roc_auc -value: [0.93235294 0.92941176 0.91764706 0.92647059 0.93235294 0.94117647 - 0.94117647 0.92647059 0.92968696 0.93835569] - -mean value: 0.9315101479188167 - -key: test_jcc -value: [0.84210526 0.63636364 0.68181818 0.80952381 0.8 0.8 - 0.77272727 0.81818182 0.77272727 0.75 ] - -mean value: 0.7683447254499887 - -key: train_jcc -value: [0.87078652 0.86592179 0.84530387 0.8603352 0.87078652 0.88636364 - 0.88636364 0.86187845 0.86592179 0.88135593] - -mean value: 0.8695017330030238 - -key: TN -value: 168 - -mean value: 168.0 - -key: FP -value: 28 - -mean value: 28.0 - -key: FN -value: 21 - -mean value: 21.0 - -key: TP -value: 161 - -mean value: 161.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.67 - -Accuracy on Blind test: 0.83 - -Running classifier: 2 -Model_name: Logistic RegressionCV -Model func: LogisticRegressionCV(random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LogisticRegressionCV(random_state=42))]) - -key: fit_time -value: [0.76184154 0.77311563 0.85383725 0.81253266 0.75160384 0.93275571 - 0.75248647 0.94675541 0.75645113 0.76324224] - -mean value: 0.8104621887207031 - -key: score_time -value: [0.01350141 0.01348019 0.01350856 0.01340699 0.01362109 0.01341414 - 0.0135057 0.01527405 0.01354718 0.01367497] - -mean value: 0.013693428039550782 - -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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): -STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. - -Increase the number of iterations (max_iter) or scale the data as shown in: - https://scikit-learn.org/stable/modules/preprocessing.html -Please also refer to the documentation for alternative solver options: - https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression - n_iter_i = _check_optimize_result( -[0.78947368 0.84327404 0.73786479 0.89473684 0.89973541 0.85280287 - 0.84327404 0.85280287 0.94736842 0.69356297] - -mean value: 0.8354895932451495 - -key: train_mcc -value: [0.97653817 0.9707394 0.97100831 0.96497304 0.97653817 0.99413485 - 0.97653817 0.98823529 0.97660738 0.98242114] - -mean value: 0.9777733926225123 - -key: test_fscore -value: [0.89473684 0.91891892 0.86486486 0.94736842 0.94444444 0.91428571 - 0.91891892 0.92682927 0.97297297 0.82352941] - -mean value: 0.9126869777621118 - -key: train_fscore -value: [0.98816568 0.9851632 0.98507463 0.98214286 0.98816568 0.99706745 - 0.98816568 0.99411765 0.98823529 0.99115044] - -mean value: 0.988744856251112 - -key: test_precision -value: [0.89473684 0.94444444 0.88888889 0.94736842 1. 1. - 0.94444444 0.86363636 0.94736842 0.93333333] - -mean value: 0.9364221158958002 - -key: train_precision -value: [0.99404762 0.99401198 1. 0.9939759 0.99404762 0.99415205 - 0.99404762 0.99411765 0.99408284 0.99408284] - -mean value: 0.994656611112104 - -key: test_recall -value: [0.89473684 0.89473684 0.84210526 0.94736842 0.89473684 0.84210526 - 0.89473684 1. 1. 0.73684211] - -mean value: 0.894736842105263 - -key: train_recall -value: [0.98235294 0.97647059 0.97058824 0.97058824 0.98235294 1. - 0.98235294 0.99411765 0.98245614 0.98823529] - -mean value: 0.9829514963880289 - -key: test_accuracy -value: [0.89473684 0.92105263 0.86842105 0.94736842 0.94736842 0.92105263 - 0.92105263 0.92105263 0.97297297 0.83783784] - -mean value: 0.9152916073968707 - -key: train_accuracy -value: [0.98823529 0.98529412 0.98529412 0.98235294 0.98823529 0.99705882 - 0.98823529 0.99411765 0.98826979 0.99120235] - -mean value: 0.988829567017423 - -key: test_roc_auc -value: [0.89473684 0.92105263 0.86842105 0.94736842 0.94736842 0.92105263 - 0.92105263 0.92105263 0.97368421 0.84064327] - -mean value: 0.9156432748538013 - -key: train_roc_auc -value: [0.98823529 0.98529412 0.98529412 0.98235294 0.98823529 0.99705882 - 0.98823529 0.99411765 0.98828689 0.99119367] - -mean value: 0.9888304093567252 - -key: test_jcc -value: [0.80952381 0.85 0.76190476 0.9 0.89473684 0.84210526 - 0.85 0.86363636 0.94736842 0.7 ] - -mean value: 0.8419275461380724 - -key: train_jcc -value: [0.97660819 0.97076023 0.97058824 0.96491228 0.97660819 0.99415205 - 0.97660819 0.98830409 0.97674419 0.98245614] - -mean value: 0.9777741778065774 - -key: TN -value: 177 - -mean value: 177.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 12 - -mean value: 12.0 - -key: TP -value: 169 - -mean value: 169.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.76 - -Accuracy on Blind test: 0.88 - -Running classifier: 3 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', GaussianNB())]) - -key: fit_time -value: [0.01353025 0.0131216 0.01090002 0.00954771 0.00945187 0.01026678 - 0.01025438 0.00937963 0.0097959 0.00951505] - -mean value: 0.010576319694519044 - -key: score_time -value: [0.01217771 0.00996184 0.00912881 0.00896931 0.00912881 0.00916576 - 0.00948095 0.00974846 0.00977588 0.00904393] - -mean value: 0.009658145904541015 - -key: test_mcc -value: [0.58218174 0.21821789 0.42640143 0.37047929 0.59222009 0.4061812 - 0.52704628 0.47633051 0.24633537 0.35558302] - -mean value: 0.4200976817609973 - -key: train_mcc -value: [0.42352941 0.45471472 0.43638266 0.48014808 0.43677775 0.46038736 - 0.4122214 0.48450361 0.47559894 0.45574606] - -mean value: 0.45200099924969084 - -key: test_fscore -value: [0.77777778 0.54545455 0.68571429 0.66666667 0.76470588 0.6 - 0.75675676 0.75 0.63157895 0.71428571] - -mean value: 0.6892940576377109 - -key: train_fscore -value: [0.71176471 0.71384615 0.70731707 0.72100313 0.70552147 0.71779141 - 0.69879518 0.72839506 0.72049689 0.71559633] - -mean value: 0.7140527418267514 - -key: test_precision -value: [0.82352941 0.64285714 0.75 0.70588235 0.86666667 0.81818182 - 0.77777778 0.71428571 0.6 0.65217391] - -mean value: 0.735135479751848 - -key: train_precision -value: [0.71176471 0.7483871 0.73417722 0.77181208 0.73717949 0.75 - 0.71604938 0.76623377 0.76821192 0.74522293] - -mean value: 0.7449038584978743 - -key: test_recall -value: [0.73684211 0.47368421 0.63157895 0.63157895 0.68421053 0.47368421 - 0.73684211 0.78947368 0.66666667 0.78947368] - -mean value: 0.6614035087719299 - -key: train_recall -value: [0.71176471 0.68235294 0.68235294 0.67647059 0.67647059 0.68823529 - 0.68235294 0.69411765 0.67836257 0.68823529] - -mean value: 0.6860715514275886 - -key: test_accuracy -value: [0.78947368 0.60526316 0.71052632 0.68421053 0.78947368 0.68421053 - 0.76315789 0.73684211 0.62162162 0.67567568] - -mean value: 0.706045519203414 - -key: train_accuracy -value: [0.71176471 0.72647059 0.71764706 0.73823529 0.71764706 0.72941176 - 0.70588235 0.74117647 0.73607038 0.72727273] - -mean value: 0.7251578402622048 - -key: test_roc_auc -value: [0.78947368 0.60526316 0.71052632 0.68421053 0.78947368 0.68421053 - 0.76315789 0.73684211 0.62280702 0.67251462] - -mean value: 0.7058479532163743 - -key: train_roc_auc -value: [0.71176471 0.72647059 0.71764706 0.73823529 0.71764706 0.72941176 - 0.70588235 0.74117647 0.73624011 0.72715858] - -mean value: 0.7251633986928105 - -key: test_jcc -value: [0.63636364 0.375 0.52173913 0.5 0.61904762 0.42857143 - 0.60869565 0.6 0.46153846 0.55555556] - -mean value: 0.5306511483685397 - -key: train_jcc -value: [0.55251142 0.55502392 0.54716981 0.56372549 0.5450237 0.55980861 - 0.53703704 0.57281553 0.5631068 0.55714286] - -mean value: 0.5553365173886561 - -key: TN -value: 142 - -mean value: 142.0 - -key: FP -value: 64 - -mean value: 64.0 - -key: FN -value: 47 - -mean value: 47.0 - -key: TP -value: 125 - -mean value: 125.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.42 - -Accuracy on Blind test: 0.71 - -Running classifier: 4 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', BernoulliNB())]) - -key: fit_time -value: [0.00943923 0.00956821 0.00933814 0.0097239 0.00987291 0.00939107 - 0.00931835 0.00963306 0.00939727 0.0093627 ] - -mean value: 0.009504485130310058 - -key: score_time -value: [0.00861311 0.0086062 0.0088203 0.00891066 0.00879312 0.00877285 - 0.00865364 0.0087235 0.00873899 0.0092628 ] - -mean value: 0.008789515495300293 - -key: test_mcc -value: [-0.05292561 0.21821789 -0.05292561 0.16151457 0.31622777 0. - 0.31622777 0.26462806 0.19469789 -0.09678053] - -mean value: 0.1268882185717482 - -key: train_mcc -value: [0.29617444 0.34807805 0.31872971 0.27651365 0.28252897 0.28828019 - 0.26471046 0.30004672 0.26734194 0.29236818] - -mean value: 0.2934772305691697 - -key: test_fscore -value: [0.5 0.54545455 0.44444444 0.52941176 0.66666667 0.48648649 - 0.64864865 0.65 0.61538462 0.54545455] - -mean value: 0.5631951717245836 - -key: train_fscore -value: [0.66666667 0.68555241 0.64417178 0.64139942 0.64739884 0.64094955 - 0.63343109 0.65306122 0.62462462 0.66295265] - -mean value: 0.6500208249874144 - -key: test_precision -value: [0.47619048 0.64285714 0.47058824 0.6 0.65 0.5 - 0.66666667 0.61904762 0.57142857 0.48 ] - -mean value: 0.5676778711484595 - -key: train_precision -value: [0.63157895 0.66120219 0.67307692 0.63583815 0.63636364 0.64670659 - 0.63157895 0.64739884 0.64197531 0.62962963] - -mean value: 0.6435349159287356 - -key: test_recall -value: [0.52631579 0.47368421 0.42105263 0.47368421 0.68421053 0.47368421 - 0.63157895 0.68421053 0.66666667 0.63157895] - -mean value: 0.5666666666666667 - -key: train_recall -value: [0.70588235 0.71176471 0.61764706 0.64705882 0.65882353 0.63529412 - 0.63529412 0.65882353 0.60818713 0.7 ] - -mean value: 0.6578775369797042 - -key: test_accuracy -value: [0.47368421 0.60526316 0.47368421 0.57894737 0.65789474 0.5 - 0.65789474 0.63157895 0.59459459 0.45945946] - -mean value: 0.5633001422475108 - -key: train_accuracy -value: [0.64705882 0.67352941 0.65882353 0.63823529 0.64117647 0.64411765 - 0.63235294 0.65 0.63343109 0.64516129] - -mean value: 0.6463886493013629 - -key: test_roc_auc -value: [0.47368421 0.60526316 0.47368421 0.57894737 0.65789474 0.5 - 0.65789474 0.63157895 0.59649123 0.45467836] - -mean value: 0.5630116959064327 - -key: train_roc_auc -value: [0.64705882 0.67352941 0.65882353 0.63823529 0.64117647 0.64411765 - 0.63235294 0.65 0.63350533 0.64532164] - -mean value: 0.6464121087031305 - -key: test_jcc -value: [0.33333333 0.375 0.28571429 0.36 0.5 0.32142857 - 0.48 0.48148148 0.44444444 0.375 ] - -mean value: 0.3956402116402116 - -key: train_jcc -value: [0.5 0.52155172 0.47511312 0.472103 0.47863248 0.47161572 - 0.46351931 0.48484848 0.45414847 0.49583333] - -mean value: 0.4817365652860478 - -key: TN -value: 106 - -mean value: 106.0 - -key: FP -value: 82 - -mean value: 82.0 - -key: FN -value: 83 - -mean value: 83.0 - -key: TP -value: 107 - -mean value: 107.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.14 - -Accuracy on Blind test: 0.57 - -Running classifier: 5 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', KNeighborsClassifier())]) - -key: fit_time -value: [0.01049709 0.0110817 0.0109179 0.01062846 0.01093102 0.0109303 - 0.00997829 0.00951314 0.01165891 0.00952864] - -mean value: 0.01056654453277588 - -key: score_time -value: [0.01962924 0.01980948 0.02061129 0.01753283 0.01924658 0.01867485 - 0.01792693 0.0186367 0.01966 0.01742816] - -mean value: 0.018915605545043946 - -key: test_mcc -value: [0.21821789 0.05802589 0. 