/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 key: train_mcc value: [0.98236994 0.99413485 0.99413485 1. 0.99413485 0.98236994 0.98250594 0.99413485 0.99415185 0.99415185] mean value: 0.9912088892612723 key: test_fscore value: [0.94736842 0.95 0.91891892 0.92307692 1. 0.94444444 0.97435897 0.97297297 0.97297297 0.91428571] mean value: 0.9518399342083553 key: train_fscore value: [0.99115044 0.99705015 0.99705015 1. 0.99705015 0.99120235 0.99109792 0.99705015 0.99708455 0.99705015] mean value: 0.995578599693568 key: test_precision value: [0.94736842 0.9047619 0.94444444 0.9 1. 1. 0.95 1. 0.94736842 1. ] mean value: 0.9593943191311614 key: train_precision value: [0.99408284 1. 1. 1. 1. 0.98830409 1. 1. 0.99418605 1. ] mean value: 0.9976572980315564 key: test_recall value: [0.94736842 1. 0.89473684 0.94736842 1. 0.89473684 1. 0.94736842 1. 0.84210526] mean value: 0.9473684210526315 key: train_recall value: [0.98823529 0.99411765 0.99411765 1. 0.99411765 0.99411765 0.98235294 0.99411765 1. 0.99411765] 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 key: TN value: 181 mean value: 181.0 key: FP value: 10 mean value: 10.0 key: FN value: 8 mean value: 8.0 key: TP value: 179 mean value: 179.0 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: 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: [0.09543562 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 0.68421053 0.71428571 0.66666667 0.71794872] mean value: 0.6611498316761475 key: train_fscore value: [0.96187683 0.96774194 0.96187683 0.97058824 0.97360704 0.95882353 0.95266272 0.96449704 0.96231884 0.95857988] mean value: 0.9632572889552196 key: test_precision value: [0.73684211 0.63636364 0.5 0.8125 0.78947368 0.71428571 0.68421053 0.65217391 0.61904762 0.7 ] mean value: 0.6844897198529922 key: train_precision value: [0.95906433 0.96491228 0.95906433 0.97058824 0.97076023 0.95882353 0.95833333 0.9702381 0.95402299 0.96428571] mean value: 0.9630093065659414 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") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /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] mean value: 0.6511695906432748 key: train_recall value: [0.96470588 0.97058824 0.96470588 0.97058824 0.97647059 0.95882353 0.94705882 0.95882353 0.97076023 0.95294118] mean value: 0.9635466116271069 key: test_accuracy value: [0.73684211 0.57894737 0.5 0.76315789 0.78947368 0.65789474 0.68421053 0.68421053 0.64864865 0.7027027 ] mean value: 0.6746088193456615 key: train_accuracy value: [0.96176471 0.96764706 0.96176471 0.97058824 0.97352941 0.95882353 0.95294118 0.96470588 0.96187683 0.95894428] mean value: 0.9632585820251853 key: test_roc_auc value: [0.73684211 0.57894737 0.5 0.76315789 0.78947368 0.65789474 0.68421053 0.68421053 0.6505848 0.70175439] mean value: 0.6747076023391813 key: train_roc_auc value: [0.96176471 0.96764706 0.96176471 0.97058824 0.97352941 0.95882353 0.95294118 0.96470588 0.96185071 0.95892673] mean value: 0.9632542139662883 key: test_jcc value: [0.58333333 0.30434783 0.32142857 0.59090909 0.65217391 0.43478261 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