0.26462806 0.47633051 0.26919095 - 0.05263158 0.31980107 0.4633451 0.40643275] - -mean value: 0.2528603803096928 - -key: train_mcc -value: [0.45997091 0.51779041 0.53574849 0.42959762 0.45885529 0.47176503 - 0.51768289 0.43556549 0.48989853 0.47834193] - -mean value: 0.47952165913480876 - -key: test_fscore -value: [0.54545455 0.4 0.38709677 0.61111111 0.72222222 0.58823529 - 0.52631579 0.68292683 0.73684211 0.7027027 ] - -mean value: 0.5902907373806912 - -key: train_fscore -value: [0.7195122 0.76162791 0.77233429 0.71044776 0.72781065 0.72560976 - 0.75739645 0.71257485 0.74927954 0.73273273] - -mean value: 0.7369326135867161 - -key: test_precision -value: [0.64285714 0.54545455 0.5 0.64705882 0.76470588 0.66666667 - 0.52631579 0.63636364 0.7 0.72222222] - -mean value: 0.6351644708920251 - -key: train_precision -value: [0.74683544 0.75287356 0.75706215 0.72121212 0.73214286 0.75316456 - 0.76190476 0.72560976 0.73863636 0.74846626] - -mean value: 0.7437907827773422 - -key: test_recall -value: [0.47368421 0.31578947 0.31578947 0.57894737 0.68421053 0.52631579 - 0.52631579 0.73684211 0.77777778 0.68421053] - -mean value: 0.5619883040935673 - -key: train_recall -value: [0.69411765 0.77058824 0.78823529 0.7 0.72352941 0.7 - 0.75294118 0.7 0.76023392 0.71764706] - -mean value: 0.7307292741658067 - -key: test_accuracy -value: [0.60526316 0.52631579 0.5 0.63157895 0.73684211 0.63157895 - 0.52631579 0.65789474 0.72972973 0.7027027 ] - -mean value: 0.6248221906116643 - -key: train_accuracy -value: [0.72941176 0.75882353 0.76764706 0.71470588 0.72941176 0.73529412 - 0.75882353 0.71764706 0.74486804 0.73900293] - -mean value: 0.7395635673624289 - -key: test_roc_auc -value: [0.60526316 0.52631579 0.5 0.63157895 0.73684211 0.63157895 - 0.52631579 0.65789474 0.73099415 0.70321637] - -mean value: 0.625 - -key: train_roc_auc -value: [0.72941176 0.75882353 0.76764706 0.71470588 0.72941176 0.73529412 - 0.75882353 0.71764706 0.74482284 0.73894049] - -mean value: 0.7395528035775714 - -key: test_jcc -value: [0.375 0.25 0.24 0.44 0.56521739 0.41666667 - 0.35714286 0.51851852 0.58333333 0.54166667] - -mean value: 0.42875454336323904 - -key: train_jcc -value: [0.56190476 0.61502347 0.62910798 0.55092593 0.57209302 0.56937799 - 0.60952381 0.55348837 0.59907834 0.57819905] - -mean value: 0.5838722731679543 - -key: TN -value: 130 - -mean value: 130.0 - -key: FP -value: 83 - -mean value: 83.0 - -key: FN -value: 59 - -mean value: 59.0 - -key: TP -value: 106 - -mean value: 106.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.63 - -Running classifier: 6 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. - warnings.warn( -[0.01980114 0.01862502 0.01608133 0.0180831 0.01655173 0.01897287 - 0.01758957 0.01705074 0.01795864 0.01760578] - -mean value: 0.017831993103027344 - -key: score_time -value: [0.01156235 0.01093364 0.0111413 0.01138067 0.01151705 0.01167202 - 0.01196718 0.01207685 0.01218247 0.01181316] - -mean value: 0.011624670028686524 - -key: test_mcc -value: [0.53300179 0.26919095 0.52704628 0.63960215 0.42163702 0.43643578 - 0.79388419 0.37686733 0.60308132 0.51319869] - -mean value: 0.5113945493809057 - -key: train_mcc -value: [0.71809445 0.77647059 0.71177702 0.73540864 0.70001211 0.70666525 - 0.74117647 0.74138173 0.69504937 0.72462581] - -mean value: 0.725066143909187 - -key: test_fscore -value: [0.74285714 0.58823529 0.75675676 0.8 0.71794872 0.66666667 - 0.9 0.71428571 0.80952381 0.76923077] - -mean value: 0.7465504871387224 - -key: train_fscore -value: [0.85628743 0.88823529 0.85545723 0.86646884 0.84955752 0.84939759 - 0.87058824 0.86904762 0.84883721 0.85970149] - -mean value: 0.8613578457802676 - -key: test_precision -value: [0.8125 0.66666667 0.77777778 0.875 0.7 0.78571429 - 0.85714286 0.65217391 0.70833333 0.75 ] - -mean value: 0.7585308833678398 - -key: train_precision -value: [0.87195122 0.88823529 0.85798817 0.8742515 0.85207101 0.87037037 - 0.87058824 0.87951807 0.84393064 0.87272727] - -mean value: 0.8681631768752531 - -key: test_recall -value: [0.68421053 0.52631579 0.73684211 0.73684211 0.73684211 0.57894737 - 0.94736842 0.78947368 0.94444444 0.78947368] - -mean value: 0.7470760233918129 - -key: train_recall -value: [0.84117647 0.88823529 0.85294118 0.85882353 0.84705882 0.82941176 - 0.87058824 0.85882353 0.85380117 0.84705882] - -mean value: 0.8547918816649467 - -key: test_accuracy -value: [0.76315789 0.63157895 0.76315789 0.81578947 0.71052632 0.71052632 - 0.89473684 0.68421053 0.78378378 0.75675676] - -mean value: 0.7514224751066856 - -key: train_accuracy -value: [0.85882353 0.88823529 0.85588235 0.86764706 0.85 0.85294118 - 0.87058824 0.87058824 0.84750733 0.86217009] - -mean value: 0.8624383301707781 - -key: test_roc_auc -value: [0.76315789 0.63157895 0.76315789 0.81578947 0.71052632 0.71052632 - 0.89473684 0.68421053 0.7880117 0.75584795] - -mean value: 0.7517543859649123 - -key: train_roc_auc -value: [0.85882353 0.88823529 0.85588235 0.86764706 0.85 0.85294118 - 0.87058824 0.87058824 0.84748882 0.8621259 ] - -mean value: 0.8624320605435157 - -key: test_jcc -value: [0.59090909 0.41666667 0.60869565 0.66666667 0.56 0.5 - 0.81818182 0.55555556 0.68 0.625 ] - -mean value: 0.602167545015371 - -key: train_jcc -value: [0.7486911 0.7989418 0.74742268 0.76439791 0.73846154 0.7382199 - 0.77083333 0.76842105 0.73737374 0.7539267 ] - -mean value: 0.7566689743248599 - -key: TN -value: 143 - -mean value: 143.0 - -key: FP -value: 48 - -mean value: 48.0 - -key: FN -value: 46 - -mean value: 46.0 - -key: TP -value: 141 - -mean value: 141.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.48 - -Accuracy on Blind test: 0.74 - -Running classifier: 7 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MLPClassifier(max_iter=500, random_state=42))]) - -key: fit_time -value: [1.64824557 1.35730505 1.36257911 1.47588587 1.33263326 1.39664102 - 1.39387536 1.27198482 1.3568151 1.27687454] - -mean value: 1.3872839689254761 - -key: score_time -value: [0.01414585 0.01384568 0.01363754 0.01391935 0.01535559 0.01518965 - 0.01253963 0.01370907 0.01538062 0.01393628] - -mean value: 0.014165925979614257 - -key: test_mcc -value: [0.78947368 0.78947368 0.52704628 0.68803296 0.73786479 0.85280287 - 0.84327404 0.74620251 0.94736842 0.73099415] - -mean value: 0.7652533382198918 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.89473684 0.89473684 0.76923077 0.85 0.86486486 0.91428571 - 0.91891892 0.87804878 0.97297297 0.86486486] - -mean value: 0.8822660569836437 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.89473684 0.89473684 0.75 0.80952381 0.88888889 1. - 0.94444444 0.81818182 0.94736842 0.88888889] - -mean value: 0.8836769955191007 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 0.89473684 0.78947368 0.89473684 0.84210526 0.84210526 - 0.89473684 0.94736842 1. 0.84210526] - -mean value: 0.8842105263157893 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.89473684 0.89473684 0.76315789 0.84210526 0.86842105 0.92105263 - 0.92105263 0.86842105 0.97297297 0.86486486] - -mean value: 0.8811522048364153 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.89473684 0.89473684 0.76315789 0.84210526 0.86842105 0.92105263 - 0.92105263 0.86842105 0.97368421 0.86549708] - -mean value: 0.8812865497076026 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.80952381 0.80952381 0.625 0.73913043 0.76190476 0.84210526 - 0.85 0.7826087 0.94736842 0.76190476] - -mean value: 0.7929069957502451 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 166 - -mean value: 166.0 - -key: FP -value: 22 - -mean value: 22.0 - -key: FN -value: 23 - -mean value: 23.0 - -key: TP -value: 167 - -mean value: 167.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.58 - -Accuracy on Blind test: 0.79 - -Running classifier: 8 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.02044773 0.02151775 0.01585531 0.0168097 0.01588988 0.016994 - 0.01665068 0.01669312 0.01583529 0.0153091 ] - -mean value: 0.017200255393981935 - -key: score_time -value: [0.01258969 0.00934052 0.00889635 0.00892234 0.00894523 0.008816 - 0.00885463 0.00886393 0.00902867 0.00899887] - -mean value: 0.009325623512268066 - -key: test_mcc -value: [0.73786479 0.78947368 0.9486833 0.9486833 0.84327404 0.84327404 - 0.9486833 0.89973541 0.94736842 0.84959079] - -mean value: 0.8756631073538641 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.87179487 0.89473684 0.97435897 0.97297297 0.91891892 0.91891892 - 0.97435897 0.94444444 0.97297297 0.91428571] - -mean value: 0.9357763605132027 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.85 0.89473684 0.95 1. 0.94444444 0.94444444 - 0.95 1. 0.94736842 1. ] - -mean value: 0.9480994152046783 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 0.89473684 1. 0.94736842 0.89473684 0.89473684 - 1. 0.89473684 1. 0.84210526] - -mean value: 0.9263157894736842 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.86842105 0.89473684 0.97368421 0.97368421 0.92105263 0.92105263 - 0.97368421 0.94736842 0.97297297 0.91891892] - -mean value: 0.9365576102418208 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.86842105 0.89473684 0.97368421 0.97368421 0.92105263 0.92105263 - 0.97368421 0.94736842 0.97368421 0.92105263] - -mean value: 0.9368421052631579 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.77272727 0.80952381 0.95 0.94736842 0.85 0.85 - 0.95 0.89473684 0.94736842 0.84210526] - -mean value: 0.8813830029619503 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 175 - -mean value: 175.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.88 - -Accuracy on Blind test: 0.94 - -Running classifier: 9 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.11336923 0.11229372 0.11243486 0.11318207 0.11696959 0.12004757 - 0.12024164 0.12119627 0.12171841 0.12052846] - -mean value: 0.11719818115234375 - -key: score_time -value: [0.01743507 0.01769543 0.01754737 0.01754832 0.01772523 0.01917768 - 0.01890135 0.01911449 0.01891112 0.01910305] - -mean value: 0.018315911293029785 - -key: test_mcc -value: [0.73786479 0.59222009 0.53300179 0.68421053 0.63245553 0.68421053 - 0.63245553 0.59222009 0.7888597 0.63129316] - -mean value: 0.6508791733340831 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.86486486 0.76470588 0.74285714 0.84210526 0.82051282 0.84210526 - 0.81081081 0.80952381 0.89473684 0.8 ] - -mean value: 0.8192222699343443 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.88888889 0.86666667 0.8125 0.84210526 0.8 0.84210526 - 0.83333333 0.73913043 0.85 0.875 ] - -mean value: 0.8349729849987287 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.84210526 0.68421053 0.68421053 0.84210526 0.84210526 0.84210526 - 0.78947368 0.89473684 0.94444444 0.73684211] - -mean value: 0.8102339181286549 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.86842105 0.78947368 0.76315789 0.84210526 0.81578947 0.84210526 - 0.81578947 0.78947368 0.89189189 0.81081081] - -mean value: 0.8229018492176386 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.86842105 0.78947368 0.76315789 0.84210526 0.81578947 0.84210526 - 0.81578947 0.78947368 0.89327485 0.8128655 ] - -mean value: 0.8232456140350877 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.76190476 0.61904762 0.59090909 0.72727273 0.69565217 0.72727273 - 0.68181818 0.68 0.80952381 0.66666667] - -mean value: 0.6960067758328629 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 158 - -mean value: 158.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 31 - -mean value: 31.0 - -key: TP -value: 153 - -mean value: 153.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.59 - -Accuracy on Blind test: 0.79 - -Running classifier: 10 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.00980186 0.00944328 0.00986433 0.00964594 0.00950003 0.00946164 - 0.00956392 0.01061344 0.00951958 0.00954795] - -mean value: 0.009696197509765626 - -key: score_time -value: [0.00867033 0.00865149 0.00879049 0.00878024 0.00864363 0.00862765 - 0.0086081 0.00866342 0.00892162 0.00872946] - -mean value: 0.008708643913269042 - -key: test_mcc -value: [0.47368421 0.21081851 0.06788442 0.21320072 0.15877684 0.26315789 - 0.47633051 0.32732684 0.62280702 0.45906433] - -mean value: 0.3273051284840057 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.73684211 0.59459459 0.30769231 0.63414634 0.55555556 0.63157895 - 0.72222222 0.69767442 0.81081081 0.73684211] - -mean value: 0.6427959408838293 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.73684211 0.61111111 0.57142857 0.59090909 0.58823529 0.63157895 - 0.76470588 0.625 0.78947368 0.73684211] - -mean value: 0.6646126792024625 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.73684211 0.57894737 0.21052632 0.68421053 0.52631579 0.63157895 - 0.68421053 0.78947368 0.83333333 0.73684211] - -mean value: 0.6412280701754386 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.73684211 0.60526316 0.52631579 0.60526316 0.57894737 0.63157895 - 0.73684211 0.65789474 0.81081081 0.72972973]/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - - -mean value: 0.6619487908961593 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.73684211 0.60526316 0.52631579 0.60526316 0.57894737 0.63157895 - 0.73684211 0.65789474 0.81140351 0.72953216] - -mean value: 0.6619883040935673 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.58333333 0.42307692 0.18181818 0.46428571 0.38461538 0.46153846 - 0.56521739 0.53571429 0.68181818 0.58333333] - -mean value: 0.4864751190838147 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 129 - -mean value: 129.0 - -key: FP -value: 68 - -mean value: 68.0 - -key: FN -value: 60 - -mean value: 60.0 - -key: TP -value: 121 - -mean value: 121.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.21 - -Accuracy on Blind test: 0.6 - -Running classifier: 11 -Model_name: Random Forest -Model func: RandomForestClassifier(n_estimators=1000, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - RandomForestClassifier(n_estimators=1000, random_state=42))]) - -key: fit_time -value: [1.56562233 1.54380798 1.53276825 1.5542326 1.55776858 1.55642462 - 1.54007173 1.55190039 1.54280162 1.53370094] - -mean value: 1.5479099035263062 - -key: score_time -value: [0.09704709 0.09707403 0.091043 0.10097241 0.09081483 0.09065866 - 0.14608049 0.0953145 0.09146309 0.09621358] - -mean value: 0.09966816902160644 - -key: test_mcc -value: [0.9486833 0.68803296 0.89473684 0.89973541 0.89973541 0.84327404 - 0.84327404 0.9486833 0.94736842 0.84959079] - -mean value: 0.8763114518085132 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.97297297 0.83333333 0.94736842 0.95 0.94444444 0.91891892 - 0.92307692 0.97435897 0.97297297 0.91428571] - -mean value: 0.9351732675416887 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [1. 0.88235294 0.94736842 0.9047619 1. 0.94444444 - 0.9 0.95 0.94736842 1. ] - -mean value: 0.9476296132488082 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94736842 0.78947368 0.94736842 1. 0.89473684 0.89473684 - 0.94736842 1. 1. 0.84210526] - -mean value: 0.9263157894736842 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.97368421 0.84210526 0.94736842 0.94736842 0.94736842 0.92105263 - 0.92105263 0.97368421 0.97297297 0.91891892] - -mean value: 0.9365576102418208 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.97368421 0.84210526 0.94736842 0.94736842 0.94736842 0.92105263 - 0.92105263 0.97368421 0.97368421 0.92105263] - -mean value: 0.9368421052631579 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.94736842 0.71428571 0.9 0.9047619 0.89473684 0.85 - 0.85714286 0.95 0.94736842 0.84210526] - -mean value: 0.8807769423558899 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 14 - -mean value: 14.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 175 - -mean value: 175.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.86 - -Accuracy on Blind test: 0.93 - -Running classifier: 12 -Model_name: Random Forest2 -Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, - n_estimators=1000, n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p...age_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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=10, - oob_score=True, random_state=42))]) - -key: fit_time -value: [0.91832066 0.91726899 0.86296964 0.92607594 0.92572975 0.97675848 - 0.87856126 0.9713788 0.91208029 0.95251131] - -mean value: 0.9241655111312866 - -key: score_time -value: [0.17326832 0.19389057 0.22207499 0.19288945 0.2027204 0.20199037 - 0.16083598 0.18386078 0.21145773 0.21888137] - -mean value: 0.19618699550628663 - -key: test_mcc -value: [0.85280287 0.59222009 0.89973541 0.89973541 0.85280287 0.84327404 - 0.84327404 0.9486833 0.89736456 0.7888597 ] - -mean value: 0.8418752287724587 - -key: train_mcc -value: [0.97647059 0.9707394 0.96477265 0.97653817 0.96477265 0.97060503 - 0.95897286 0.97653817 0.97660738 0.98242114] - -mean value: 0.9718438042005666 - -key: test_fscore -value: [0.91428571 0.76470588 0.94444444 0.95 0.91428571 0.91891892 - 0.92307692 0.97435897 0.94736842 0.88888889] - -mean value: 0.9140333881665151 - -key: train_fscore -value: [0.98823529 0.9851632 0.98224852 0.98816568 0.98224852 0.98533724 - 0.97922849 0.98816568 0.98823529 0.99115044] - -mean value: 0.9858178367876451 - -key: test_precision -value: [1. 0.86666667 1. 0.9047619 1. 0.94444444 - 0.9 0.95 0.9 0.94117647] - -mean value: 0.9407049486461251 - -key: train_precision -value: [0.98823529 0.99401198 0.98809524 0.99404762 0.98809524 0.98245614 - 0.98802395 0.99404762 0.99408284 0.99408284] - -mean value: 0.9905178757371325 - -key: test_recall -value: [0.84210526 0.68421053 0.89473684 1. 0.84210526 0.89473684 - 0.94736842 1. 1. 0.84210526] - -mean value: 0.894736842105263 - -key: train_recall -value: [0.98823529 0.97647059 0.97647059 0.98235294 0.97647059 0.98823529 - 0.97058824 0.98235294 0.98245614 0.98823529] - -mean value: 0.9811867905056759 - -key: test_accuracy -value: [0.92105263 0.78947368 0.94736842 0.94736842 0.92105263 0.92105263 - 0.92105263 0.97368421 0.94594595 0.89189189] - -mean value: 0.9179943100995732 - -key: train_accuracy -value: [0.98823529 0.98529412 0.98235294 0.98823529 0.98235294 0.98529412 - 0.97941176 0.98823529 0.98826979 0.99120235] - -mean value: 0.9858883905468346 - -key: test_roc_auc -value: [0.92105263 0.78947368 0.94736842 0.94736842 0.92105263 0.92105263 - 0.92105263 0.97368421 0.94736842 0.89327485] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. - warn( - -mean value: 0.9182748538011696 - -key: train_roc_auc -value: [0.98823529 0.98529412 0.98235294 0.98823529 0.98235294 0.98529412 - 0.97941176 0.98823529 0.98828689 0.99119367] - -mean value: 0.985889232886137 - -key: test_jcc -value: [0.84210526 0.61904762 0.89473684 0.9047619 0.84210526 0.85 - 0.85714286 0.95 0.9 0.8 ] - -mean value: 0.8459899749373434 - -key: train_jcc -value: [0.97674419 0.97076023 0.96511628 0.97660819 0.96511628 0.97109827 - 0.95930233 0.97660819 0.97674419 0.98245614] - -mean value: 0.9720554270247919 - -key: TN -value: 178 - -mean value: 178.0 - -key: FP -value: 20 - -mean value: 20.0 - -key: FN -value: 11 - -mean value: 11.0 - -key: TP -value: 169 - -mean value: 169.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.85 - -Accuracy on Blind test: 0.92 - -Running classifier: 13 -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=None, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_p... - 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=None, 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: [0.06367397 0.05790019 0.06048632 0.07141566 0.06891084 0.0619297 - 0.06166148 0.06271529 0.06555653 0.06549811] - -mean value: 0.06397480964660644 - -key: score_time -value: [0.01117396 0.01130056 0.01229215 0.01066804 0.01104975 0.01074004 - 0.01051497 0.01053643 0.01045895 0.0108583 ] - -mean value: 0.010959315299987792 - -key: test_mcc -value: [0.84327404 0.89973541 0.84327404 0.9486833 0.89473684 0.9486833 - 0.9486833 1. 0.94736842 0.89736456] - -mean value: 0.9171803215999187 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.91891892 0.95 0.91891892 0.97435897 0.94736842 0.97297297 - 0.97435897 1. 0.97297297 0.94444444] - -mean value: 0.9574314597998809 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.94444444 0.9047619 0.94444444 0.95 0.94736842 1. - 0.95 1. 0.94736842 1. ] - -mean value: 0.9588387635756057 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 1. 0.89473684 1. 0.94736842 0.94736842 - 1. 1. 1. 0.89473684] - -mean value: 0.9578947368421054 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92105263 0.94736842 0.92105263 0.97368421 0.94736842 0.97368421 - 0.97368421 1. 0.97297297 0.94594595] - -mean value: 0.9576813655761024 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.92105263 0.94736842 0.92105263 0.97368421 0.94736842 0.97368421 - 0.97368421 1. 0.97368421 0.94736842] - -mean value: 0.9578947368421054 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85 0.9047619 0.85 0.95 0.9 0.94736842 - 0.95 1. 0.94736842 0.89473684] - -mean value: 0.919423558897243 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 181 - -mean value: 181.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 8 - -mean value: 8.0 - -key: TP -value: 181 - -mean value: 181.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.94 - -Accuracy on Blind test: 0.97 - -Running classifier: 14 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', LinearDiscriminantAnalysis())]) - -key: fit_time -value: [0.04517841 0.06639409 0.06207037 0.06229568 0.06589484 0.06404686 - 0.07109642 0.07244468 0.07659793 0.09462166] - -mean value: 0.06806409358978271 - -key: score_time -value: [0.02114558 0.02277398 0.02187371 0.0231576 0.02125788 0.0219233 - 0.02175045 0.02295852 0.02342176 0.02293897] - -mean value: 0.02232017517089844 - -key: test_mcc -value: [0.52704628 0.84327404 0.45291081 0.85280287 0.84327404 0.63960215 - 0.84327404 0.61017022 0.73821295 0.60308132] - -mean value: 0.6953648715234931 - -key: train_mcc -value: [0.96497304 0.93543979 0.95300713 0.92966915 0.95320508 0.95884012 - 0.95884012 0.95300713 0.94762566 0.95314274] - -mean value: 0.9507749950947344 - -key: test_fscore -value: [0.76923077 0.92307692 0.64516129 0.92682927 0.92307692 0.8 - 0.92307692 0.81818182 0.84848485 0.75 ] - -mean value: 0.8327118763743467 - -key: train_fscore -value: [0.98214286 0.96735905 0.97633136 0.96428571 0.97619048 0.97935103 - 0.97935103 0.97633136 0.97329377 0.97633136] - -mean value: 0.9750968014347139 - -key: test_precision -value: [0.75 0.9 0.83333333 0.86363636 0.9 0.875 - 0.9 0.72 0.93333333 0.92307692] - -mean value: 0.8598379953379954 - -key: train_precision -value: [0.9939759 0.9760479 0.98214286 0.97590361 0.98795181 0.98224852 - 0.98224852 0.98214286 0.98795181 0.98214286] - -mean value: 0.9832756649570428 - -key: test_recall -value: [0.78947368 0.94736842 0.52631579 1. 0.94736842 0.73684211 - 0.94736842 0.94736842 0.77777778 0.63157895] - -mean value: 0.8251461988304092 - -key: train_recall -value: [0.97058824 0.95882353 0.97058824 0.95294118 0.96470588 0.97647059 - 0.97647059 0.97058824 0.95906433 0.97058824] - -mean value: 0.9670829033367733 - -key: test_accuracy -value: [0.76315789 0.92105263 0.71052632 0.92105263 0.92105263 0.81578947 - 0.92105263 0.78947368 0.86486486 0.78378378] - -mean value: 0.8411806543385492 - -key: train_accuracy -value: [0.98235294 0.96764706 0.97647059 0.96470588 0.97647059 0.97941176 - 0.97941176 0.97647059 0.97360704 0.97653959] - -mean value: 0.9753087804036571 - -key: test_roc_auc -value: [0.76315789 0.92105263 0.71052632 0.92105263 0.92105263 0.81578947 - 0.92105263 0.78947368 0.8625731 0.7880117 ] - -mean value: 0.8413742690058477 - -key: train_roc_auc -value: [0.98235294 0.96764706 0.97647059 0.96470588 0.97647059 0.97941176 - 0.97941176 0.97647059 0.97364981 0.97652219] - -mean value: 0.9753113175094601 - -key: test_jcc -value: [0.625 0.85714286 0.47619048 0.86363636 0.85714286 0.66666667 - 0.85714286 0.69230769 0.73684211 0.6 ] - -mean value: 0.7232071875492927 - -key: train_jcc -value: [0.96491228 0.93678161 0.95375723 0.93103448 0.95348837 0.95953757 - 0.95953757 0.95375723 0.94797688 0.95375723] - -mean value: 0.9514540444170766 - -key: TN -value: 162 - -mean value: 162.0 - -key: FP -value: 33 - -mean value: 33.0 - -key: FN -value: 27 - -mean value: 27.0 - -key: TP -value: 156 - -mean value: 156.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.58 - -Accuracy on Blind test: 0.79 - -Running classifier: 15 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', MultinomialNB())]) - -key: fit_time -value: [0.01357484 0.01096988 0.00983119 0.01637244 0.00990582 0.00953031 - 0.00932145 0.00958228 0.00970745 0.00929451] - -mean value: 0.010809016227722169 - -key: score_time -value: [0.01272416 0.00973797 0.00986862 0.0147357 0.00962996 0.00907183 - 0.00882053 0.00862551 0.00871229 0.00876045] - -mean value: 0.010068702697753906 - -key: test_mcc -value: [0.21320072 0.16641006 0.37047929 0.37047929 0.36842105 0.15877684 - 0.26462806 0.37047929 0.150005 0.13259028] - -mean value: 0.2565469865654629 - -key: train_mcc -value: [0.26574237 0.30607305 0.28828019 0.27060696 0.28252897 0.30588235 - 0.30000519 0.30590352 0.29643783 0.29660505] - -mean value: 0.29180654798197747 - -key: test_fscore -value: [0.57142857 0.5 0.7 0.66666667 0.68421053 0.55555556 - 0.65 0.7 0.61904762 0.61904762] - -mean value: 0.6265956558061822 - -key: train_fscore -value: [0.64788732 0.65895954 0.64094955 0.6374269 0.64739884 0.65294118 - 0.65102639 0.65497076 0.64285714 0.65517241] - -mean value: 0.6489590047244118 - -key: test_precision -value: [0.625 0.61538462 0.66666667 0.70588235 0.68421053 0.58823529 - 0.61904762 0.66666667 0.54166667 0.56521739] - -mean value: 0.6277977799111196 - -key: train_precision -value: [0.62162162 0.64772727 0.64670659 0.63372093 0.63636364 0.65294118 - 0.64912281 0.65116279 0.65454545 0.64044944] - -mean value: 0.6434361714704944 - -key: test_recall -value: [0.52631579 0.42105263 0.73684211 0.63157895 0.68421053 0.52631579 - 0.68421053 0.73684211 0.72222222 0.68421053] - -mean value: 0.6353801169590644 - -key: train_recall -value: [0.67647059 0.67058824 0.63529412 0.64117647 0.65882353 0.65294118 - 0.65294118 0.65882353 0.63157895 0.67058824] - -mean value: 0.654922600619195 - -key: test_accuracy -value: [0.60526316 0.57894737 0.68421053 0.68421053 0.68421053 0.57894737 - 0.63157895 0.68421053 0.56756757 0.56756757] - -mean value: 0.6266714082503556 - -key: train_accuracy -value: [0.63235294 0.65294118 0.64411765 0.63529412 0.64117647 0.65294118 - 0.65 0.65294118 0.64809384 0.64809384] - -mean value: 0.6457952389166811 - -key: test_roc_auc -value: [0.60526316 0.57894737 0.68421053 0.68421053 0.68421053 0.57894737 - 0.63157895 0.68421053 0.57163743 0.56432749] - -mean value: 0.6267543859649123 - -key: train_roc_auc -value: [0.63235294 0.65294118 0.64411765 0.63529412 0.64117647 0.65294118 - 0.65 0.65294118 0.64814241 0.64815961] - -mean value: 0.6458066735466115 - -key: test_jcc -value: [0.4 0.33333333 0.53846154 0.5 0.52 0.38461538 - 0.48148148 0.53846154 0.44827586 0.44827586] - -mean value: 0.4592905000491207 - -key: train_jcc -value: [0.47916667 0.49137931 0.47161572 0.46781116 0.47863248 0.48471616 - 0.4826087 0.48695652 0.47368421 0.48717949] - -mean value: 0.48037504072686216 - -key: TN -value: 117 - -mean value: 117.0 - -key: FP -value: 69 - -mean value: 69.0 - -key: FN -value: 72 - -mean value: 72.0 - -key: TP -value: 120 - -mean value: 120.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.26 - -Accuracy on Blind test: 0.62 - -Running classifier: 16 -Model_name: Passive Aggresive -Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.0143621 0.02125692 0.02209306 0.02404094 0.02061987 0.02214313 - 0.02414536 0.02194238 0.02830362 0.02095532] - -mean value: 0.0219862699508667 - -key: score_time -value: [0.00867319 0.01160479 0.01166391 0.01169205 0.01168108 0.01165795 - 0.01173759 0.01164198 0.01599741 0.01177049] - -mean value: 0.011812043190002442 - -key: test_mcc -value: [0.68803296 0.78947368 0.69989647 0.80757285 0.55708601 0.65465367 - 0.89973541 0.47519096 0.80369958 0.69356297] - -mean value: 0.7068904580864126 - -key: train_mcc -value: [0.94786272 0.94117647 0.9353103 0.83159022 0.75029297 0.72433672 - 0.91994182 0.81649658 0.91844838 0.95333798] - -mean value: 0.8738794139884047 - -key: test_fscore -value: [0.85 0.89473684 0.85714286 0.88235294 0.64285714 0.8372093 - 0.94444444 0.76 0.9 0.82352941] - -mean value: 0.8392272941816467 - -key: train_fscore -value: [0.9740634 0.97058824 0.96774194 0.89967638 0.84067797 0.86513995 - 0.95731707 0.90909091 0.95977011 0.97619048] - -mean value: 0.9320256435364644 - -key: test_precision -value: [0.80952381 0.89473684 0.7826087 1. 1. 0.75 - 1. 0.61290323 0.81818182 0.93333333] - -mean value: 0.860128772460285 - -key: train_precision -value: [0.95480226 0.97058824 0.96491228 1. 0.992 0.76233184 - 0.99367089 0.83333333 0.94350282 0.98795181] - -mean value: 0.9403093465944856 - -key: test_recall -value: [0.89473684 0.89473684 0.94736842 0.78947368 0.47368421 0.94736842 - 0.89473684 1. 1. 0.73684211] - -mean value: 0.8578947368421053 - -key: train_recall -value: [0.99411765 0.97058824 0.97058824 0.81764706 0.72941176 1. - 0.92352941 1. 0.97660819 0.96470588] - -mean value: 0.9347196422428621 - -key: test_accuracy -value: [0.84210526 0.89473684 0.84210526 0.89473684 0.73684211 0.81578947 - 0.94736842 0.68421053 0.89189189 0.83783784] - -mean value: 0.8387624466571836 - -key: train_accuracy -value: [0.97352941 0.97058824 0.96764706 0.90882353 0.86176471 0.84411765 - 0.95882353 0.9 0.95894428 0.97653959] - -mean value: 0.93207779886148 - -key: test_roc_auc -value: [0.84210526 0.89473684 0.84210526 0.89473684 0.73684211 0.81578947 - 0.94736842 0.68421053 0.89473684 0.84064327] - -mean value: 0.839327485380117 - -key: train_roc_auc -value: [0.97352941 0.97058824 0.96764706 0.90882353 0.86176471 0.84411765 - 0.95882353 0.9 0.95889233 0.97650499] - -mean value: 0.9320691434468523 - -key: test_jcc -value: [0.73913043 0.80952381 0.75 0.78947368 0.47368421 0.72 - 0.89473684 0.61290323 0.81818182 0.7 ] - -mean value: 0.7307634025136793 - -key: train_jcc -value: [0.9494382 0.94285714 0.9375 0.81764706 0.7251462 0.76233184 - 0.91812865 0.83333333 0.92265193 0.95348837] - -mean value: 0.876252273542207 - -key: TN -value: 155 - -mean value: 155.0 - -key: FP -value: 27 - -mean value: 27.0 - -key: FN -value: 34 - -mean value: 34.0 - -key: TP -value: 162 - -mean value: 162.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.73 - -Accuracy on Blind test: 0.86 - -Running classifier: 17 -Model_name: Stochastic GDescent -Model func: SGDClassifier(n_jobs=10, random_state=42) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', SGDClassifier(n_jobs=10, random_state=42))]) - -key: fit_time -value: [0.01865387 0.02316356 0.01780033 0.01992989 0.02176332 0.02368212 - 0.02137089 0.02858639 0.0251441 0.02628136] - -mean value: 0.022637581825256346 - -key: score_time -value: [0.01332283 0.0134902 0.01279187 0.01360536 0.01311684 0.01771569 - 0.0219903 0.01374888 0.01476026 0.01400638] - -mean value: 0.014854860305786134 - -key: test_mcc -value: [0.63245553 0.65465367 0.47809144 0.63960215 0.63828474 0.74620251 - 0.85280287 0.29277002 0.67434178 0.63309535] - -mean value: 0.6242300058520188 - -key: train_mcc -value: [0.91190671 0.83493231 0.80156851 0.83359019 0.65007241 0.83186096 - 0.80951605 0.44405304 0.65760625 0.70139855] - -mean value: 0.7476504986184476 - -key: test_fscore -value: [0.82051282 0.78787879 0.62068966 0.8 0.82608696 0.87804878 - 0.91428571 0.7037037 0.8372093 0.73333333] - -mean value: 0.7921749054221898 - -key: train_fscore -value: [0.95548961 0.90384615 0.87788779 0.91021672 0.83129584 0.91758242 - 0.88599349 0.74889868 0.83663366 0.79432624] - -mean value: 0.8662170604495014 - -key: test_precision -value: [0.8 0.92857143 0.9 0.875 0.7037037 0.81818182 - 1. 0.54285714 0.72 1. ] - -mean value: 0.8288314093314092 - -key: train_precision -value: [0.96407186 0.99295775 1. 0.96078431 0.71129707 0.86082474 - 0.99270073 0.59859155 0.72532189 1. ] - -mean value: 0.8806549897524336 - -key: test_recall -value: [0.84210526 0.68421053 0.47368421 0.73684211 1. 0.94736842 - 0.84210526 1. 1. 0.57894737] - -mean value: 0.8105263157894737 - -key: train_recall -value: [0.94705882 0.82941176 0.78235294 0.86470588 1. 0.98235294 - 0.8 1. 0.98830409 0.65882353] - -mean value: 0.8853009975920191 - -key: test_accuracy -value: [0.81578947 0.81578947 0.71052632 0.81578947 0.78947368 0.86842105 - 0.92105263 0.57894737 0.81081081 0.78378378] - -mean value: 0.7910384068278805 - -key: train_accuracy -value: [0.95588235 0.91176471 0.89117647 0.91470588 0.79705882 0.91176471 - 0.89705882 0.66470588 0.80645161 0.82991202] - -mean value: 0.858048128342246 - -key: test_roc_auc -value: [0.81578947 0.81578947 0.71052632 0.81578947 0.78947368 0.86842105 - 0.92105263 0.57894737 0.81578947 0.78947368] - -mean value: 0.7921052631578948 - -key: train_roc_auc -value: [0.95588235 0.91176471 0.89117647 0.91470588 0.79705882 0.91176471 - 0.89705882 0.66470588 0.80591675 0.82941176] - -mean value: 0.8579446164430686 - -key: test_jcc -value: [0.69565217 0.65 0.45 0.66666667 0.7037037 0.7826087 - 0.84210526 0.54285714 0.72 0.57894737] - -mean value: 0.6632541014371677 - -key: train_jcc -value: [0.91477273 0.8245614 0.78235294 0.83522727 0.71129707 0.84771574 - 0.79532164 0.59859155 0.71914894 0.65882353] - -mean value: 0.7687812804160212 - -key: TN -value: 146 - -mean value: 146.0 - -key: FP -value: 36 - -mean value: 36.0 - -key: FN -value: 43 - -mean value: 43.0 - -key: TP -value: 153 - -mean value: 153.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.72 - -Accuracy on Blind test: 0.86 - -Running classifier: 18 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.18785214 0.17773461 0.18532491 0.16907597 0.16370225 0.16271091 - 0.17593288 0.16754818 0.15381432 0.14739585] - -mean value: 0.16910920143127442 - -key: score_time -value: [0.01635075 0.01651716 0.01675177 0.0207417 0.02422071 0.01987314 - 0.0169642 0.01543593 0.01600432 0.0163753 ] - -mean value: 0.017923498153686525 - -key: test_mcc -value: [0.84327404 0.89973541 0.84327404 0.89973541 0.9486833 0.9486833 - 0.89473684 1. 0.94736842 0.89736456] - -mean value: 0.9122855328791111 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.91891892 0.95 0.91891892 0.95 0.97435897 0.97297297 - 0.94736842 1. 0.97297297 0.94444444] - -mean value: 0.9549955623639835 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.94444444 0.9047619 0.94444444 0.9047619 0.95 1. - 0.94736842 1. 0.94736842 1. ] - -mean value: 0.9543149540517961 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.89473684 1. 0.89473684 1. 1. 0.94736842 - 0.94736842 1. 1. 0.89473684] - -mean value: 0.9578947368421054 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92105263 0.94736842 0.92105263 0.94736842 0.97368421 0.97368421 - 0.94736842 1. 0.97297297 0.94594595] - -mean value: 0.9550497866287339 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.92105263 0.94736842 0.92105263 0.94736842 0.97368421 0.97368421 - 0.94736842 1. 0.97368421 0.94736842] - -mean value: 0.9552631578947368 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85 0.9047619 0.85 0.9047619 0.95 0.94736842 - 0.9 1. 0.94736842 0.89473684] - -mean value: 0.9148997493734337 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 180 - -mean value: 180.0 - -key: FP -value: 8 - -mean value: 8.0 - -key: FN -value: 9 - -mean value: 9.0 - -key: TP -value: 181 - -mean value: 181.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.95 - -Accuracy on Blind test: 0.97 - -Running classifier: 19 -Model_name: Bagging Classifier -Model func: BaggingClassifier(n_jobs=10, 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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', - BaggingClassifier(n_jobs=10, oob_score=True, - random_state=42))]) - -key: fit_time -value: [0.03417587 0.04569674 0.06490278 0.05447721 0.05367684 0.05330181 - 0.05160975 0.04998469 0.04956794 0.05220819] - -mean value: 0.05096018314361572 - -key: score_time -value: [0.01767707 0.03466558 0.02043867 0.02243829 0.0180974 0.02084994 - 0.02497602 0.02161694 0.0347724 0.0240984 ] - -mean value: 0.023963069915771483 - -key: test_mcc -value: [0.89473684 0.89973541 0.84327404 0.84327404 1. 0.89973541 - 0.9486833 0.9486833 0.94736842 0.84959079] - -mean value: 0.907508155685268 - 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-mean value: 0.9935294117647059 - -key: test_accuracy -value: [0.94736842 0.94736842 0.92105263 0.92105263 1. 0.94736842 - 0.97368421 0.97368421 0.97297297 0.91891892] - -mean value: 0.9523470839260313 - -key: train_accuracy -value: [0.99117647 0.99705882 0.99705882 1. 0.99705882 0.99117647 - 0.99117647 0.99705882 0.99706745 0.99706745] - -mean value: 0.9955899603243058 - -key: test_roc_auc -value: [0.94736842 0.94736842 0.92105263 0.92105263 1. 0.94736842 - 0.97368421 0.97368421 0.97368421 0.92105263] - -mean value: 0.9526315789473683 - -key: train_roc_auc -value: [0.99117647 0.99705882 0.99705882 1. 0.99705882 0.99117647 - 0.99117647 0.99705882 0.99705882 0.99705882] - -mean value: 0.9955882352941178 - -key: test_jcc -value: [0.9 0.9047619 0.85 0.85714286 1. 0.89473684 - 0.95 0.94736842 0.94736842 0.84210526] - -mean value: 0.9093483709273184 - -key: train_jcc -value: [0.98245614 0.99411765 0.99411765 1. 0.99411765 0.98255814 - 0.98235294 0.99411765 0.99418605 0.99411765] - -mean value: 0.9912141502867977 - 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dtype='object', length=169)), - ('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: [0.09543562 0.2472887 0.20239449 0.18664932 0.15393376 0.09733796 - 0.09409285 0.09378242 0.09236574 0.12468719] - -mean value: 0.13879680633544922 - -key: score_time -value: [0.02156401 0.02162814 0.02199697 0.02589583 0.02167106 0.02212262 - 0.02591872 0.02162528 0.02147341 0.02145791] - -mean value: 0.02253539562225342 - -key: test_mcc -value: [0.47368421 0.17407766 0. 0.53300179 0.57894737 0.32732684 - 0.36842105 0.37686733 0.30384671 0.40469382] - -mean value: 0.35408667738076577 - -key: train_mcc -value: [0.92354539 0.9353103 0.92354539 0.94117647 0.94707521 0.91764706 - 0.90594505 0.92947609 0.92389212 0.91794819] - -mean value: 0.9265561272730107 - -key: test_fscore -value: [0.73684211 0.46666667 0.48648649 0.74285714 0.78947368 0.60606061 - 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warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") -[0.73684211 0.36842105 0.47368421 0.68421053 0.78947368 0.52631579 - 0.68421053 0.78947368 0.72222222 0.73684211] - 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0.52 0.55555556 0.5 0.56 ] - -mean value: 0.5022530899052638 - -key: train_jcc -value: [0.92655367 0.9375 0.92655367 0.94285714 0.94857143 0.92090395 - 0.90960452 0.93142857 0.9273743 0.92045455] - -mean value: 0.9291801809196704 - -key: TN -value: 132 - -mean value: 132.0 - -key: FP -value: 66 - -mean value: 66.0 - -key: FN -value: 57 - -mean value: 57.0 - -key: TP -value: 123 - -mean value: 123.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.34 - -Accuracy on Blind test: 0.67 - -Running classifier: 21 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.58275342 0.56033206 0.55378675 0.54357123 0.54775739 0.548141 - 0.54349136 0.54084659 0.54360294 0.53465486] - -mean value: 0.5498937606811524 - -key: score_time -value: [0.0103116 0.00930452 0.00959516 0.00907445 0.00908446 0.00913668 - 0.00937724 0.00914311 0.00933385 0.00930929] - -mean value: 0.009367036819458007 - -key: test_mcc -value: [0.84327404 0.89973541 0.9486833 0.89973541 0.89473684 0.9486833 - 0.9486833 0.9486833 0.94736842 0.89736456] - -mean value: 0.9176947882180571 - -key: train_mcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_fscore -value: [0.92307692 0.95 0.97435897 0.95 0.94736842 0.97297297 - 0.97435897 0.97297297 0.97297297 0.94444444] - -mean value: 0.9582526656210867 - -key: train_fscore -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_precision -value: [0.9 0.9047619 0.95 0.9047619 0.94736842 1. - 0.95 1. 0.94736842 1. ] - -mean value: 0.9504260651629073 - -key: train_precision -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_recall -value: [0.94736842 1. 1. 1. 0.94736842 0.94736842 - 1. 0.94736842 1. 0.89473684] - -mean value: 0.968421052631579 - -key: train_recall -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_accuracy -value: [0.92105263 0.94736842 0.97368421 0.94736842 0.94736842 0.97368421 - 0.97368421 0.97368421 0.97297297 0.94594595] - -mean value: 0.9576813655761024 - -key: train_accuracy -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_roc_auc -value: [0.92105263 0.94736842 0.97368421 0.94736842 0.94736842 0.97368421 - 0.97368421 0.97368421 0.97368421 0.94736842] - -mean value: 0.9578947368421051 - -key: train_roc_auc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: test_jcc -value: [0.85714286 0.9047619 0.95 0.9047619 0.9 0.94736842 - 0.95 0.94736842 0.94736842 0.89473684] - -mean value: 0.9203508771929826 - -key: train_jcc -value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] - -mean value: 1.0 - -key: TN -value: 179 - -mean value: 179.0 - -key: FP -value: 6 - -mean value: 6.0 - -key: FN -value: 10 - -mean value: 10.0 - -key: TP -value: 183 - -mean value: 183.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.92 - -Accuracy on Blind test: 0.96 - -Running classifier: 22 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', QuadraticDiscriminantAnalysis())]) - -key: fit_time -value: [0.02372193 0.11949515 0.09284544 0.06949425 0.02872419 0.02634549 - 0.02604413 0.0261867 0.02649474 0.02665615] - -mean value: 0.0466008186340332 - -key: score_time -value: [0.01266813 0.02644086 0.01325297 0.01300836 0.01368618 0.01373649 - 0.01375484 0.01376176 0.0138309 0.01402521] - -mean value: 0.014816570281982421 - -key: test_mcc -value: [0.31980107 0.05547002 0.15877684 0.31980107 0.63960215 0.67936622 - 0.58218174 0.22645541 0.51478965 0.18768409] - -mean value: 0.36839282598856604 - -key: train_mcc -value: [0.91533482 0.98236994 0.98250594 0.94838881 0.89810426 0.976741 - 0.84174979 0.90594505 0.97069043 0.80693921] - -mean value: 0.9228769257292058 - -key: test_fscore -value: [0.62857143 0.4375 0.6 0.62857143 0.82926829 0.84444444 - 0.77777778 0.66666667 0.77272727 0.61538462] - -mean value: 0.6800911926826562 - -key: train_fscore -value: [0.95384615 0.99115044 0.99109792 0.97280967 0.9494382 0.98837209 - 0.90675241 0.95321637 0.98542274 0.88157895] - -mean value: 0.9573684955854628 - -key: test_precision -value: [0.6875 0.53846154 0.57142857 0.6875 0.77272727 0.73076923 - 0.82352941 0.57692308 0.65384615 0.6 ] - -mean value: 0.664268525592055 - -key: train_precision -value: [1. 0.99408284 1. 1. 0.90860215 0.97701149 - 1. 0.94767442 0.98255814 1. ] - -mean value: 0.980992904316673 - -key: test_recall -value: [0.57894737 0.36842105 0.63157895 0.57894737 0.89473684 1. - 0.73684211 0.78947368 0.94444444 0.63157895] - -mean value: 0.7154970760233919 - -key: train_recall -value: [0.91176471 0.98823529 0.98235294 0.94705882 0.99411765 1. - 0.82941176 0.95882353 0.98830409 0.78823529] - -mean value: 0.9388304093567251 - -key: test_accuracy -value: [0.65789474 0.52631579 0.57894737 0.65789474 0.81578947 0.81578947 - 0.78947368 0.60526316 0.72972973 0.59459459] -/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear - warnings.warn("Variables are collinear") - -mean value: 0.6771692745376956 - -key: train_accuracy -value: [0.95588235 0.99117647 0.99117647 0.97352941 0.94705882 0.98823529 - 0.91470588 0.95294118 0.98533724 0.89442815] - -mean value: 0.9594471278247368 - -key: test_roc_auc -value: [0.65789474 0.52631579 0.57894737 0.65789474 0.81578947 0.81578947 - 0.78947368 0.60526316 0.73538012 0.59356725] - -mean value: 0.6776315789473684 - -key: train_roc_auc -value: [0.95588235 0.99117647 0.99117647 0.97352941 0.94705882 0.98823529 - 0.91470588 0.95294118 0.98532852 0.89411765] - -mean value: 0.9594152046783627 - -key: test_jcc -value: [0.45833333 0.28 0.42857143 0.45833333 0.70833333 0.73076923 - 0.63636364 0.5 0.62962963 0.44444444] - -mean value: 0.527477836977837 - -key: train_jcc -value: [0.91176471 0.98245614 0.98235294 0.94705882 0.90374332 0.97701149 - 0.82941176 0.91061453 0.97126437 0.78823529] - -mean value: 0.9203913372479293 - -key: TN -value: 123 - -mean value: 123.0 - -key: FP -value: 58 - -mean value: 58.0 - -key: FN -value: 66 - -mean value: 66.0 - -key: TP -value: 131 - -mean value: 131.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.36 - -Accuracy on Blind test: 0.68 - -Running classifier: 23 -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', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('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: [0.01482964 0.01921058 0.02182555 0.04833794 0.03775072 0.03403735 - 0.03341413 0.0305233 0.02764773 0.03826261] - -mean value: 0.030583953857421874 - -key: score_time -value: [0.0120647 0.01216769 0.01974583 0.02638054 0.01293182 0.02799201 - 0.02798343 0.05023241 0.02441835 0.01207447] - -mean value: 0.022599124908447267 - -key: test_mcc -value: [0.84327404 0.73786479 0.65465367 0.9486833 0.79388419 0.89973541 - 0.78947368 0.73786479 0.89736456 0.69356297] - -mean value: 0.7996361400329882 - -key: train_mcc -value: [0.92999118 0.91821914 0.93102393 0.91923882 0.93660759 0.91821914 - 0.92999118 0.91923882 0.92415697 0.94160363] - -mean value: 0.9268290384615124 - -key: test_fscore -value: [0.91891892 0.86486486 0.78787879 0.97435897 0.88888889 0.94444444 - 0.89473684 0.87179487 0.94736842 0.82352941] - -mean value: 0.8916784426072353 - -key: train_fscore -value: [0.96407186 0.95808383 0.96363636 0.95757576 0.96676737 0.95808383 - 0.96407186 0.95757576 0.96142433 0.9702381 ] - -mean value: 0.9621529055216904 - -key: test_precision -value: [0.94444444 0.88888889 0.92857143 0.95 0.94117647 1. - 0.89473684 0.85 0.9 0.93333333] - -mean value: 0.9231151407931595 - -key: train_precision -value: [0.98170732 0.97560976 0.99375 0.9875 0.99378882 0.97560976 - 0.98170732 0.9875 0.97590361 0.98192771] - -mean value: 0.9835004291518444 - -key: test_recall -value: [0.89473684 0.84210526 0.68421053 1. 0.84210526 0.89473684 - 0.89473684 0.89473684 1. 0.73684211] - -mean value: 0.868421052631579 - -key: train_recall -value: [0.94705882 0.94117647 0.93529412 0.92941176 0.94117647 0.94117647 - 0.94705882 0.92941176 0.94736842 0.95882353] - -mean value: 0.941795665634675 - -key: test_accuracy -value: [0.92105263 0.86842105 0.81578947 0.97368421 0.89473684 0.94736842 - 0.89473684 0.86842105 0.94594595 0.83783784] - -mean value: 0.8967994310099574 - -key: train_accuracy -value: [0.96470588 0.95882353 0.96470588 0.95882353 0.96764706 0.95882353 - 0.96470588 0.95882353 0.96187683 0.97067449] - -mean value: 0.9629610143177507 - -key: test_roc_auc -value: [0.92105263 0.86842105 0.81578947 0.97368421 0.89473684 0.94736842 - 0.89473684 0.86842105 0.94736842 0.84064327] - -mean value: 0.8972222222222224 - -key: train_roc_auc -value: [0.96470588 0.95882353 0.96470588 0.95882353 0.96764706 0.95882353 - 0.96470588 0.95882353 0.9619195 0.97063983] - -mean value: 0.9629618163054696 - -key: test_jcc -value: [0.85 0.76190476 0.65 0.95 0.8 0.89473684 - 0.80952381 0.77272727 0.9 0.7 ] - -mean value: 0.8088892686261108 - -key: train_jcc -value: [0.93063584 0.91954023 0.92982456 0.91860465 0.93567251 0.91954023 - 0.93063584 0.91860465 0.92571429 0.94219653] - -mean value: 0.9270969331925858 - -key: TN -value: 175 - -mean value: 175.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 164 - -mean value: 164.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 - -Running classifier: 24 -Model_name: Ridge ClassifierCV -Model func: RidgeClassifierCV(cv=10) - -Running model pipeline: Pipeline(steps=[('prep', - ColumnTransformer(remainder='passthrough', - transformers=[('num', MinMaxScaler(), - Index(['consurf_score', 'snap2_score', 'provean_score', - 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', - 'contacts', 'electro_rr', 'electro_mm', - ... - 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', - 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', - 'lineage_count_unique'], - dtype='object', length=169)), - ('cat', OneHotEncoder(), - Index(['ss_class', 'aa_prop_change', 'electrostatics_change', - 'polarity_change', 'water_change', 'active_site'], - dtype='object'))])), - ('model', RidgeClassifierCV(cv=10))]) - -key: fit_time -value: [0.29604077 0.39670277 0.19968796 0.29961491 0.36493897 0.33663177 - 0.26365566 0.39019799 0.39198065 0.3928957 ] - -mean value: 0.33323471546173095 - -key: score_time -value: [0.02103925 0.02264595 0.01202512 0.02318287 0.02210951 0.02111554 - 0.02328563 0.023489 0.0272603 0.01209092] - -mean value: 0.020824408531188963 - -key: test_mcc -value: [0.84327404 0.73786479 0.65465367 0.9486833 0.79388419 0.89973541 - 0.78947368 0.73786479 0.89736456 0.69356297] - -mean value: 0.7996361400329882 - -key: train_mcc -value: [0.92999118 0.91821914 0.93102393 0.91923882 0.93660759 0.91821914 - 0.92999118 0.91923882 0.92415697 0.94160363] - -mean value: 0.9268290384615124 - -key: test_fscore -value: [0.91891892 0.86486486 0.78787879 0.97435897 0.88888889 0.94444444 - 0.89473684 0.87179487 0.94736842 0.82352941] - -mean value: 0.8916784426072353 - -key: train_fscore -value: [0.96407186 0.95808383 0.96363636 0.95757576 0.96676737 0.95808383 - 0.96407186 0.95757576 0.96142433 0.9702381 ] - -mean value: 0.9621529055216904 - -key: test_precision -value: [0.94444444 0.88888889 0.92857143 0.95 0.94117647 1. - 0.89473684 0.85 0.9 0.93333333] - -mean value: 0.9231151407931595 - -key: train_precision -value: [0.98170732 0.97560976 0.99375 0.9875 0.99378882 0.97560976 - 0.98170732 0.9875 0.97590361 0.98192771] - -mean value: 0.9835004291518444 - -key: test_recall -value: [0.89473684 0.84210526 0.68421053 1. 0.84210526 0.89473684 - 0.89473684 0.89473684 1. 0.73684211] - -mean value: 0.868421052631579 - -key: train_recall -value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: 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 - rouC_CV['Data_source'] = 'CV' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: 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 - rouC_CV['Resampling'] = rs_rouC -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: 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 - rouC_BT['Data_source'] = 'BT' -/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: 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 - rouC_BT['Resampling'] = rs_rouC -[0.94705882 0.94117647 0.93529412 0.92941176 0.94117647 0.94117647 - 0.94705882 0.92941176 0.94736842 0.95882353] - -mean value: 0.941795665634675 - -key: test_accuracy -value: [0.92105263 0.86842105 0.81578947 0.97368421 0.89473684 0.94736842 - 0.89473684 0.86842105 0.94594595 0.83783784] - -mean value: 0.8967994310099574 - -key: train_accuracy -value: [0.96470588 0.95882353 0.96470588 0.95882353 0.96764706 0.95882353 - 0.96470588 0.95882353 0.96187683 0.97067449] - -mean value: 0.9629610143177507 - -key: test_roc_auc -value: [0.92105263 0.86842105 0.81578947 0.97368421 0.89473684 0.94736842 - 0.89473684 0.86842105 0.94736842 0.84064327] - -mean value: 0.8972222222222224 - -key: train_roc_auc -value: [0.96470588 0.95882353 0.96470588 0.95882353 0.96764706 0.95882353 - 0.96470588 0.95882353 0.9619195 0.97063983] - -mean value: 0.9629618163054696 - -key: test_jcc -value: [0.85 0.76190476 0.65 0.95 0.8 0.89473684 - 0.80952381 0.77272727 0.9 0.7 ] - -mean value: 0.8088892686261108 - -key: train_jcc -value: [0.93063584 0.91954023 0.92982456 0.91860465 0.93567251 0.91954023 - 0.93063584 0.91860465 0.92571429 0.94219653] - -mean value: 0.9270969331925858 - -key: TN -value: 175 - -mean value: 175.0 - -key: FP -value: 25 - -mean value: 25.0 - -key: FN -value: 14 - -mean value: 14.0 - -key: TP -value: 164 - -mean value: 164.0 - -key: trainingY_neg -value: 189 - -mean value: 189.0 - -key: trainingY_pos -value: 189 - -mean value: 189.0 - -key: blindY_neg -value: 93 - -mean value: 93.0 - -key: blindY_pos -value: 91 - -mean value: 91.0 - -MCC on Blind test: 0.71 - -Accuracy on Blind test: 0.85 - -PASS: sorting df by score that is mapped onto the order I want - -Concatenating dfs with different resampling methods [WF]: 70/30 -No. of dfs combining: 10 -The sampling methods are: - Resampling -Logistic Regression none -Logistic Regression smnc -Logistic Regression ros -Logistic Regression rus -Logistic Regression rouC - -PASS: 10 dfs successfully combined -nrows in combined_df_wf: 240 -ncols in combined_df_wf: 9 - -Concatenating dfs with different resampling methods: 70/30 -No. of dfs combining: 5 -The sampling methods are: - Resampling training_size -0 none 373 -24 smnc 378 -48 ros 378 -72 rus 368 -96 rouC 378 - -PASS: 5 dfs successfully combined -nrows in combined_df: 120 -ncols in combined_df: 32 - -File successfully written: /home/tanu/git/Data/rifampicin/output/ml/tts_7030/rpob_baselineC_7030.csv - -File successfully written: /home/tanu/git/Data/rifampicin/output/ml/tts_7030/rpob_baselineC_ext_7030.csv diff --git a/scripts/ml/ml_data_7030.py b/scripts/ml/ml_data_7030.py index d7300f7..560d06f 100644 --- a/scripts/ml/ml_data_7030.py +++ b/scripts/ml/ml_data_7030.py @@ -557,7 +557,7 @@ def setvars(gene,drug): # FG5: Genomic features #======================== X_gn_mafor_Fnum = ['maf' - , 'logorI' + #, 'logorI' # , 'or_rawI' # , 'or_mychisq' # , 'or_logistic' diff --git a/scripts/ml/run_7030.py b/scripts/ml/run_7030.py old mode 100755 new mode 100644 index 902c5fe..a963161 --- a/scripts/ml/run_7030.py +++ b/scripts/ml/run_7030.py @@ -5,28 +5,28 @@ Created on Mon Jun 20 13:05:23 2022 @author: tanu """ +#%%Imports #################################################################### import re import argparse -############################################################################### +import os, sys + # gene = 'pncA' # drug = 'pyrazinamide' #total_mtblineage_uc = 8 - +############################################################################### #%% command line args: case sensitive -arg_parser = argparse.ArgumentParser() -arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '') -arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') -args = arg_parser.parse_args() +# arg_parser = argparse.ArgumentParser() +# arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '') +# arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') +# args = arg_parser.parse_args() -drug = args.drug -gene = args.gene +# drug = args.drug +# gene = args.gene ############################################################################### -#================== -# other vars -#================== -tts_split = '70/30' -OutFile_suffix = '7030' +homedir = os.path.expanduser("~") +sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml') + ############################################################################### #================== # Import data @@ -39,10 +39,15 @@ from ml_data_7030 import * #from UQ_yc_RunAllClfs import run_all_ML #==================== -# Import ML function +# Import ML functions #==================== -# TT run all ML clfs: baseline model -from MultModelsCl import MultModelsCl +from MultClfs import * + +#================== +# other vars +#================== +tts_split_7030 = '70_30' +OutFile_suffix = '7030' #================== # Specify outdir @@ -51,563 +56,71 @@ outdir_ml = outdir + 'ml/tts_7030/' print('\nOutput directory:', outdir_ml) outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv' -outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + OutFile_suffix + '.csv' +#outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + OutFile_suffix + '.csv' -############################################################################### -score_type_ordermapD = { 'mcc' : 1 - , 'fscore' : 2 - , 'jcc' : 3 - , 'precision' : 4 - , 'recall' : 5 - , 'accuracy' : 6 - , 'roc_auc' : 7 - , 'TN' : 8 - , 'FP' : 9 - , 'FN' : 10 - , 'TP' : 11 - , 'trainingY_neg': 12 - , 'trainingY_pos': 13 - , 'blindY_neg' : 14 - , 'blindY_pos' : 15 - , 'fit_time' : 16 - , 'score_time' : 17 - } - -scoreCV_mapD = {'test_mcc' : 'MCC' - , 'test_fscore' : 'F1' - , 'test_precision' : 'Precision' - , 'test_recall' : 'Recall' - , 'test_accuracy' : 'Accuracy' - , 'test_roc_auc' : 'ROC_AUC' - , 'test_jcc' : 'JCC' - } - -scoreBT_mapD = {'bts_mcc' : 'MCC' - , 'bts_fscore' : 'F1' - , 'bts_precision' : 'Precision' - , 'bts_recall' : 'Recall' - , 'bts_accuracy' : 'Accuracy' - , 'bts_roc_auc' : 'ROC_AUC' - , 'bts_jcc' : 'JCC' - } - -# data dependent variables but NOT dependent on resampling -bts_size = len(X_bts) -yc2 = Counter(y_bts) -yc2_ratio = yc2[0]/yc2[1] - -############################################################################### +#%% Running models ############################################################ print('\n#####################################################################\n' - , '\nRunning ML analysis: feature groups ' + , '\nStarting--> Running ML analysis: Baseline modes (No FS)' , '\nGene name:', gene - , '\nDrug name:', drug) + , '\nDrug name:', drug + , '\n#####################################################################\n') -#%% Basic: No Oversampling -#================ -# Baseline -# No resampling -#================ -scores_mmD = MultModelsCl(input_df = X - , target = y - , var_type = 'mixed' - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -baseline_all_scores = pd.DataFrame(scores_mmD) -rs_none = 'none' -#------------------------ -# WF: only CV and BTS -#----------------------- -baseline_allT = baseline_all_scores.T -#baseline_train = baseline_all.filter(regex='train_', axis=1) - -baseline_CV = baseline_allT.filter(regex='test_', axis = 1); baseline_CV.columns -# map colnames for consistency to allow concatenting -baseline_CV.columns = baseline_CV.columns.map(scoreCV_mapD); baseline_CV.columns -baseline_CV['Data_source'] = 'CV' -baseline_CV['Resampling'] = rs_none - -baseline_BT = baseline_allT.filter(regex='bts_', axis = 1); baseline_BT.columns -# map colnames for consistency to allow concatenting -baseline_BT.columns = baseline_BT.columns.map(scoreBT_mapD); baseline_BT.columns -baseline_BT['Data_source'] = 'BT' -baseline_BT['Resampling'] = rs_none - -# # Write csv -#baseline_CV.sort_values(by = ['test_mcc'], ascending = False, inplace = True) -#baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) -# baseline_CV.to_csv(outdir_ml + gene.lower() + '_baseline_CV_allF.csv') -# baseline_BT.to_csv(outdir_ml + gene.lower() + '_baseline_BT_allF.csv') - -#---------------------------------- -# LF*: CV + BTS + Other info -#----------------------------------- -# other data dependent variables -training_size_ns = len(X) -n_features = len(X.columns) -yc1 = Counter(y) -yc1_ratio = yc1[0]/yc1[1] - -baseline_all = baseline_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) -baseline_all = baseline_all.reset_index() -baseline_all.rename(columns = {'index': 'original_names'}, inplace = True) - -# Indicate whether BT or CV -bt_pattern = re.compile(r'bts_.*') -baseline_all['Data_source'] = baseline_all.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) - -baseline_all['score_type'] = baseline_all['original_names'].str.replace('bts_|test_', '', regex = True) - -score_type_uniqueN = set(baseline_all['score_type']) -cL1 = list(score_type_ordermapD.keys()) -cL2 = list(score_type_uniqueN) - -if set(cL1).issubset(cL2): - print('\nPASS: sorting df by score that is mapped onto the order I want') - baseline_all['score_order'] = baseline_all['score_type'].map(score_type_ordermapD) - baseline_all.sort_values(by = ['Data_source', 'score_order'], ascending = [True, True], inplace = True) -else: - sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') - -# add cols: specific -baseline_all['Resampling'] = rs_none -baseline_all['training_size'] = training_size_ns -baseline_all['trainingY_ratio']= round(yc1_ratio,2) -baseline_all['n_features'] = n_features - -############################################################################### -#%% SMOTE NC: Smote Oversampling [Numerical + categorical] -#================ -# Baseline -# SMOTE NC: SMNC -#================ -smnc_scores_mmD = MultModelsCl(input_df = X_smnc - , target = y_smnc - , var_type = 'mixed' - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -smnc_all_scores = pd.DataFrame(smnc_scores_mmD) -rs_smnc = 'smnc' -#------------------------ -# WF: only CV and BTS -#----------------------- -smnc_allT = smnc_all_scores.T - -smnc_CV = smnc_allT.filter(regex='test_', axis = 1); smnc_CV.columns -# map colnames for consistency to allow concatenting -smnc_CV.columns = smnc_CV.columns.map(scoreCV_mapD); smnc_CV.columns -smnc_CV['Data_source'] = 'CV' -smnc_CV['Resampling'] = rs_smnc - -smnc_BT = smnc_allT.filter(regex='bts_', axis = 1); smnc_BT.columns -# map colnames for consistency to allow concatenting -smnc_BT.columns = smnc_BT.columns.map(scoreBT_mapD); smnc_BT.columns -smnc_BT['Data_source'] = 'BT' -smnc_BT['Resampling'] = rs_smnc - -# Write csv -# smnc_CV.sort_values(by = ['test_mcc'], ascending = False, inplace = True) -# smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) -# smnc_CV.to_csv(outdir_ml + gene.lower() + '_smnc_CV_allF.csv') -# smnc_BT.to_csv(outdir_ml + gene.lower() + '_smnc_BT_allF.csv') - -#---------------------------------- -# LF*: CV + BTS + Other info -#----------------------------------- -# other data dependent variables -training_size_smnc = len(X_smnc) -n_features = len(X_smnc.columns) -yc1_smnc = Counter(y_smnc) -yc1_ratio_smnc = yc1_smnc[0]/yc1_smnc[1] - -smnc_all = smnc_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) -smnc_all = smnc_all.reset_index() -smnc_all.rename(columns = {'index': 'original_names'}, inplace = True) - -# Indicate whether BT or CV -bt_pattern = re.compile(r'bts_.*') -smnc_all['Data_source'] = smnc_all.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) - -smnc_all['score_type'] = smnc_all['original_names'].str.replace('bts_|test_', '', regex = True) - -score_type_uniqueN = set(smnc_all['score_type']) -cL1 = list(score_type_ordermapD.keys()) -cL2 = list(score_type_uniqueN) - -if set(cL1).issubset(cL2): - print('\nPASS: sorting df by score that is mapped onto the order I want') - smnc_all['score_order'] = smnc_all['score_type'].map(score_type_ordermapD) - smnc_all.sort_values(by = ['Data_source', 'score_order'], ascending = [True, True], inplace = True) -else: - sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') - -# add cols: specific -smnc_all['Resampling'] = rs_smnc -smnc_all['training_size'] = training_size_smnc -smnc_all['trainingY_ratio'] = round(yc1_ratio_smnc,2) -smnc_all['n_features'] = n_features - -############################################################################### -#%% ROS: Random Over Sampling [Numerical + categorical] -#================ -# Baseline -# ROS -#================ -ros_scores_mmD = MultModelsCl(input_df = X_ros - , target = y_ros - , var_type = 'mixed' - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -ros_all_scores = pd.DataFrame(ros_scores_mmD) -rs_ros = 'ros' -#------------------------ -# WF: only CV and BTS -#----------------------- -ros_allT = ros_all_scores.T - -ros_CV = ros_allT.filter(regex='test_', axis = 1); ros_CV.columns -# map colnames for consistency to allow concatenting -ros_CV.columns = ros_CV.columns.map(scoreCV_mapD); ros_CV.columns -ros_CV['Data_source'] = 'CV' -ros_CV['Resampling'] = rs_ros - -ros_BT = ros_allT.filter(regex='bts_', axis = 1); ros_BT.columns -# map colnames for consistency to allow concatenting -ros_BT.columns = ros_BT.columns.map(scoreBT_mapD); ros_BT.columns -ros_BT['Data_source'] = 'BT' -ros_BT['Resampling'] = rs_ros - -# Write csv -# ros_CV.sort_values(by = ['test_mcc'], ascending = False, inplace = True) -# ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) -# ros_CV.to_csv(outdir_ml + gene.lower() + '_ros_CV_allF.csv') -# ros_BT.to_csv(outdir_ml + gene.lower() + '_ros_BT_allF.csv') -#---------------------------------- -# LF*: CV + BTS + Other info -#---------------------------------- -# other data dependent variables -training_size_ros = len(X_ros) -n_features = len(X_ros.columns) -yc1_ros = Counter(y_ros) -yc1_ratio_ros = yc1_ros[0]/yc1_ros[1] - -ros_all = ros_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) -ros_all = ros_all.reset_index() -ros_all.rename(columns = {'index': 'original_names'}, inplace = True) - -# Indicate whether BT or CV -bt_pattern = re.compile(r'bts_.*') -ros_all['Data_source'] = ros_all.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) - -ros_all['score_type'] = ros_all['original_names'].str.replace('bts_|test_', '', regex = True) - -score_type_uniqueN = set(ros_all['score_type']) -cL1 = list(score_type_ordermapD.keys()) -cL2 = list(score_type_uniqueN) - -if set(cL1).issubset(cL2): - print('\nPASS: sorting df by score that is mapped onto the order I want') - ros_all['score_order'] = ros_all['score_type'].map(score_type_ordermapD) - ros_all.sort_values(by = ['Data_source', 'score_order'], ascending = [True, True], inplace = True) -else: - sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') - -# add cols: specific -ros_all['Resampling'] = rs_ros -ros_all['training_size'] = training_size_ros -ros_all['trainingY_ratio'] = round(yc1_ratio_ros,2) -ros_all['n_features'] = n_features -############################################################################### -#%% RUS: Random Under Sampling [Numerical + categorical] -#================ -# Baseline -# RUS -#================ -rus_scores_mmD = MultModelsCl(input_df = X_rus - , target = y_rus - , var_type = 'mixed' - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -rus_all_scores = pd.DataFrame(rus_scores_mmD) -rs_rus = 'rus' -#----------------------- -# WF: only CV and BTS -#----------------------- -rus_allT = rus_all_scores.T - -rus_CV = rus_allT.filter(regex='test_', axis=1); rus_CV.columns -# map colnames for consistency to allow concatenting -rus_CV.columns = rus_CV.columns.map(scoreCV_mapD); rus_CV.columns -rus_CV['Data_source'] = 'CV' -rus_CV['Resampling'] = rs_rus - -rus_BT = rus_allT.filter(regex='bts_' , axis=1); rus_BT.columns -# map colnames for consistency to allow concatenting -rus_BT.columns = rus_BT.columns.map(scoreBT_mapD); rus_BT.columns -rus_BT['Data_source'] = 'BT' -rus_BT['Resampling'] = rs_rus - -# # Write csv -# rus_CV.sort_values(by = ['test_mcc'], ascending = False, inplace = True) -# rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) -# rus_CV.to_csv(outdir_ml + gene.lower() + '_rus_CV_allF.csv') -# rus_BT.to_csv(outdir_ml + gene.lower() + '_rus_BT_allF.csv') - -#---------------------------------- -# LF*: CV + BTS + Other info -#---------------------------------- -# other data dependent variables -training_size_rus = len(X_rus) -n_features = len(X_rus.columns) -yc1_rus = Counter(y_rus) -yc1_ratio_rus = yc1_rus[0]/yc1_rus[1] - -rus_all = rus_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) -rus_all = rus_all.reset_index() -rus_all.rename(columns = {'index': 'original_names'}, inplace = True) - -# Indicate whether BT or CV -bt_pattern = re.compile(r'bts_.*') -rus_all['Data_source'] = rus_all.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) - -rus_all['score_type'] = rus_all['original_names'].str.replace('bts_|test_', '', regex = True) - -score_type_uniqueN = set(rus_all['score_type']) -cL1 = list(score_type_ordermapD.keys()) -cL2 = list(score_type_uniqueN) - -if set(cL1).issubset(cL2): - print('\nPASS: sorting df by score that is mapped onto the order I want') - rus_all['score_order'] = rus_all['score_type'].map(score_type_ordermapD) - rus_all.sort_values(by = ['Data_source', 'score_order'], ascending = [True, True], inplace = True) -else: - sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') - -# add cols: specific -rus_all['Resampling'] = rs_rus -rus_all['training_size'] = training_size_rus -rus_all['trainingY_ratio'] = round(yc1_ratio_rus,2) -rus_all['n_features'] = n_features - -############################################################################### -#%% ROS+RUS Combined: [Numerical + categorical] -#================ -# Baseline -# ROUC -#================ -rouC_scores_mmD = MultModelsCl(input_df = X_rouC - , target = y_rouC - , var_type = 'mixed' - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -rouC_all_scores = pd.DataFrame(rouC_scores_mmD) -rs_rouC = 'rouC' -#----------------------- -# WF: only CV and BTS -#----------------------- -rouC_allT = rouC_all_scores.T - -rouC_CV = rouC_allT.filter(regex='test_', axis=1); rouC_CV.columns -# map colnames for consistency to allow concatenting -rouC_CV.columns = rouC_CV.columns.map(scoreCV_mapD); rouC_CV.columns -rouC_CV['Data_source'] = 'CV' -rouC_CV['Resampling'] = rs_rouC - -rouC_BT = rouC_allT.filter(regex='bts_', axis=1); rouC_BT.columns -# map colnames for consistency to allow concatenting -rouC_BT.columns = rouC_BT.columns.map(scoreBT_mapD); rouC_BT.columns -rouC_BT['Data_source'] = 'BT' -rouC_BT['Resampling'] = rs_rouC - -# Write csv -# rouC_CV.sort_values(by = ['test_mcc'], ascending = False, inplace = True) -# rouC_CV.to_csv(outdir_ml + gene.lower() + '_rouC_CV_allF.csv') -# rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) -# rouC_BT.to_csv(outdir_ml + gene.lower() + '_rouC_BT_allF.csv') - -#---------------------------------- -# LF*: CV + BTS + Other info -#---------------------------------- -# other data dependent variables -training_size_rouC = len(X_rouC) -n_features = len(X_rouC.columns) -yc1_rouC = Counter(y_rouC) -yc1_ratio_rouC = yc1_rouC[0]/yc1_rouC[1] - -rouC_all = rouC_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) -rouC_all = rouC_all.reset_index() -rouC_all.rename(columns = {'index': 'original_names'}, inplace = True) - -# Indicate whether BT or CV -bt_pattern = re.compile(r'bts_.*') -rouC_all['Data_source'] = rouC_all.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) - -rouC_all['score_type'] = rouC_all['original_names'].str.replace('bts_|test_', '', regex = True) - -score_type_uniqueN = set(rouC_all['score_type']) -cL1 = list(score_type_ordermapD.keys()) -cL2 = list(score_type_uniqueN) - -if set(cL1).issubset(cL2): - print('\nPASS: sorting df by score that is mapped onto the order I want') - rouC_all['score_order'] = rouC_all['score_type'].map(score_type_ordermapD) - rouC_all.sort_values(by = ['Data_source', 'score_order'], ascending = [True, True], inplace = True) -else: - sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') - -# add cols: specific -rouC_all['Resampling'] = rs_rouC -rouC_all['training_size'] = training_size_rouC -rouC_all['trainingY_ratio'] = round(yc1_ratio_rouC,2) -rouC_all['n_features'] = n_features - -############################################################################### -#%% COMBINING all dfs: WF and LF -# https://stackoverflow.com/questions/39862654/pandas-concat-of-multiple-data-frames-using-only-common-columns -#%% Combine WF -#----------------- -# Combine WF -#----------------- -dfs_combine_wf = [baseline_BT, smnc_BT, ros_BT, rus_BT, rouC_BT, - baseline_CV, smnc_CV, ros_CV, rus_CV, rouC_CV] - -dfs_nrows_wf = [] -for df in dfs_combine_wf: - dfs_nrows_wf = dfs_nrows_wf + [len(df)] -dfs_nrows_wf = max(dfs_nrows_wf) +paramD = { + 'baseline_paramD': { 'input_df' : X + , 'target' : y + , 'var_type' : 'mixed' + , 'resampling_type': 'none'} + + , 'smnc_paramD': { 'input_df' : X_smnc + , 'target' : y_smnc + , 'var_type' : 'mixed' + , 'resampling_type' : 'smnc'} -dfs_ncols_wf = [] -for df in dfs_combine_wf: - dfs_ncols_wf = dfs_ncols_wf + [len(df.columns)] -dfs_ncols_wf = max(dfs_ncols_wf) + , 'ros_paramD': { 'input_df' : X_ros + , 'target' : y_ros + , 'var_type' : 'mixed' + , 'resampling_type' : 'ros'} -expected_nrows_wf = len(dfs_combine_wf) * dfs_nrows_wf -expected_ncols_wf = dfs_ncols_wf + , 'rus_paramD' : { 'input_df' : X_rus + , 'target' : y_rus + , 'var_type' : 'mixed' + , 'resampling_type' : 'rus'} -common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf))) + , 'rouC_paramD' : { 'input_df' : X_rouC + , 'target' : y_rouC + , 'var_type' : 'mixed' + , 'resampling_type' : 'rouC'} + } -if len(common_cols_wf) == dfs_ncols_wf : - combined_baseline_wf = pd.concat([df[common_cols_wf] for df in dfs_combine_wf], ignore_index=False) - resampling_methods_wf = combined_baseline_wf[['Resampling']] - resampling_methods_wf = resampling_methods_wf.drop_duplicates() - print('\nConcatenating dfs with different resampling methods [WF]:', tts_split - , '\nNo. of dfs combining:', len(dfs_combine_wf) - , '\nThe sampling methods are:' - , '\n', resampling_methods_wf) - if len(combined_baseline_wf) == expected_nrows_wf and len(combined_baseline_wf.columns) == expected_ncols_wf: - print('\nPASS:', len(dfs_combine_wf), 'dfs successfully combined' - , '\nnrows in combined_df_wf:', len(combined_baseline_wf) - , '\nncols in combined_df_wf:', len(combined_baseline_wf.columns)) - else: - print('\nFAIL: concatenating failed' - , '\nExpected nrows:', expected_nrows_wf - , '\nGot:', len(combined_baseline_wf) - , '\nExpected ncols:', expected_ncols_wf - , '\nGot:', len(combined_baseline_wf.columns)) - sys.exit() -else: - sys.exit('\nConcatenting dfs not possible [WF],check numbers ') +# Initial run to get the dict containing CV, BT and metadata DFs +mmD = {} +for k, v in paramD.items(): +# print(mmD[k]) + scores_7030D = MultModelsCl(**paramD[k] + , tts_split_type = tts_split_7030 + , skf_cv = skf_cv + , blind_test_df = X_bts + , blind_test_target = y_bts + , add_cm = True + , add_yn = True + , return_formatted_output = True) + mmD[k] = scores_7030D + +# Extracting the dfs from within the dict and concatenating to output as one df +for k, v in mmD.items(): + out_wf_7030 = pd.concat(mmD, ignore_index = True) -# Add index as a column -combined_baseline_wf.columns -combined_baseline_wf = combined_baseline_wf.reset_index() -combined_baseline_wf.rename(columns = {'index': 'Model_name'}, inplace = True) -combined_baseline_wf.head() - -# sort df: Resampling, Data_source, and MCC -combined_baseline_wf.sort_values(by = ['Resampling', 'Data_source', 'MCC'], ascending = [True, True, False], inplace = True) -############################################################################## -#%% Combine LF -#----------------- -# Combine LF* -#----------------- -dfs_combine = [baseline_all, smnc_all, ros_all, rus_all, rouC_all ] - -dfs_nrows = [] -for df in dfs_combine: - dfs_nrows = dfs_nrows + [len(df)] -dfs_nrows = max(dfs_nrows) - -dfs_ncols = [] -for df in dfs_combine: - dfs_ncols = dfs_ncols + [len(df.columns)] -dfs_ncols = max(dfs_ncols) - -# dfs_ncols = [] -# dfs_ncols2 = mode(dfs_ncols.append(len(df.columns) for df in dfs_combine) -# dfs_ncols2 - -expected_nrows = len(dfs_combine) * dfs_nrows -expected_ncols = dfs_ncols - -common_cols = list(set.intersection(*(set(df.columns) for df in dfs_combine))) - -if len(common_cols) == dfs_ncols : - combined_baseline = pd.concat([df[common_cols] for df in dfs_combine], ignore_index=True) - resampling_methods = combined_baseline[['Resampling', 'training_size']] - resampling_methods = resampling_methods.drop_duplicates() - print('\nConcatenating dfs with different resampling methods:', tts_split - , '\nNo. of dfs combining:', len(dfs_combine) - , '\nThe sampling methods are:' - , '\n', resampling_methods) - if len(combined_baseline) == expected_nrows and len(combined_baseline.columns) == expected_ncols: - print('\nPASS:', len(dfs_combine), 'dfs successfully combined' - , '\nnrows in combined_df:', len(combined_baseline) - , '\nncols in combined_df:', len(combined_baseline.columns)) - else: - print('\nFAIL: concatenating failed' - , '\nExpected nrows:', expected_nrows - , '\nGot:', len(combined_baseline) - , '\nExpected ncols:', expected_ncols - , '\nGot:', len(combined_baseline.columns)) - sys.exit() -else: - sys.exit('\nConcatenting dfs not possible,check numbers ') - -# Add further column indications -combined_baseline['test_size'] = bts_size -combined_baseline['tts_split'] = tts_split -combined_baseline['testY_ratio'] = round(yc2_ratio,2) -#combined_baseline.columns - -# change to column names to be lower case for consistency -combined_baseline.rename(columns = {'Resampling' : 'resampling' - , 'Data_source': 'data_source'}, inplace = True) -combined_baseline.columns - -# sort df: resampling, data_source, mcc -combined_baseline.sort_values(by = ['resampling', 'data_source', 'score_order'], ascending = [True, True, True], inplace = True) - -# # rpow bind -# if all(XXX): -# print('\nPASS:colnames match, proceeding to rowbind') -# comb_df = pd.concat()], axis = 0, ignore_index = True ) combined_baseline +print('\n######################################################################' + , '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)' + , '\nGene:', gene.lower() + , '\nDrug:', drug + , '\noutput file:', outFile_wf + , '\nDim of output:', out_wf_7030.shape + , '\n######################################################################') ############################################################################### #==================== # Write output file #==================== -combined_baseline_wf.to_csv(outFile_wf, index = False) +#out_wf_7030.to_csv(outFile_wf, index = False) print('\nFile successfully written:', outFile_wf) - -combined_baseline.to_csv(outFile_lf, index = False) -print('\nFile successfully written:', outFile_lf) ############################################################################### \ No newline at end of file diff --git a/scripts/ml/run_7030_LOOP.py b/scripts/ml/run_7030_LOOP.py index 1b12a38..e20adee 100644 --- a/scripts/ml/run_7030_LOOP.py +++ b/scripts/ml/run_7030_LOOP.py @@ -9,6 +9,8 @@ Created on Mon Jun 20 13:05:23 2022 import re import argparse import os, sys +import collections + # gene = 'pncA' # drug = 'pyrazinamide' #total_mtblineage_uc = 8 @@ -25,6 +27,7 @@ import os, sys ############################################################################### homedir = os.path.expanduser("~") sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml') + ############################################################################### #================== # Import data @@ -54,79 +57,70 @@ outdir_ml = outdir + 'ml/tts_7030/' print('\nOutput directory:', outdir_ml) outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv' -outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + OutFile_suffix + '.csv' +#outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + OutFile_suffix + '.csv' #%% Running models ############################################################ print('\n#####################################################################\n' - , '\nRunning ML analysis: feature groups ' + , '\nStarting--> Running ML analysis: Baseline modes (No FS)' , '\nGene name:', gene - , '\nDrug name:', drug) + , '\nDrug name:', drug + , '\n#####################################################################\n') -fooD = {'baseline_paramD': { - 'input_df': X - , 'target': y - , 'var_type': 'mixed' - , 'resampling_type': 'none'} - , - 'smnc_paramD': {'input_df': X_smnc - , 'target': y_smnc - , 'var_type': 'mixed' - , 'resampling_type': 'smnc'} -} +paramD = { + 'baseline_paramD': { 'input_df' : X + , 'target' : y + , 'var_type' : 'mixed' + , 'resampling_type': 'none'} + + , 'smnc_paramD': { 'input_df' : X_smnc + , 'target' : y_smnc + , 'var_type' : 'mixed' + , 'resampling_type' : 'smnc'} + + , 'ros_paramD': { 'input_df' : X_ros + , 'target' : y_ros + , 'var_type' : 'mixed' + , 'resampling_type' : 'ros'} -barD = {} -for k, v in fooD.items(): - #print(k) - print(fooD[k]) - scores_7030D = MultModelsCl(**fooD[k] + , 'rus_paramD' : { 'input_df' : X_rus + , 'target' : y_rus + , 'var_type' : 'mixed' + , 'resampling_type' : 'rus'} + + , 'rouC_paramD' : { 'input_df' : X_rouC + , 'target' : y_rouC + , 'var_type' : 'mixed' + , 'resampling_type' : 'rouC'} + } + +# Initial run to get the dict containing CV, BT and metadata DFs +mmD = {} +for k, v in paramD.items(): +# print(fooD[k]) + scores_7030D = MultModelsCl(**paramD[k] , tts_split_type = tts_split_7030 , skf_cv = skf_cv , blind_test_df = X_bts , blind_test_target = y_bts , add_cm = True - , add_yn = True) - barD[k] = scores_7030D + , add_yn = True + , return_formatted_output = True) + mmD[k] = scores_7030D - -ros_paramD = {input_df = X_ros - , target = y_ros - , var_type = 'mixed' - , resampling_type = 'smnc'} - - -rus_paramD = {input_df = X_rus - , target = y_rus - , var_type = 'mixed' - , resampling_type = 'rus'} - - -rouC_paramD = {input_df = X_rouC - , target = y_rouC - , var_type = 'mixed' - , resampling_type = 'rouC'} - - - - -#==== -scores_7030D = MultModelsCl(**rouC_paramD - , tts_split_type = tts_split_7030 - , skf_cv = skf_cv - , blind_test_df = X_bts - , blind_test_target = y_bts - , add_cm = True - , add_yn = True) - -############################################################################### -############################################################################### -#%% COMBINING all dfs: WF and LF -# https://stackoverflow.com/questions/39862654/pandas-concat-of-multiple-data-frames-using-only-common-columns - - +for k, v in mmD.items(): + out_wf_7030 = pd.concat(mmD, ignore_index = True) + +print('\n######################################################################' + , '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)' + , '\nGene:', gene.lower() + , '\nDrug:', drug + , '\noutput file:', outFile_wf + , '\nDim of output:', out_wf_7030.shape + , '\n######################################################################') ############################################################################### #==================== # Write output file #==================== -#combined_baseline_wf.to_csv(outFile_wf, index = False) -#print('\nFile successfully written:', outFile_wf) +out_wf_7030.to_csv(outFile_wf, index = False) +print('\nFile successfully written:', outFile_wf) ############################################################################### \ No newline at end of file diff --git a/scripts/ml/running_ml_scripts.txt b/scripts/ml/running_ml_scripts.txt index e391698..deb193e 100644 --- a/scripts/ml/running_ml_scripts.txt +++ b/scripts/ml/running_ml_scripts.txt @@ -11,6 +11,7 @@ time ./run_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_7030.txt time ./run_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_7030.txt time ./run_7030.py -g gid -d streptomycin 2>&1 | tee log_gid_7030.txt time ./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030.txt + # alr: # ERROR, as expected, too few values! # gid: problems ######################################################################## @@ -73,9 +74,11 @@ time ./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030.txt ######################################################################## ######################################################################## - # running feature selection # Split:70/30 time ./run_FS.py -g pncA -d pyrazinamide 2>&1 | tee log_FS_pnca_7030.txt +real 338m26.705s +user 1946m12.173s +sys 189m40.122s diff --git a/scripts/ml/test_MultClfs.py b/scripts/ml/test_MultClfs.py index 201233b..99d7798 100644 --- a/scripts/ml/test_MultClfs.py +++ b/scripts/ml/test_MultClfs.py @@ -7,21 +7,24 @@ Created on Fri Jun 24 11:07:05 2022 """ import re import argparse +import os, sys ############################################################################### # gene = 'pncA' # drug = 'pyrazinamide' #total_mtblineage_uc = 8 -#%% command line args: case sensitive -arg_parser = argparse.ArgumentParser() -arg_parser.add_argument('-d', '--drug', help = 'drug name', default = 'pncA') -arg_parser.add_argument('-g', '--gene', help = 'gene name', default = 'pyrazinamide') -args = arg_parser.parse_args() +# #%% command line args: case sensitive +# arg_parser = argparse.ArgumentParser() +# arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '') +# arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') +# args = arg_parser.parse_args() -drug = args.drug -gene = args.gene +# drug = args.drug +# gene = args.gene ############################################################################### +homedir = os.path.expanduser("~") +sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml') ############################################################################### #================== @@ -79,7 +82,7 @@ mmD = MultModelsCl(input_df = X_smnc #================ # MultModelsCl: WITH formatted output #================ -mmDF = MultModelsCl(input_df = X_smnc +mmDF3 = MultModelsCl(input_df = X_smnc , target = y_smnc , var_type = 'mixed' , tts_split_type = tts_split_7030 @@ -96,4 +99,4 @@ mmDF = MultModelsCl(input_df = X_smnc # test function #================= # output from function call -ProcessMultModelCl(smnc_scores_mmD) +ProcessMultModelsCl(mmD)